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McGill University is located on land which has long served as a site of meeting and exchange amongst Indigenous peoples, including the Haudenosaunee and Anishinabeg nations. The PSI Journal would like to acknowledge these nations as the traditional stewards of the land on which we have the privilege of engaging in academic pursuits.


PSI Ψ

McGill Psychology Undergraduate Research Journal Issue IX April 2019


Contents

Does Cooperation Exist Amidst Adverse Conditions? Rebecca Wu 9

Does Parental Substance Abuse Make Children More Vulnerable to Depression and Anxiety? If So, What Mechanisms Play a Role in This Risk Transmission? JoĂŁo Vitor Paes de Mello de Camargo 14

Why Do You Treat Me This Way? Outgroup Hostility as a Function of Attribution Tendencies and Essentialist Beliefs Jeannine A. C. Bertin 23

Blockade of Endogenous Opioid Systems Post-stress in Mice: Restraint vs Social Defeat Karim Abou Nader 36

In Types of Deceptive Communication, Where Does the Role of Machiavellianism Lie? Vaishnavi Kapil 40

Loving Intelligently in an Age of Smartphones: The Association Between Social Media Use, Quality of Alternatives, and Relationship Closeness Sophie Y. Zhao 46

Weighing My Options: How Attentional Resources Affect Robust Averaging Nimra Adil 60

Attentional Bias to Threat in Offspring of Alcoholics: An Examination of a Shared Etiology in Alcohol Use Disorders and Anxiety Disorders Micaela Wiseman 70

Only The Young? Hanna Warith 81

Alicia Florrick: The Good Wife (Seasons 1-4) Bianca Mercadante 83


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Foreword Dear Reader, The PSI Journal was an initiative started by the McGill Psychology Students’ Association (MPSA) in 2011. The journal serves as a showcase of the impressive quality and variety of research and other academic work put forth by undergraduate students. This year, our team is pleased to present to you the ninth volume of the PSI journal. Within this volume, you shall read ten papers spanning a broad range of topics, from the role of attention in decision making, motives behind lie-telling in young adults, the effect of social media on relationship closeness, and how beliefs about people’s good or bad nature affect outgroup prejudice, just to name a few. Throughout the year, I worked alongside seven passionate members of the editorial and design committees for the PSI Journal, as well as the ten brilliant Psychology students whose submissions we are thankful for in making this publication possible. As well, I would like to extend my gratitude to the executive members of the MPSA for their encouragement and support, and Dr. David Vachon for serving as the Supervising Professor for this year. On behalf of everyone involved, I hope you enjoy the journal as much as we have enjoyed putting it together. Thank you!

Vaishnavi Kapil Journal Coordinator, Editor-in-Chief


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Editors Amanda is currently in U2, majoring in Psychology, minoring in Behavioral Science and Sociology. She is planning on pursuing graduate studies in Forensic or Clinical Psychology. As of now, her interest in the field is quite broad but it would ideally involve her working as both a researcher and clinician—developing effective rehabilitation programs for criminals in hopes of reducing re-offence rates, exploring the complex world of interviewing/eye-witness testimony, doing trial consulting and evaluations, as well as risk assessments. In her spare time, she enjoys reading and writing which is what motivated her to join this year’s team of editors. Amy is a student from Princeton, NJ majoring in Psychology with a minor in Communications. Dara is a third year Psychology major, minoring in Interdisciplinary Life Sciences from Halifax, NS. This is her second year on the editorial team of the PSI journal, and she has really enjoyed getting to see the incredible research coming from her peers in the department. Delali is a rare B.Sc. Psychology major in her 4th, but not final year and a potential GIS minor. She enjoys “wasting time” with friends, music that makes you feel some type of way, realizing things, taking photos of people and occasionally feeling artistic. One of her dreams is to meet all the other black people in psychology, but is too close to introversion on the introversion – extraversion scale. Delali’s favourite psychology course is Human Motivation and her favourite professor is Dr. Sadia Zafar. She hopes to one day figure out what comes next. Megan is from London, Ontario. She is a U1 student pursuing a degree in Psychology with a Minor in European Literature and Culture. She is interested in pursuing a career that involves conducting psychological research and is grateful for the opportunity to gain further insight into the research process by contributing to the production of this journal. Reading and writing have always been a big part of her life, both in her academic endeavors and as a pastime. She is very excited to have the opportunity to bring her personal passion for these pursuits to the role of Writing Editor at the PSI Journal. Michaela is currently a U3 Psychology student, with a minor in World Islamic and Middle Eastern Studies. At the moment, she works in the Culture and Mental Health Research Unit (CMHRU) at the Jewish General Hospital, as she has an interest in the intersection between culture and mental health. She has always loved writing and editing, and greatly appreciates this platform that allows us to publish and share our work with others. I truly enjoyed reading and learning about the work my fellow students accomplished this year, and hope that we were able to adequately showcase their immense talent and hard work. Hana is a U1 student in Anthropology and Computer Science (Clearly, the rationale for being an editor on a psychology journal is lacking - she’s not quite sure how she got here herself but she knows she doesn’t regret it!) Outside of editing, she loves going to concerts, testing out new recipes, and naturally, reading and writing! She’s originally from Toronto and loves exploring new places in this city as well. She hopes you enjoy reading this journal as much as our team loved piecing it together!


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Does Cooperation Exist Amidst Adverse Conditions?

REBECCA WU Abstract Cooperation allows for evolution to be constructive as it enables the evolution of new levels of organization (Nowak, 2006). Ethnocentrism occurs when one cooperates with their in-group members, but not with out-group members; it arises when there are discriminatory attitudes and behaviours (Hammond & Axelrod, 2006). Ethnocentrism creates in-group bias as shown through simulations by Hammond and Axelrod (2006). Throughout their simulations, cooperation was favored specifically the ethnocentric strategy. The simulation utilized a prisoner’s dilemma framework to enable individuals to cooperate and defect in order to see the evolution of ethnocentrism. Kropotkin’s Mutual Aid theory of evolution argues that under adverse conditions, species showcase more cooperation (Borrello, 2004). As such, this paper examines if this is also seen when applied to the simulations of Hammond and Axelrod (2006). Increased death rates will be used to simulate adverse conditions; higher death rates would mean more adverse environments. In this study, the results from different death rates will be compared to examine whether cooperation indeed occurs in adverse conditions. Keywords: Cooperation, Prisoner’s Dilemma, Ingroup Bias, Kropotkin Introduction There are two fundamental principles in evolution which are mutation and natural selection (Nowak, 2006). Evolution is constructive due to cooperation allowing for the evolution of new levels of organization as competing individuals on the lower level begin to cooperate (Nowak, 2006). Thus, cooperation allows for specialisation and thus promotes biological diversity (Nowak, 2006). When one classifies themselves as an individual of a specific group, ethnocentrism arises (Nowak, 2006). Ethnocentrism is a common syndrome of “discrim-

inatory attitudes and behaviours” (Hammond & Axelrod, 2006, p.926). The attitudes and behaviours include seeing one’s own group as superior and exclusively helping one’s own group (in-group), while viewing out-groups as inferior and therefore not cooperating with them (LeVine & Campbell, 1972). Ethnocentric behaviors arise from group boundaries, or observable characteristics which signify a common descent (Hammond & Axelrod, 2006, p.926). Ethnocentrism has implications in everyday life, such as in voting (Kinder, 1998) and ethnic conflicts (Chirot & Seligman, 2001). As ethnocentrism plays a role in one’s life, examining factors that affect it is important. In this paper, ethnocentrism will be referred to as in-group favoritism as in Hammond and Axelrod’s paper (2006). Hammond and Axelrod.(2006) found that ethnocentrism results even with minimal cognition and absence of higher-order mechanisms. Throughout the simulations, cooperation was favored; specifically, the ethnocentrism strategy (Hammond & Axelrod, 2006). The result was still seen even when key parameters such as lattice size, cost of helping, immigration rate, and mutation rate were doubled or halved (Hartshorn et al, 2013). In a later study, Shultz et al. (2008) showed the different stages of evolution of the different cooperation strategies. Initially humanitarian and ethnocentrism strategies competed, but overtime ethnocentrism evolved to be the dominant strategy (Shultz et al, 2008). According to the Mutual Aid theory put forth by Russian biologist, Petr Kropotkin, “in the course of the struggle against environment, species practiced mutual aid, and cooperative species would increase in numbers and outlast their individualistic rivals” (Borrello, 2004, p.19). Essentially, cooperation would be more common in the event of adverse conditions. Adverse environments in today’s society such as airborne pollutants (Curtis et al, 2006) childhood abuse, and household dysfunctions (Felitti et al, 1998) can lead to risk factors for


10 several of the leading causes of death in adults. Due to adverse environments, death rates may arise. In this study, we want to examine whether adverse environments encourage more cooperation as Kropotkin has theorized, with an increase of death rate to reflect adverse environments. This study will give individuals insight regarding how adverse environments can change individuals’ cooperative behaviours. Methods The study will follow the Hammond and Axelrod (2008) simulation closely, but different death rates will be tested. The prisoner’s dilemma (PD) framework is utilized in order to make cooperation individually costly. A one-move PD is used to avoid agents learning how the other individual agents play. Each agent will possess three heritable traits; the first is a tag that identifies its group membership in one of four groups, while the second and third traits indicates the agent’s strategy. The second trait is an in-group strategy (cooperate or defect), when an agent encounters an agent of its group membership. The third is out-group strategy (cooperate or defect), when an agent encounters an agent of a different group membership. The simulated world will consist of 50 by 50 sites and will be empty initially. Each site can potentially be occupied by one agent. An agent can interact with one of its four neighbours who are either to the North, West, South, or East of them. To ensure all agents have the potential for four neighbours at all times, the space is toroidal, where there are wraparound borders. Each period encompasses four different phases which are listed and described below. 1. Immigration Phase An agent with random traits (tag, in-group, and outgroup strategy) is placed randomly on an empty site. This is the phase where new agents are created at the rate of immigration. The default immigration rate is set to one immigrant per cycle. New agent(s) are placed on the site one at a time. At the beginning of evolution there are no agents. 2. Interaction Phase All agents have their potential to reproduce (PTR) set to 12%. Then each agent has the opportunity to interact with their neighbours (up to four) in a onemove PD, where each chooses whether to cooperate

R. Wu or defect independently. Choosing to help will decrease the PTR by 1% for an agent, which is the cost of helping, while receiving help will increase an agent’s PTR by 3%, which is the benefit of receiving help. For an example, if agent A donates to B, A’s PTR decreases by 1%, while B’s increases by 3%. 3. Reproduction Phase All existing agents are sorted into a random order (to avoid potential source of simulation artifacts) for the chance to reproduce offspring if there is an empty adjacent site. If there are no sites available, no offspring is produced. The chance to reproduce has the same probability as PTR. An offspring obtains the traits of its parents, where each trait can change with the probability equal to the mutation rate of 0.5%. 4. Death Phase Each agent has an equal chance of dying compared to the standard death rate. The death rates of 0.1, 0.11, 0.12, 0.13 0.14, and 0.15 will be tested separately in this study. When an agent dies, they are removed from their site to make room for future offspring. In the simulation, there are two binary strategy behaviours (cooperate or defect with in-group or outgroup). There are four genotypic strategies that can evolve: Selfish strategy always defects, traitor only cooperates with out-group members, ethnocentrism only cooperates with in-group members, and humanitarian always cooperates. The model will run 2000 cycles as a constant pattern arises prior to this point. The simulation will be run 50 times to increase the accuracy of the results. Results The results were assessed based on the last 100 cycles of the fifty 2000 cycles using genotypes and phenotypes. In figure 1, one can see the results of the mean behaviour proportions across the different death rates. The cooperation proportion is 0.745, 0.829, 0.869, 0.750, 0.578, 0.588 for death rates in the order of 0.1, 0.11, 0.12, 0.13, 0.14, and 0.15. A one-way analysis of variance (ANOVA) was calculated on the proportion of agents that cooperated. The results were found to be significant at p<0.05, with F(5, 245)=102.33, p<0.00001. For all death rates, the proportion of cooperation is greater than defect. For death rates of 0.1-0.13,


Does Cooperation Exist Admist Adverse Conditions? the cooperating behaviour is far greater than the rate of defect, while for death rates 0.14 and 0.15 the proportion of cooperate is slightly higher. One should also note that the proportion of cooperating increases from the death rates of 0.1 to 0.12, but the effect levels off at death rate 0.13 where it begins to decrease.

Figure 1. Mean behavior proportions (cooperate or defect) of different death rates.

Table 1 shows the proportions of each of the four genotypic strategies across the five different death rates. Notably, for death rates 0.1, 0.11, and 0.12, ethnocentrism is the dominant strategy. Its proportion is relatively greater than the rest of the strategies as well. For death rates 0.13-0.15, humanitarian is the dominant strategy. Although it is the dominant one, ethnocentrism is not far behind; the proportions of the two only differ by approximately 0.01 for death rates 0.13-0.15. One should also note that as death rates increases, the proportions of the four genotypic strategies appear to become more similar.

Table 1. Mean genotype strategy proportions across all six different death rates. The genotypic strategy with the highest proportion is bolded for each death rate. A oneway analysis of variance (ANOVA) was calculated on the proportion of agents of each strategy. The results were found to be significant at p<0.05, with F(5, 245)=188.74, p<0.00001.

Discussion From Table 1, one can see that cooperation is the dominant behavioural choice despite varying proportions. For death rates of 0.1 to 0.12, ethnocentrism is the common genotypic strategy, proving the results of Hammond and Axelrod

11 (2008). Simple tags and local interaction that cause in-group favouritism can prevail over egoism and dictate a population despite the absence of reciprocity and reputation. As mentioned, cooperation is the prominent behavioural choice in death rates of 0.1 to 0.12. This is due to the fact that when ethnocentric strategy is dominant, there is also a tendency for neighbours to be of the same tag. As the death rates increase to 0.13, the relative proportion of humanitarian and ethnocentrism is very similar, where humanitarian is 0.01 greater than ethnocentric proportion (this is seen in death rate 0.14 and 0.15 as well). From this, one may hypothesize that there may be a threshold at which ethnocentrism will stop dominating. Furthermore, there are less survivors during the last 100 cycles with higher death rates, as such the proportions obtained are from smaller population numbers which could explain why the proportions may be similar. For example, if the average of the behavioral proportions from the 50 runs in the last 100 cycles for a high death rate of 0.13 were 1.3 selfish, 1.3 traitor, 2 ethnocentric, and 2.3 humanitarian, it would yield proportions of 0.19, 0.19, 2.9, 0.33. The proportions are smaller as there are less agents present during the last 100 cycles. One explanation for why humanitarian may be slightly more dominant is that an individual may care more about the survival of the species and not just their in-group when conditions are extremely severe. Additionally, one should note that for death rates of 0.14 and 0.15, the proportions of ethnocentrism and humanitarian are not much greater than the selfish and traitor strategies. The results from table 1 complement the results from figure 1, as the dominant genotypic strategies both incorporate cooperation, and cooperation is seen to be the dominant behaviour across all six death rates. One should also note selfish and traitorous strategies may emerge due to mutation within ethnocentric and humanitarian groups which may also explain the increase in their proportions. From Figure 1, one can see that cooperation does increase under adverse conditions (higher death rates) like Kropotkinâ&#x20AC;&#x2122;s Mutual Aid theory suggests, but only to a certain degree, and then the amount of cooperation decreases. There may be a threshold for how extreme the adverse condition (how high the death rate) can be in order to elicit more cooperation. A similar argument to the one made for the strategy proportions can be made for why there may be a decrease in number of cooperators and defectors


12 for higher death rates. There would be less agents in the last 100 cycles with higher death rates. As such, the proportions obtained would be based on a smaller overall population. When the population is small enough, the proportions may be skewed even with one extra agent. For an example, there may be 30 agents left for the death rate of 0.14, where 19 would be cooperative and 11 non-cooperative; the proportions would be 0.63 for cooperative & 0.37 for non-coop. As the conditions become more severe, individuals may not have as big of an incentive to cooperate, as it may not benefit their livelihood, which may explain why the amount of cooperation decreases after the death rate of 0.12. Future work can do more than 2000 cycles to see what strategy would be prominent before extinction (where there are no more agents left) across different death rates. One advantage of the model is that it enables individuals to see that Kropotkinâ&#x20AC;&#x2122;s mutual aid theory is correct to a certain degree, as there is a point where increases in cooperation seizes with more adverse environments. Another advantage of this model is that it enables individuals to see that increasing death rates can switch the dominant strategy from ethnocentrism to humanitarian. This may give insight in to how individuals may react in times of adverse conditions. One limitation of this model is that the parameters are static throughout each run which may not be reflective of what happens in reality. As such, future work should vary key parameters in order to mimic conditions of real life. Additionally, social influences, and learning factors can be added as well. Future models can incorporate these factors in order to get a better understanding of cooperation and the evolution of ethnocentrism. One can use those simulations to compare with the present to study to see what the effects of each respective factor is on cooperation and ethnocentrism evolution. Additionally, one can do more work on finding the causal reason for cooperation and defective behaviours reaching a similar proportion when conditions are severely adverse (death rates are high). Moreover, one can try to simulate adverse conditions via decreases in birth rate as opposed to death rates, to see if similar results would be obtained. This study demonstrates the usefulness of agentbased simulations to test evolutionary theories which may not be possible in laboratory settings. Running these simulations gives one a complete evolutionary record, unlike studying evolution solely from fossil

R. Wu records. Using the results from the simulations, one can abstract numerous details for their phenomena of study. Therefore, agent-based simulations can be used to compliment theories of evolution and other phenomena in the world. Acknowledgement This paper was written under the supervision and guidance of Dr. Thomas Shultz from McGill University. References Borrello, M. E. (2004). Mutual Aid and Animal Dispersion: An Historical Analysis of Alternatives to Darwin. Perspectives in Biology and Medicine 47(1), 15-31. Johns Hopkins University Press. Retrieved November 20, 2018, from Project MUSE database. Chirot, D., & Seligman, M. E. P. (Eds.). (2001). Ethnopolitical warfare: Causes, consequences, and possible solutions. Washington, DC, US: American Psychological Association. Curtis, L., Rea, W., Smith-Willis, P., Fenyves, E., & Pan, Y. (2006). Adverse Health Effects of Outdoor Air Pollutants. Environment International, 32, 6, 815-830. Doi: https://doi.org/10.1016/j. envint.2006.03.012 Cutler, D.M., & McClellan, M.B. (2001). Is technological change in medicine worth it?Health affairs, 20 5, 11-29. Ettenson, K., Ettenson, J., & Ettenson, R. (1999). Consumer animosity and consumer ethnocentrism: An analysis of unique antecedents. Journal of International Consumer, Marketing 11:524 Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., . . . Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245-258. Griffiths, AJF, Gelbart, WM, & Miller, JH. (1999.) Modern Genetic Analysis. W.H. Freeman. Hammond, R. A., & Axelrod, R. (2006). The Evolution of ethnocentrism. Journal of Conflict Resolution, 50, 926-936. Hartshorn, M., Kaznatcheev, A., Shultz, T. (2013). The Evolutionary Dominance of Ethnocentric Cooperation. Journal of Artificial Societies and


Does Cooperation Exist Admist Adverse Conditions? Social Simulation, 16, 3:7 Kinder, D. R. (1998). Opinion and action in the realm of politics. Handbook of social psychology, Boston: McGraw-Hill. LeVine, R. A., & Campbell, D. T. (1972). Ethnocentrism. New York: John Wiley. Nowak, M. A. (2006). Five Rules for the Evolution of Cooperation. Science,314(5805), 1560-1563. doi:10.1126/science.1133755 Preston, S. H. (1975). The Changing Relation between Mortality and Level of Economic Development. Population Studies,29(2), 231. doi:10.2307/2173509 Shultz, T. R., Hartshorn, M., & Hammond, R. A. (2008). Stages in the evolution of ethnocentrism. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1244-1249). Austin, TX: Cognitive Science Society.

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Does Parental Substance Abuse Make Children More Vulnerable to Depression and Anxiety? If So, What Mechanisms Play a Role in This Risk Transmission? JOĂ&#x192;O VITOR PAES DE MELLO DE CAMARGO Substance use disorders are one of the most common mental illnesses, with a 12-month prevalence of around 9% in the U.S. population (Grant et al., 2014). Substance use disorders are characterized by the recurrent use of a substance, associated with impairing symptoms in physiological, cognitive, and behavioural domains (American Psychiatric Association [APA], 2013). Affected individuals typically display cravings, defined as an intense desire to use the substance typically occurring in settings where it is normally acquired or used, such as a liquor store for patients with alcohol use disorder. Affected individuals may also display impaired control, defined by consistently taking in a larger amount of the substance, and/or for a longer period of time than intended (APA, 2013). One of the criterion groupings for the diagnosis of substance use disorders relates to social impairment associated with substance use. The symptoms under this grouping include continued substance use despite social or interpersonal problems exacerbated by it, as well as failure to fulfill major role obligations due to substance use (American Psychiatric Association, 2013). One major role obligation that may be affected by substance use is parenting. It is estimated that 1 in 5 children grow up in a home with someone who abuses drugs (Kulig, 2005). Therefore, it is important to understand the impacts of parental substance abuse in their offspring, as to drive intervention programs and social policy. Parental substance use and offspring outcomes It is well known that parental psychopathology may influence the development of internalizing and externalizing behaviour problems in offspring. This risk transmission is most often investigated in disorder-specific terms. For example, parental depression is a known risk factor for offspring depression (Goodman, 2007), and parental anxiety is a known risk factor for offspring

anxiety (Merikangas et al., 1998). However, there is significant evidence that intergenerational transmission of internalizing psychopathology may be non-specific (Starr, Conway, Hammen & Brennan, 2014). As internalizing and externalizing psychopathology are at least moderately related (Krueger, 1999), the question arises as to whether parental substance use disorders may predispose offspring to internalizing disorders such as anxiety and depression. There is significant evidence in the literature that this is the case. One of the most used scales for assessing behaviour problems in children is the Child Behaviour Checklist (CBCL; Achenbach & Edelbrock, 1983), which includes subscales for internalizing and externalizing problems. Using this scale, several studies have found higher rates of internalizing problems in children of substanceabusing parents. Stanger et al. (1999) compared demographically matched children of cocaine or opiate abusing parents undergoing treatment, children referred for mental health services, and non-referred children. Children of drug-abusing parents scored significantly higher than non-referred children on the CBCL subscale of internalizing problems; nearly 20% of children of drug-abusing parents scored in the clinically significant range. Similarly, another group of investigators also found children of opiate abusing parents scored significantly higher than controls in the internalizing subscale of the CBCL (Wilens, Biederman, Kiely, Bredin & Spencer, 1995). Finally, yet another group found that around 50% of their sample of 78 unrelated children of opiate and cocaine abusing mothers had clinically significant scores on at least one CBCL subscale; 13% had clinically significant scores on the internalizing subscale (Luthar, Cushing, Merikangas, & Rounsaville, 1998). An overwhelming 46% of these children had at least one psychiatric diagnosis of an anxiety or mood disorder.


Parental Substance Abuse: Depression and Anxiety Risk in Children Evidence of elevated internalizing disorders in children of drug-abusing parents has also been found longitudinally, raising the possibility of a causal influence. Adolescent children of alcoholic parents, aged between 10.5 and 15.5 years old, were tracked through early adulthood. The authors found that maternal alcoholism at adolescence significantly predicted DSM-III diagnoses of depression and anxiety in their offspring in young adulthood, accounting for maternal comorbid depression or anxiety (Chassin, Pitts, DeLucia and Todd, 1998). Paternal alcoholism was uniquely associated with depression at young adulthood only in female offspring. While adolescent internalizing symptoms predicted both anxiety and depression disorders in young adulthood, these did not fully account for the effects of either maternal or paternal alcoholism, suggesting that parental alcoholism may pose unique risks to the future development of anxiety and depression on their offspring. Limitations of the existing literature While the link between parental substance abuse and offspring internalizing disorders is well established, several factors highlight the difficulties in investigating this risk transmission, resulting in the need of more research on the topic. Firstly, substance use disorders are highly comorbid with depression and anxiety. It is estimated that around 20% of those with a substance use disorder have a comorbid mood disorder, and 18% have a comorbid anxiety disorder (Grant et al., 2014). Furthermore, around 20% of those with any mood disorder and 15% of those with an anxiety disorder will also present with a concurrent substance use disorder in the same 12-month period (Grant et al., 2014). In the current literature, this is highlighted by the fact that in Luthar et al. (1998), 89.7% of substance-abusing mothers also met criteria for one mood or anxiety disorder. The high comorbidity of substance abuse with mood and anxiety disorders makes it difficult to disentangle the transmission of risk for depression and anxiety in the offspring of substance-abusing parents. Due to the high heritability of mood disorders and anxiety disorders, it is possible that children of substance-abusing parents have heightened risk for anxiety and depression merely because their parents also have comorbid anxiety or depression. Chassin et al. (1999) did account for comorbid parental depression and anxiety in their findings, and have

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highlighted this issue as a possible explanation for inconsistencies in the literature. Secondly, as children and biological parents share around 50% of their genes, many research designs used to investigate this possible risk transmission are not able to ascertain the specific route of risk transmission. Evidently, there are three possible ways offspring of substanceabusing parents may be at heightened risk for depression and anxiety. Firstly, substance-abusing parents may pass on a genetic vulnerability in their offspring that predisposes them to anxiety and depression. Secondly, parental substance abuse may expose offspring to an environment causing heightened risk for anxiety and depression. Finally, gene-environment interactions may play a role; for example, substance abusing parents could pass on a genetic vulnerability to stressful environments in general, and simultaneously create such an environment, predisposing their children to anxiety and depression. While it is possible that all of these three pathways play some role in the risk transmission, neither cross-sectional designs such as Stanger et al.â&#x20AC;&#x2122;s, nor passive longitudinal designs such as Chassin et al. (1999)â&#x20AC;&#x2122;s, can accurately determine which pathway is most responsible for this risk transmission. Thirdly, children of drug-abusing parents are more likely to abuse drugs themselves (Chassin et al., 1999). Nurco et al. (1999) found that in a sample of adolescent offspring of narcotic addicts, with a mean age of 14.4 years, 21% of the offspring had reported a lifetime use of illicit drugs, and 37% had reported lifetime use of alcohol. Children of drug-abusing parents also use substances earlier in life. For example, in the same study, the mean age of first alcohol or drug use in children of narcotic addicts was found to be two to four years earlier than peer and community controls (Nurco et al., 1999). Data also suggested children of substanceabusing parents use alcohol and drugs even earlier than addicts themselves (Nurco et al., 1999). In another study with offspring of substance-abusing parents with a mean age of only 12.5 years old, 20% already met criteria for drug dependence or abuse and around 25% smoked cigarettes regularly (Merikangas, Dierker, & Szatmari, 1998). Substance use in offspring of substance-abusing parents may be one of the factors behind an increased risk for depression and anxiety, and an especially early onset of substance use could also play a role. Indeed, drug


16 abuse and dependence at ages 17 to 20 have been found to significantly predict diagnoses of major depressive disorder at ages 20 to 24, even after adjusting for continuity effects (Marmorstein, Iacono & Malone, 2009). While Chassin et al. (1999) did not find any unique effects of adolescent substance use on young adult depression and anxiety, the authors established that the data collected was insufficient to determine whether substance use was either a cause or effect of adolescent symptomatology. Furthermore, the authors also raised doubts as to the effectiveness of measures used, as substance use during adolescence was only collected at one time point. A more frequent measure of substance use through adolescence would have been more efficient in investigating the impacts of ongoing drug use. Fourth, the existing literature has not thoroughly investigated the impact of different substances on offspring outcomes. For example, two previously discussed studies (Luthar et al., 1998; Stanger et al., 1999) had opiate and cocaine abusing parents analysed together as one group, and no efforts were made to determine any possible substance effects on the offspring. The different physiological and subjective effects of opiates and cocaine may result in different stressors for the children. Additionally, literature has not addressed whether multiple substance abuse is associated with worse outcomes for children. For example, in Luthar et al. (1998)â&#x20AC;&#x2122;s sample of opiate or cocaine abusing mothers, 60% also met criteria for alcohol use disorder; however, no analyses were made as to whether or not concurrently abusing alcohol was associated with worse outcomes for the children. Finally, as many of the previous factors discussed highlight, there is still much work to be done to understand the mechanisms of the transmission of risk for depression and anxiety in children of substance-abusing parents. Previous literature has included few attempts to investigate such mechanisms, perhaps because this specific risk transmission has not been the key focus of much research. For example, Stanger et al. (1999) and Wilens et al. (1995) both investigated behavioural and emotional problems more broadly. This transmission of risk for internalizing problems in children of substance-abusing parents was a greater focus in Chassin et al. (1999). This study, however, only looked at adolescent internalizing or externalizing symptoms and substance use as possible mediators of relationships between offspring depression

J. V. Camargo and anxiety and parental substance use. This is as the investigation was focused on the specificity of parental alcoholism effects on adolescent depression, anxiety, and substance use, rather than on any explanations for such effects. No studies as of yet have closely investigated any possible mechanisms of transmission of risk for depression or anxiety in offspring of substance-abusing parents, which stresses the importance of further research on the topic. Possible mechanisms of risk transmission Goodman & Connell (2002) have arranged mechanisms for risk transmission of psychopathology from parents to children into four groups. Firstly, as children share about half of their parentsâ&#x20AC;&#x2122; genes, there is the possibility that children inherit genetic vulnerabilities to psychopathology from their parents. Secondly, parental psychopathology may expose children to perinatal stress, such as birth complications, and these stressors may make children vulnerable to psychopathology. Thirdly, it is possible that parental psychopathology exposes children to contextual stressors such as economic pressure. In this case, children and parents may also experience shared stressors that predispose them to psychopathology. Finally, parental psychopathology may expose children to a range of maladaptive cognitions, emotions and behaviour, predisposing them to psychopathology. A brief review of possible mechanisms for transmission of risk for depression and anxiety in children of drug-abusing parents, organized as per Goodman and Connell (2002)â&#x20AC;&#x2122;s groupings, follows. Genetics As previously discussed, one of the limitations seen in studies investigating the transmission of risk for anxiety and depression in children of substance-abusing parents is their inability to disentangle effects of genetics from effects of the environment. Twin and adoption studies are the most effective manner in which to investigate these differential impacts, as they isolate effects of genetics and the environment. Twin studies compare correlations between identical twins (who share a zygote, and hence, nearly 100% of their genes) with those between fraternal twins (who share around 50% of their genes). An example would be to investigate twin pairs of substance-abusing parents. If there were


Parental Substance Abuse: Depression and Anxiety Risk in Children higher correlations in levels of depression and anxiety in identical twins of substance-abusing parents compared to fraternal twins, this would suggest a genetic transmission of risk for depression and anxiety in offspring of substance-abusing parents. Adoption studies investigate the effects of genetics and environment by contrasting effects of traits in biological parents and in adoptive parents to childrenâ&#x20AC;&#x2122;s outcomes. A strong correlation between outcomes in biological parents and children reared apart from them would suggest a genetic influence in these outcomes. In turn, a strong correlation between outcomes in adoptive parents and in their adopted children would suggest high environmental effects. An example of such a study would be to investigate whether adopted children of biological substance-abusing parents develop depression and anxiety at higher rates than community controls. If higher rates of internalizing disorders were seen in adopted children independent of adoptive parentsâ&#x20AC;&#x2122; substance abuse, depression and/or anxiety, a particularly strong case would be made for a genetic transmission of risk for depression and anxiety in children of substance-abusing parents. No twin or adoption studies were found to investigate the rates of depression and anxiety in children of substance-abusing parents. Twin and adoption studies have mostly focused on untangling gene-environment effects in disorderspecific risk transmission. For example, a twin study has found that illicit substance use and abuse are around 25% heritable, and tobacco use and abuse are around 40 to 60% heritable, yet there were significant environmental influences in all substance use measures (McGue, Elkins, & Iacono, 2000). Additionally, another twin study has found depression to be moderately heritable (29% to 42%), more so in women than in men (Kendler et al, 2006). Until twin or adoption studies specifically investigate the impact of parental substance abuse on offspring depression and anxiety, the differential impacts of genetics and environment in this risk transmission will remain unclear. Perinatal stress Another group of possible mechanisms for transmission of risk for internalizing disorders in children of substance-abusing parents relates to perinatal stress. It may be that parental drug abuse during pregnancy exposes children to several

17

stressors that may increase risk for depression and anxiety later in their lifetime. Substance abuse during pregnancy is associated with a variety of birth complications (Kuczkowski, 2007), some of which may predispose children to depression or anxiety. For example, a study has found that almost half of a sample of adults with fetal alcohol syndrome, caused by maternal alcohol use during pregnancy, met diagnostic criteria for depression (Famy et al., 1998). Additionally, lower birth weight, which is associated with maternal cannabis abuse (Kuczkowski, 2007), may also predispose children to depression (Gale & Martyn, 2004). Perinatal stress, however, may go beyond obvious birth complications. Abuse of some substances, such as opiates, is associated with malnutrition (Kuczkowski, 2007). Studies linking famines such as the Dutch Famine of 1944-45 to higher indices of depression (Stein et al., 2009) raise the possibility of malnutrition as a risk factor for depression in children of substance-abusing parents. A review has found that gestational stress in general, including malnutrition as well as maternal depression or anxiety, is related to offspring emotional problems (Rice, Jones & Thapar, 2007). A longitudinal study found maternal smoking during pregnancy leads to higher rates of depression, but not anxiety, in their offspring (Fergusson, Woodward & Horwood, 1998). Doserespondent relationships were observed, where higher amounts of cigarettes smoked per day during pregnancy corresponded to higher indices of offspring depression. However, one caveat is of importance: gestational smoking was also associated with several social and contextual factors. Mothers who smoked during pregnancy were more likely to be less educated, come from a lower socioeconomic status, and have an unplanned pregnancy and higher rates of criminal offending. Mothers who smoked during pregnancy were less nurturing with their children, and their children were significantly more exposed to physical and sexual abuse compared to children of non-smoking mothers. The relationship between gestational smoking and offspring depression was fully accounted for by these social and contextual factors (Fergusson et al., 1998). It is therefore difficult to establish any causal effects of perinatal stress in this risk transmission. For example, reviews such as Rice et al. (2007)â&#x20AC;&#x2122;s, which includes maternal depression


18 or anxiety as a measure of gestational stress, do not rule out the possibility that offspring of depressed or anxious mothers have higher indices of emotional problems simply due to shared genes. This highlights how any studies investigating higher vulnerability for depression and anxiety in offspring of substance-abusing parents will also struggle to rule out the possibility that genetics may be driving both substance abuse in parents and internalizing disorders in their offspring. Furthermore, Fergusson et al. (1998) found no relationship between gestational smoking and offspring anxiety, and the association between gestational smoking and offspring depression was fully accounted for by social and contextual factors. The evidence presented so far puts in question whether mechanisms related to perinatal stress play a direct role in the transmission of risk for depression and anxiety in the offspring of substance-abusing parents. Contextual stressors Contextual stressors related to parental substance abuse may also explain some of the effects on increased rates of internalizing problems in their offspring. Fergusson et al. (1998)â&#x20AC;&#x2122;s findings are consistent with this, as lower socioeconomic statuses in mothers who smoked during pregnancy partially accounted for the higher rates of internalizing problems in their offspring. Other studies also suggest an interaction between contextual stressors and the other mechanism groups. For example, in addition to drug use exposure, children of lower socioeconomic statuses are also more likely to be exposed to malnutrition perinatally (Bradley & Corwyn, 2002). Additionally, families from lower socioeconomic statuses also experience more uncontrollable life events, and the stressors of lower socioeconomic statuses may lead to less warm and responsive parenting (Bradley & Corwyn, 2002). Overall, it seems contextual stressors, such as low socioeconomic statuses, may affect children of substance-abusing parents in two possible ways. Firstly, they may indirectly affect offspring outcomes, such as through predisposing children to stressors such as malnutrition and, therefore, depression and anxiety. Alternatively, contextual stressors such as poverty may serve as shared vulnerabilities to both parental substance abuse and offspring depression and anxiety; in this case, parental substance abuse would not necessarily cause offspring depression and anxiety, but contextual stressors would be

J. V. Camargo behind both parental and offspring psychopathology. Exposure to maladaptive behaviour, affect, and cognitions Finally, it is possible that substanceabusing parents expose their children to maladaptive behaviour, affect and cognitions, which may play a role in the development of depression and anxiety. Connell & Goodman (2002) posited that one of the ways this may happen is through modelling. This may be behind the observed findings that children of substance-abusing parents are more likely to abuse drugs themselves and use drugs at an earlier age than controls (Nurco et al., 1999; Merikangas et al., 1998; Chassin et al., 1999). As drug abuse has been found to predict diagnoses of major depression (Marmorstein, Iacono & Malone, 2009), children of substance-abusing parents may model their parentsâ&#x20AC;&#x2122; behaviours through using substances themselves, and therefore becoming predisposed to developing depression and/or anxiety. Another potential way in which substanceabusing parents may expose their children to maladaptive behaviour, affect and cognitions is through parenting. Substance abuse has been related to a myriad of maladaptive parenting behaviours. Ongoing maternal drug use has been associated with a lack of warmth and emotional responsivity towards their children (Schuler et al., 2000; Gottwald & Thurman, 1994). Increasing drug involvement has been associated with a parenting style involving less child supervision, less discussion and positive involvement, less closeness, and more punitive forms of discipline (Kandel, 1990). Parental substance abuse is also associated with insecure and disorganized attachment styles (Goodman et al., 1999). Parental substance abuse is also associated with a more than twofold increase in the risk of childhood physical and sexual abuse (Walsh, MacMillan, & Jamieson, 2003), which are in turn associated with symptoms of depression and anxiety (Gibb, Butler, & Beck, 2003). Maladaptive and abusive parenting practices may therefore be at least partially responsible for the increased rates of internalizing disorders seen in children of substanceabusing parents. Methods The proposed research design is a highrisk longitudinal study investigating the prevalence of depression and anxiety in children of substance-


Parental Substance Abuse: Depression and Anxiety Risk in Children abusing parents, as well as possible mechanisms for this risk transmission. Both parents will be assessed for current substance use disorders as per the DSM5 criteria at some point between the third and eighth months of the motherâ&#x20AC;&#x2122;s pregnancy. Children will be divided in four different groups according to parental diagnoses at the initial measure; each group will have approximately 100 children, for a total of 400 children and 800 parents. The maternal substance use disorder group (MSUD) will consist of children whose mothers, but not fathers, met criteria for any substance use disorder. The paternal substance use disorder group (PSUD) will consist of children whose fathers, but not mothers, met criteria for any substance use disorder. The substance use disorders in both parents group (BSUD) will consist of children for whom both parents met criteria for substance use disorders. Finally, the control group (CONT) will consist of children for whom neither parent met criteria for substance use disorders. Groups will be matched for socioeconomic status, and any complications in the childâ&#x20AC;&#x2122;s birth, after the first visit, will also be recorded. After the initial assessment, parents will return to the lab with their child in four additional occasions: when the child is 3 years old, 6 years old, 9 years old and 12 years old. In each of these visits, internalizing symptoms in children will be assessed using the Child Behaviour Checklist (CBCL; Achenbach & Edelbrock, 1983). In each of the these visits, parents will also be assessed for substance use disorders again, as to determine any diagnostic status changes over time and the possible impacts of such changes. In all five visits, the severity of the substance use disorder in parents will be measured and analyzed, as to assess whether more severe addictions result in greater risk transmission of depression and anxiety to children. The type of substance use disorder, as well as whether or not parents are addicted to more than one substance, will also be analysed. This is to investigate the effects of different types of drugs on childrenâ&#x20AC;&#x2122;s internalizing symptoms, as well as whether parental addiction to multiple substances poses an even greater risk to their children. As substance use disorders are highly comorbid with both depression and anxiety, parents will also be screened for these at every time point, as to investigate whether transmission of risk for depression and anxiety in their children is independent of any parental depression or anxiety

19

diagnoses Possible mechanisms of risk transmission to be investigated are related to parenting. Therefore, at each of the assessments following the birth of the child, measures of parenting style, attachment style, and indices of physical, sexual and psychological abuse will be included. Hypotheses Firstly, in concordance with the existing literature, it is hypothesized that children of one or two substance-abusing parents (MSUD, PSUD, BSUD) will show greater levels of internalizing behaviours as measured by the CBCL when compared to children of parents without any substance use disorder diagnoses (CONT), controlling for parental depression and anxiety diagnoses. Statistical analyses controlling for socioeconomic status and birth complications will provide information concerning the explanatory effects of either of these factors; it is hypothesized that after controlling for both of these factors, the relationship will remain significant, due to environmental effects such as parenting. Additionally, analyses will be made to investigate whether different substance use disorders are associated with different offspring outcomes. While hypotheses are made concerning the effect of different substances use disorders, it is hypothesized that more severe and multiple substance use disorders in parents will be associated with higher indices of internalizing problems in their offspring. Finally, it is hypothesized that parental substance abuse poses risk for offspring internalizing problems regardless of parental comorbid depression or anxiety diagnoses; this will be confirmed if associations between parental substance abuse and offspring internalizing problems remain significant, controlling for parental comorbid depression or anxiety diagnoses. Secondly, it is hypothesized that children of substance-abusing parents will be exposed to higher levels of maladaptive parenting styles, characterized by greater control and less responsiveness and warmth, more insecure attachment styles, as well as greater levels of physical, sexual and psychological abuse. This effect will be even stronger in children of parents who still meet criteria for substance use disorders at further time points in the study. Furthermore, it is hypothesized that these associations will at least partially explain levels of internalizing problems in these children, such that


20 parenting style, attachment style, and abuse will moderate relationships between parental substance abuse and offspring internalizing problems. It is hypothesized that children in the group with both substance-abusing parents (BSUD) will be exposed to more parenting stressors such as abuse, and therefore will show greater rates of internalizing problems compared to children in either group with one substance-abusing parent (MSUD or PSUD). As Connell & Goodman (2002) have reported, during early and middle childhood, maternal psychopathology has greater links to offspring internalizing problems than paternal psychopathology; it is also hypothesized that the MSUD group will show greater rates of internalizing problems compared to the PSUD group. No hypotheses are made between moderating effects of offspring gender on these relationships, due to the lack of relevant literature. However, if girls in the MSUD group show more internalizing problems than boys, and the opposite is true in the PSUD group, this will suggest offspring modelling explaining some of the risk transmission, as parents have shown to impose greater modelling effects on their same-sex children (Connell & Goodman, 2002). Finally, it is also hypothesized that recurrent substance abuse in parents over each of the studyâ&#x20AC;&#x2122;s time periods will be associated with greater levels of internalizing symptoms in their children. If this is confirmed, support will be given for a view of environmental effects playing a role in this risk transmission. However, if parental substance abuse at time 1 still predicts offspring internalizing problems even if parents do not meet criteria for SUDs at further time points, this will suggest some genetic effects. Discussion Implications Firstly, this study will add to the literature on whether substance abuse in parents makes children more prone to internalizing problems. In particular, one strength of this study that is not often seen in the existing literature is that it will account for the effects of comorbid depression or anxiety diagnoses in the parents. If parental substance abuse is still associated with a greater level of offspring internalizing symptoms after accounting for such comorbid disorders, support will be given

J. V. Camargo to a unique transmission of risk for internalizing disorders in children of substance-abusing parents. Additionally, if recurring parental substance abuse is associated with a greater risk of internalizing problems, as well as more abusive and maladaptive parenting styles, this will add support to the view that substance abuse in parents creates an adverse environment for children, in which they are more prone to developing depression and anxiety. Hence, this can inform future interventions directly targeting parenting and rearing practices in parents with treatment resistance substance use disorders, as to reduce the adverse impacts on their children. The study will also provide information as to further understand the impact of different types of substance use disorders on child-rearing practices. If any particular substance is associated with greater abuse or maladaptive parenting practices compared to other substances, this will inform interventions in parents who abuse these substances to directly target parenting practices, as to reduce adverse outcomes in their offspring. Similarly, if abuse of multiple substances is associated with greater risk for offspring, this will inform interventions to prioritize children of these parents in preventing and targeting internalizing problems. Finally, the study will also investigate differences between risks associated with paternal and/or maternal substance abuse. This will firstly be informative in investigating whether modelling effects play a role in this risk transmission. Furthermore, if maternal substance abuse is more strongly associated with offspring internalizing problems than paternal substance abuse, this will suggest an environmental effect, as mothers spend more time in child-rearing activities (Connell & Goodman, 2002). Furthermore, this study will also investigate whether substance abuse in both parents is linked to worse outcomes for their children. If this is confirmed, while it will be difficult to ascertain the mechanisms of these effects, more support will be given to findings of higher rates of internalizing problems in children of substance-abusing parents. Limitations While very informative in some areas, this study will unfortunately fail to address certain gaps in the literature, for which more empirical evidence will still be needed. Firstly, this study does not directly address differential impacts of genetics and the environment. If associations between parental


Parental Substance Abuse: Depression and Anxiety Risk in Children substance abuse at time 1 and offspring internalizing problems remain significant after controlling for recurring substance abuse at later time points, this will suggest a genetic effect. However, it is impossible to be certain of this without completely separating genetic and environment effects. If such an association is found, it will suggest twin and adoption research designs to investigate this risk transmission, in order to fully disentangle effects of genetics and the environment. Secondly, the study only investigates internalizing problems in children up to age 12. Therefore, the effects of parental substance abuse in their offspring at later time points will remain unclear. The study presupposes that internalizing symptoms in early to middle childhood will be related to greater levels of depression and anxiety over the lifespan. However, it is possible that after offspring get older and move out of their family household, the removal of an adverse environment will reduce levels of internalizing symptoms. Furthermore, even though Connell & Goodman (2002) have suggested that paternal psychopathology poses a greater risk to children at later ages, the study’s age range will not be sufficient in investigating whether this is true in the transmission of risk for internalizing problems in children of fathers with substance abuse. Finally, the age range of children in the study will not be sufficient in investigating whether increased levels of offspring substance abuse will play a role in higher levels of internalizing problems in these children. The literature suggests this is possible, as children of drug-abusing parents are more likely to abuse drugs themselves, and drug abuse has been found to predict internalizing disorders. However, as the last time point of the study is at age 12, children will likely be too young in order to investigate these effects. While the literature points to children of substance-abusing parents abusing substances at a very early age, suggesting that some of these children may already use or abuse drugs at age 12, the onset of drug use will likely be too early in order for it to have any meaningful impact on children’s internalizing disorders. Therefore, more empirical evidence will be needed in order to investigate this risk transmission in offspring of substance-abusing parents at a later age. References Achenbach, T. M., & Edelbrock, C. S. (1983). Manual for the child behavior checklist: and

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revised child behavior profile. University of Vermont, Department of Psychiatry. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub. Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual review of psychology, 53(1), 371-399. Connell, A. M., & Goodman, S. H. (2002). The association between psychopathology in fathers versus mothers and children’s internalizing and externalizing behavior problems: a meta analysis. Psychological bulletin, 128(5), 746. Chassin, L., Pitts, S. C., DeLucia, C., & Todd, M. (1999). A longitudinal study of children of alcoholics: predicting young adult substance use disorders, anxiety, and depression. Journal of abnormal psychology, 108(1), 106. Famy, C., Streissguth, A. P., & Unis, A. S. (1998). Mental illness in adults with fetal alcohol syndrome or fetal alcohol effects. American Journal of Psychiatry, 155(4), 552-554. Gale, C. R., & Martyn, C. N. (2004). Birth weight and later risk of depression in a national birth cohort. The British Journal of Psychiatry, 184(1), 28-33. Gibb, B. E., Butler, A. C., & Beck, J. S. (2003). Childhood abuse, depression, and anxiety in adult psychiatric outpatients. Depression and anxiety, 17(4), 226-228. Goodman, G., Hans, S. L., & Cox, S. M. (1999). Attachment behavior and its antecedents in offspring born to methadone-maintained women. Journal of Clinical Child Psychology, 28(1), 58-69. Goodman, S. H. (2007). Depression in mothers. Annu. Rev. Clin. Psychol., 3, 107-135. Gottwald, S. R., & Thurman, S. K. (1994). The effects of prenatal cocaine exposure on motherinfant interaction and infant arousal in the newborn period. Topics in Early Childhood Special Education, 14(2), 217-231. Luthar, S. S., Cushing, G., Merikangas, K. R., & Rounsaville, B. J. (1998). Multiple jeopardy: Risk and protective factors among addicted mothers’ offspring. Development and psychopathology, 10(1), 117-136. Kandel, D. B. (1990). Parenting styles, drug use, and children’s adjustment in families of young adults. Journal of Marriage and the Family, 183-196.


22 Kendler, K. S., Gatz, M., Gardner, C. O., & Pedersen, N. L. (2006). A Swedish national twin study of lifetime major depression. American Journal of Psychiatry, 163(1), 109-114. Krueger, R. F. (1999). The structure of common mental disorders. Archives of general psychiatry, 56(10), 921-926. Kulig, J. W. (2005). Tobacco, alcohol, and other drugs: the role of the pediatrician in prevention, identification, and management of substance abuse. Pediatrics, 115(3), 816-821. Kuczkowski, K. M. (2007). The effects of drug abuse on pregnancy. Current Opinion in Obstetrics and Gynecology, 19(6), 578585. Marmorstein, N. R., Iacono, W. G., & Malone, S. M. (2010). Longitudinal associations between depression and substance dependence from adolescence through early adulthood. Drug and Alcohol Dependence, 107(2-3), 154160. McGue, M., Elkins, I., & Iacono, W. G. (2000). Genetic and environmental influences on adolescent substance use and abuse. American journal of medical genetics, 96(5), 671 677. Merikangas, K. R., Dierker, L. C., & Szatmari, P. (1998). Psychopathology among offspring of parents with substance abuse and/or anxiety disorders: a high-risk study. The Journal of Child Psychology and Psychiatry and Allied Disciplines, 39(5), 711-720. Nurco, D. N., Blatchley, R. J., Hanlon, T. E., & Oâ&#x20AC;&#x2122;Grady, K. E. (1999). Early deviance and related risk factors in the children of narcotic addicts. The American journal of drug and alcohol abuse, 25(1), 25-45. Rice, F., Jones, I., & Thapar, A. (2007). The impact of gestational stress and prenatal growth on emotional problems in offspring: a review. Acta Psychiatrica Scandinavica, 115(3), 171 183. Schuler, M. E., Nair, P., Black, M. M., & Kettinger, L. (2000). Mother-infant interaction: effects of a home intervention and ongoing maternal drug use. Journal of clinical child psychology, 29(3), 424-431. Stanger, C., Higgins, S. T., Bickel, W. K., Elk, R., Grabowski, J., Schmitz, J., ... & Seracini, A. M. (1999). Behavioral and emotional problems among children of cocaineâ&#x20AC;?and opiate

J. V. Camargo dependent parents. Journal of the American Academy of Child & Adolescent Psychiatry, 38(4), 421-428. Walsh, C., MacMillan, H. L., & Jamieson, E. (2003). The relationship between parental substance abuse and child maltreatment: findings from the Ontario Health Supplement. Child abuse & neglect, 27(12), 14091425. Wilens, T. E., Biederman, J., Kiely, K., Bredin, E., & Spencer, T. J. (1995). Pilot study of behavioral and emotional disturbances in the highrisk children of parents with opioid dependence. Journal of the American Academy of Child & Adolescent Psychiatry, 34(6), 779-785.


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Why Do You Treat Me This Way? Outgroup Hostility as a Function of Attribution Tendencies and Essentialist Beliefs JEANNINE A. C. BERTIN Abstract To varying degrees, people hold essetialist beliefs that people are either fundamentally good or bad. Using a novel and interactive laboratory simulation of oppression (N = 357, 117 groups), we examined how such beliefs impact the emotions and attitudes that oppressed group members hold toward their oppressors, as well as the attribution they make for the outgroup’s oppressive behaviour. Oppressed group members who held essentialist beliefs were more likely to attribute their oppression to the oppressive outgroup’s core nature (an internal “outgroup blaming” attribution) rather than the societal context impacting the outgroup (an external “system blaming” attribution). Regardless of attribution style, oppressed group members always felt anger towards the oppressive outgroup. Importantly however, oppressed group members who made an internal “outgroup blaming” attribution were more prejudiced towards the oppressive outgroup than those who did not. Ultimately, having essentialist beliefs about people’s good or bad nature led to greater outgroup prejudice with “outgroup blaming” attributions having a mediating effect. Keywords: outgroup blaming attribution, system blaming attribution, essentialist beliefs, hostility “I feel that “man-hating” is an honorable and viable political act, that the oppressed have a right to class-hatred against the class that is oppressing them.” – Robin Morgan, 1977, p.171. “Visionary feminism…is rooted in the love of male and female being, refusing to privilege one over the other. The soul of feminist politics is the commitment to ending patriarchal domination of women and men, girls and boys.” – Bell Hooks, 2000, p.110. Many societies are characterized by social hierarchies in which there are great discrepancies in power between the groups at the top and

bottom of the social hierarchy (Sidanius & Pratto, 1999). Within hierarchical systems, it is possible that members of the lower power group may be mistreated, discriminated against, and oppressed by the high power group (Ho et al., 2015). For example, in the United States of America, White Americans are a high power group relative to Black Americans and have historically used this power to oppress Black Americans through enslavement, Jim Crow laws, and police brutality (Gates, 2011). As well, in many patriarchal societies, men are a relatively high power group in relation to women and have historically oppressed women through actions such as denying women’s suffrage, domestic violence and sexual assault, and disadvantageous hiring and pay practices (Lerner, 1986; MacLean, 2009). There is substantial evidence that when low power group members feel that other groups have either threatened the welfare of their group (i.e., realistic threats) or the cultural values, beliefs, and worldviews of their group (i.e., symbolic threats), they may have hostile emotional and attitudinal responses such as feeling fear, anger, resentment (Stephan & Renfro, 2002), rage, hatred, and righteous indignation toward the outgroup (Stephan, Ybarra, & Morrison, 2009). However, while general feelings of hostile emotions, such as anger and disgust, tend to be the response to threats such as oppression, individual group members also vary in terms of where they direct these hostile emotions. While some oppressed low power group members may direct their hostile emotions and attitudes toward the members of the oppressive high power outgroup directly, others may direct their hostile hostility at the broader hierarchical social structure that gives the high-power group their advantage. For example, this variation can be seen in the different responses by Feminist women to the oppression of women by men. Radical Feminist, Robin Morgan, declared: “I feel that “man-hating” is an honorable and viable political act, that the oppressed have


24 a right to class-hatred against the class that is oppressing them” (Morgan, 1977, p.171). Robin Morgan provides a prime example of when the hostile emotions felt by a member of the oppressed low power group (women) are directed at the members of the oppressive group (men). On the other hand, Intersectional Feminist, Bell Hooks, argues that, “Visionary feminism… is rooted in the love of male and female being, refusing to privilege one over the other. The soul of feminist politics is the commitment to ending patriarchal domination of women and men, girls and boys” (Hooks, 2000, p.110). In direct contrast to Morgan, Bell Hooks is an example of a low-power group member wanting to end the present hierarchy, but not directing her negative feelings toward the high power group (men) directly. Our focus in the present research is the question of where low power group members direct the hostile emotions and attitudes they experience in response to oppressive treatment from a highpower outgroup. Do low-power groups direct their hostility to the high-power group who oppresses them, as was the case for feminist Robin Morgan? Or rather, do disadvantaged group members direct their anger at the broader social hierarchy and social structures that allows the high power group to enjoy an advantaged position, as was the case for feminist Bell Hooks? We argue that whether the low-power group directs their hostile emotions and attitudes directly at the high power group who oppresses them, versus at their social system more broadly, has a profound impact on whether the future relations between both groups will be characterized by cycles of conflict versus reconciliation and allyship. In the present research we examine what factors predict the extent to which members of oppressed low power groups will react to their oppression with hostility directed toward the members of the oppressive high power group (rather than to the system). Outgroup hostility and attribution This research proposes that the extent to which oppressed low power group members direct their hostile emotions toward members of the oppressive high power outgroup is a function of the type of attribution that the members of the low power group make for the high power outgroup’s oppressive behaviour. Attribution theory describes two types of attributions that individuals might make in their attempt to find causes for behaviour: internal

J. Bertin attributions and external attributions (Kelley, 1973). An external attribution involves explaining behavior as being caused by the situation that the individual is in, while an internal attribution involves perceiving behavior as being caused by some internal characteristic of the person rather than to outside forces (Kelley, 1973). We hypothesize that low-power group members may make two different types of attributions for why the highpower outgroup chooses to oppress their group. On one hand, oppressed low power group members may make an internal attribution by attributing their oppression to the inherent traits and nature of the outgroup (who they are as a people), an action we call an outgroup blaming attribution. Alternatively, oppressed low power group members may make an external attribution by attributing the oppressive actions of the outgroup to the inegalitarian system and societal structures (ideologies, institutions, laws, education systems, etc.) that provide the foundation for the hierarchical system that allows for oppressive behaviour between the groups. We call this second type of attribution a system blaming attribution. One example of this distinction between outgroup blaming and system blaming attributions can be found in the words of one Civil Rights Activist, Malcolm X, who had a drastic shift in his response to the oppression of Black Americans by White Americans during his time as an activist in the American Civil Rights Movement. At one point, he claimed that “…the collective white man had acted like a devil in virtually every contact he had with the world’s collective non-white man” (X & Haley, 1965, p.204), demonstrating an outgroup blaming attribution. Later on, he stated that “…it isn’t the American white man who is a racist, but it’s the American political, economic and social atmosphere that automatically nourishes a racist psychology in the white man” (X & Haley, 1965, p.371), demonstrating the adoption of a system blaming attribution. We argue that whether oppressed low power groups make an outgroup blaming versus system blaming attribution for the oppression they experience will predict the extent of hostility the oppressed low power group members feel toward the oppressive high power group. An outgroup blaming attribution should result in members of oppressed low power groups being more likely to harbour hostile emotions and attitudes directed toward the members of the


Why Do You Treat Me This Way? oppressive high power group. A system blaming attribution should lead oppressed low power group members to direct their hostility toward dismantling the hierarchical system and to a lesser extent, toward the outgroup as a people. As such, in this research we will examine whether an (internal) outgroup blaming attribution by a member of an oppressed low power group will positively predict hostility toward members of the oppressive high power outgroup while an (external) system blaming attribution will negatively predict hostility toward members of the oppressive high power outgroup as a people. The effect of belief about good or evil on attribution If making an (internal) outgroup blaming attribution over an (external) system blaming attribution has important consequences for whether oppressed low power group members direct their hostility toward the high-power group, then it is also important to ask what leads oppressed low power group members to make an (internal) outgroup blaming attribution over an (external) system blaming attribution. In general, people tend to make attributions that underestimate environmental and situational factors and favor dispositional explanations - what is referred to as the fundamental attribution error (Ross, 1997). Within intergroup contexts, people can also show a bias to make an internal attribution for negative behaviour of the outgroup, but an external attribution for their ingroup’s negative behavior – what is described as the “ultimate attribution error” (Pettigrew, 1979). For example, in one study, both Muslim and Christian Indonesians used stronger dispositional (internal) attributions for violent outgroup acts than for violent in-group acts (Ariyanto, Hornsey, & Gallois, 2009). However, while group members may on the whole tend to be more biased to make internal attributions for negative behaviours of the outgroup versus their ingroup, it remains that group members still vary individually in whether they make an internal versus external attribution for the outgroup’s behavior. Prejudice toward the outgroup, for example, predicts the likelihood of ingroup members to make an internal versus external attribution for negative behaviour by the outgroup (Pettigrew, 1979). In the present research, we propose that the world views and lay theories (Dweck, Chiu, & Hong, 1995) that people have about human nature influence how they make attributions for an outgroup’s oppressive treatment of their ingroup (Hong, Chiu, Dweck, Lin, & Wan, 1999).

25 Specifically, we focus on the lay-theory beliefs that people have about good or evil human natures, which we define as the belief that each individual either has an inherently good disposition or an inherently evil disposition. We call these beliefs essentialist beliefs about good or evil. The presence of these beliefs is attested by the everyday use of terms such as good apple and rotten apple or bad apple to describe individuals. For example, one quote that demonstrates this essentialist belief that people are either good or bad comes from American Christian anarchist, pacifist, and social activist Ammon Hennacy, who declared: “Oh judge! Your damn laws! The good people don’t need them, and the bad people don’t obey them” (Troester, 1993, p.114). However, not all individuals carry this essentialist belief that people are either good or bad. Author Suzy Kassem demonstrates a rejection of this essentialist belief by writing that: “…there is good and bad in everybody, in every nation, in every race, and in every religion. To hear someone say that all the people that belong to a certain country, race, or religion are bad — is extremely untruthful….” (Kassem, 2011). We propose that people’s essentialist beliefs about good or evil natures will influence the type of attribution that individual members of a low power group make when they face oppression. We assert that individuals carrying this essential belief about good or evil (who see human nature in black and white) will perceive people as having fixed and fundamental evil essences or good essences that define who they are as people. These individuals will therefore be more likely to interpret the outgroup’s oppressive behaviour as evidence of who they are as a person while ignoring situational explanations for their behaviour. An indication of how one’s belief about individual’s capacity to be good or evil can influence the attribution one makes for others’ behaviour is given by this second quote by author Suzy Kassem who wrote: “None of us are just black or white…. Everybody has good and bad forces working with them, against them, and within them” (Kassem, 2011, Part Sun and Moon, para. 1). Here, this author refers to good and bad forces that can work with or against individuals, demonstrating a willingness to look at forces outside of individuals’ control when seeking explanations for their behaviour. As such, members of oppressed low power groups who see people as either good or evil would be more likely to make an (internal) outgroup


26 blaming attribution for the oppressive behaviour of the high power outgroup, while those who do not think that people are generally either good or bad would be less likely to make this internal attribution and thus more likely to make the (external) system blaming attribution. It should be noted that similar concepts have been previously been proposed (Webster & Saucier, 2013). Webster and Saucier demonstrated in their research that their proposed concepts of belief in pure good and belief in pure evil are valid psychological constructs (Webster & Saucier, 2013). They define belief in pure good as the belief that there a few people in the world with pure intentions to help others without using violence and without seeking reward (Webster & Saucier, 2013), while belief in pure evil is defined as the belief that there are purely sadistic individuals in the world (Vasturia, Webster, & Saucier, 2018). Their research also proposed a link between the concepts and attribution, demonstrating a tendency to disregard external/non dispositional explanations for violent behaviour in those who were high in belief in pure evil (Vasturia et al., 2018). Critically however, the concepts of belief in pure good and belief in pure evil are distinct from our proposed concept of essentialist belief about good or evil. Our concept of an essentialist belief about good or evil measures the belief that each and every person is necessarily, primarily and dispositionally either good or evil (versus a mix of both), rather than the belief that a few purely good people or some purely evil people exist in the world. As such, we retain our concept of belief about good or evil as an individual difference factor that can predict whether individual members of oppressed low power groups make (internal) outgroup blaming attributions or (external) system blaming attribution for the oppressive behaviour of high power outgroups. Present research To explore these questions, we carried out an intensive simulation laboratory experiment during which participants actually experienced being oppressed as a low power group at the hands of a high power group in society. Specifically, during a 2-hour experiment three groups of participants imagined that they belonged to one of three distinct groups that lived on a fictional society called Grabodia. Each group engaged in the study in different experimental rooms that they imagined was their group’s territory

J. Bertin on Grabodia. Participants imagined that they belonged to a group of people called the Hoye and that the other two groups in the experiment belonged to two other distinct social groups: the Arado and the Suebla. Participants were told that while their ingroup (the Hoye) and the Suebla outgroup had relatively low power on Grabodia, the Arado group had relatively high power. During this experiment groups were able to form a meaningful culture and identity for their group using procedures validated by Kachanoff, Taylor, Caouette, Khullar, and Wohl (2018) in which groups for an identity by creating a coat of arms (a collection of symbols and colors that reflect the shared values and traits of a group). Groups were also able to create their own cultural food and behavioural customs. To varying degrees, we manipulated the extent to which the high power outgroup oppressed participants’ ingroup. Groups were potentially oppressed in terms of having their collective autonomy restricted – a form of symbolic threat that involves the denial of groups being able to freely practice their culture during the experiment (Kachanoff et al., 2018). Groups were also potentially threatened through use of realistic threats to resources by being forced to do unequal amounts of work on a boring task relative to the high power group. We then examined how participants’ essentialist beliefs about good or evil would influence the types of attributions they made for the high-power Arado group’s actions and how this impacted the extent to which participants’ directed their hostile emotions toward the Arado. Hypotheses We tested three distinct but related hypotheses. H1: Members of an oppressed low power group who make an outgroup blaming attribution for their oppression will harbor relatively more hostility (emotions and attitudes) toward the oppressive high power outgroup than those who do not, while those who make a system blaming attribution for their oppression will harbor relatively less hostility toward the outgroup than those who do not. H2: Group members who hold the essentialist belief that people are either good or evil (versus those who believe people cannot be dichotomized in this way) will be more likely to make an outgroup blaming attribution and less likely to make a system blaming attribution for how the high power outgroup treated their ingroup. H3: Group members’ essentialist beliefs about


Why Do You Treat Me This Way? good or evil will predict hostility toward members of oppressive high power outgroup, with attribution having a mediating effect. Specifically, essentialist beliefs about good or evil will increase group members’ tendency to make an outgroup blaming attribution which will in turn promote greater outgroup hostility. Conversely, essentialist beliefs about good or evil will reduce the likelihood to make a system blaming attribution, and in turn, predict lesser outgroup hostility (see Figure 1 for conceptual model).

Figure 1. Conceptual path model showing hypothesized mediated relationship between Essentialist Beliefs About Good or Evil and Outgroup Hostility.

Sample A total sample of 357 participants was recruited from McGill University undergraduate student population through Sona Systems and through a paid participant pool. The 357 participants were divided into a total of 117 groups (34 groups/94 people had their culture restricted and were treated unequally, 29 groups/100 people had their culture restricted but were treated equally, and 22 groups/ 69 people had their culture respected but were treated unequally). Additionally, 32 groups were assigned to a nooppression condition (n = 94 participants) and were both allowed to keep their culture and were treated equally on the boring work task. However, these participants were excluded from our analyses as our research focused on the reaction of low power group members who are actually oppressed as a result of their low power position. Procedure Group placement and experiment contextualization. A group of six to twelve persons participated in each experiment, directed by three different experimenters. The participants gathered together upon arrival, in the presence of the three experimenters. They were then informed that they would be participating in group activities and would be divided into groups of two to four people for

27 that purpose. The participants were then randomly divided into three groups of two to four people by drawing slips (labeled group A, group B, or group C) from a hat. Three different experimenters ran each experimental session. Each experimenter was responsible for one of the three groups. After the group division, each group was led to a separate lab room and did not come into physical contact with the other two groups again for the remainder of the experiment. In their separate rooms, each group was played an instructional video that explained the background of the experiment. The video prompted them to imagine that they were part of a fictional society called Grabodia and that each of the three separate groups were either the Hoye, the Suebla, or the Arado. Groups were then instructed to flip over a piece of paper left near the computer to find out what group they belonged to for the experiment. Regardless of whether groups were assigned to group A, B, or C at the beginning of the experiment, each group flipped over a paper telling them that they were the “Hoye” group. This group assignment was done in this manner to create the appearance that the group names were randomly assigned and thus create the impression that the other two groups in the other two rooms were therefore assigned to Suebla and to the Arado. In reality, all of the groups were assigned to the Hoye and the Suebla and the Arado were deception groups. The video then informed the Hoye groups that the Arado possessed the power to make decisions on Grabodia. Group identity creation. Each Hoye group then engaged in a group identity formation task during which they created a group culture (see Kachanoff et al. (2018) for details about this paradigm). The identity formation task involved having the groups use a program that we designed to create a coat of arms. The coat of arms program allowed members of the group to select their own group name and group motto. Groups also had to select a colour and animal from a range of options where each represented different values and characteristics. Groups were instructed by experimenters to choose the options that were meaningful to them as a group. Groups also chose a cultural food and group custom. This identity formation task had the purpose of fostering in the participants a feeling that their group was a real and unified entity (Callahan & Ledgerwood, 2016), which would promote group identification


28 (Castano, Yzerbyt, & Bourguignon, 2003). Work task. The Hoye groups all watched a video that informed them that there was an upcoming work task that required grey “mana” beads to be sorted from several other coloured beads as they were needed in order to power up Grabodia. The video informed groups that the Arado would decide how to distribute the work among the three groups. It also informed them that the Arado would decide whether the Hoye and the Suebla would be allowed to keep the culture that they created. Experimental simulation of oppression. We then administered the experimental manipulation, which varied depending on the condition that participants were in. There were three core experimental conditions of oppression in which group members were oppressed either in terms of symbolic collective autonomy threat, realistic threats to equality, or both types of threat. In each running of the experiment, all of the three Hoye groups partaking on a given day were in the same randomly assigned condition. Collective autonomy threat/work inequality. In this condition, the Hoye groups were informed by the experimenter that the Arado group had changed their culture as well as the Suebla’s culture. They were subsequently given a coat of arms, food and assigned custom that were different to what they originally created during the group identity formation task. They were also informed that the Arado had decided that the Hoye and the Suebla would sort sixty beads each, while they (the Arado) would not sort any beads. They were also informed that the Arado had decided that the Hoye and the Suebla would both sort sixty beads each, while they (the Arado) would not sort any beads. Collective autonomy threat/work equality. In this condition, the Hoye groups were informed by the experimenter that the Arado group had changed their culture, as well as the Suebla’s culture. They were then given a coat of arms, food and assigned custom that were different to what they created during the group identity formation task. They were also informed that the Arado had decided that each of the three groups (the Arado, the Hoye, and the Suebla) would sort sixty beads each. Collective autonomy support/work inequality. In this condition, the Hoye groups were informed that the Arado group had decided to allow them (and

J. Bertin the Suebla) to keep their culture. They were then given the coat of arms, food, and assigned custom that they created during the group identity formation task.1 Individual Questionnaire. At the end of the experiment participants individually completed a computer questionnaire in which we assessed the key constructs of interest in the present research. Essentialist beliefs about good or evil. We assessed essentialist beliefs about good or evil with four items we generated for the purpose of the experiment: “In this world, there are two kinds of people: good people and bad people”; “At their core, each person is either good or bad”; “Everyone has an equal capacity to do either good or bad”; “There is no such thing as a good or bad person”. Participants rated each item on a scale from 1 (strongly disagree) to 7 (strongly agree), α = .64. Outgroup blaming and system blaming attribution. To assess outgroup blaming attributions participants rated their agreement to one item: “I think the Arado’s actions during the experiment is a reflection of who they are, at the core, as a people.” To assess system blaming attributions participants rated their agreement to one item: “Any group of people in the Arado’s position would have acted in the same way that they did during the experiment.” These items were each face valid items adapted from a previous attribution scale by Caroll and colleagues to fit the fictional context of this experiment. Participants rated each item on a scale from 1 (strongly disagree) to 7 (strongly agree). The two items did not significantly correlate (r = .02, p = .76) and were treated as distinct constructs in our analyses. Hostile Emotions. Participants rated the extent to which they felt “anger”, “disgust”, and “contempt” towards the high-power (Arado) group. Participants rated each item on a scale from 1 (not at all) to 7 (very much so), α = .73. 1

Power inversion. The experimental simulation of oppression manipulation, groups were given the opportunity to become the high power group and make a series of decisions that would impact the Suebla and Arado. However, while the power inversion is a central component of the larger experiment, it is beyond the scope and focus of the present research.


Why Do You Treat Me This Way? Hostile Attitudes (Prejudice). To measure hostile attitudes (prejudice), we used a feeling thermometer (Haddock, Zanna, & Esses, 1993). Specifically, participants rated how warm or cold they felt toward the high-power Arado outgroup and their own Hoye in group from 0 (very cold) to 100 (very warm). Hostile attitudes (prejudice) was calculated by subtracting the rating for the Arado from the rating for the Hoye (ingroup) such that higher scores indicate greater prejudice. Results Analysis Strategy The participants in this experiment were nested within groups. As a result, we could not assume that the responses of one participant in a given group were independent of the responses of other participants within their same group with whom they interacted throughout the experiment (Kenny & La Voie, 1985). To account for this non-independence, we conducted a regression analysis of the data using multilevel linear modeling (Finch, Bolin, & Kelley, 2014; Hayes, 2006) that took into account the random effect of the unique group within which participants were nested for the duration of the experiment. We conducted a series of multilevel regressions. First, to test (H1) we regressed participants’ hostility toward the outgroup (person level factor) on participants’ tendency to make an outgroup blaming attribution and to make a system blaming attribution for why the outgroup oppressed their ingroup (person level factors). In this regression, we controlled for the type of oppression that group members experienced (i.e., equality threat and/ or collective autonomy threat). Then to test (H2), we regressed participants’ tendency to make an outgroup blaming attribution and to make a system blaming attribution on their tendency to hold essentialist beliefs about good or evil (a person level factor). We again controlled for the type of oppression participants experienced. Analysis Results Table 1 contains a summary of the means, standard deviations and correlations for all measured variables. Table 2 contains a summary of the regression analysis results for the three hypotheses. Figure 2 contains a path diagram illustrating the mediation relationship between essentialist beliefs about good and evil and hostile attitudes (prejudice).

29 The intraclass correlation coefficients (ICCs) for beliefs about good and evil, ICC = .24, 95% CI [.14, .38], outgroup blaming attribution, ICC = .26, 95% CI [.15, .39], system blaming attribution, ICC = .40, 95% CI [.36, .44], hostile attitudes ICC = .35, 95% CI [.30, .41], and hostile emotions, ICC = .32, 95% CI [.26, .39], were all significant. This means that oppressed group members’ perceptions of beliefs about good and evil, outgroup blaming attribution, hostile emotions and hostile attitudes were interrelated with the perceptions of their other group members. Attribution and Outgroup Hostility. As expected oppressed group members who made an outgroup blaming attribution for their oppression were more likely to hold hostile attitudes (prejudice) toward the oppressive outgroup than those who did not, γ = 4.00, 95% CI [2.03, 5.98], t(153) = 4.00, p < .001. Unexpectedly however, there was no significant effect found for system blaming attribution. Those who made a system blaming attribution were not less (or more) likely to hold hostile attitudes toward the oppressive outgroup than those who did not, γ = -.44, 95% CI [-2.30, 1.14], t(153) = -.46, p = .65. Furthermore, there was no significant effect of either outgroup blaming attribution style (or system blaming attribution style) on hostile emotions (anger, disgust and contempt). Group members who made an outgroup blaming attribution were not any more (or less) likely to feel hostile emotions toward the oppressive outgroup, γ = .07, 95% CI [-.04, .19], t(152) = 1.27, p = .20. Similarly, group members who made a system blaming attribution were not any less (or more) likely to feel hostile emotions toward the oppressive outgroup, γ = .01, 95% CI [-.09, .12], t(152) = .25, p = .80. Essentialist Beliefs and Attribution. As hypothesized oppressed group members who held essentialist beliefs about good or evil were more likely to attribute their oppression to the oppressive outgroup’s core nature (make an internal outgroup blaming attribution) than those who did not, γ = .45, 95% CI [.30, .59], t(154) = 6.19, p < .001. However, there was no effect of essentialist beliefs on likelihood to make a system blaming attribution. Those who held essentialist beliefs were not any less (or more) likely to make a system blaming attribution than those who did not, γ = .03, 95% CI [-.13, .19], t(154) = .32, p = .75.


30 Essentialist Beliefs, Attribution and Outgroup Hostility. As predicted, oppressed group members who held essentialist beliefs about good or evil were more likely hold hostile attitudes (prejudice) toward the oppressive outgroup than those who did not, γ = 3.55, 95% CI [1.19, 5.90], t(154) = 2.97, p = .003. However, there was no relation between beliefs about good and evil and whether groups felt hostile emotions toward the outgroup, γ = .11, 95% CI [-.02, .24], t(153) = 1.65, p = .10. Mediation analysis. We used Bauer, Preacher, and Gil’s (2006) approach for testing mediation within a multilevel model to test whether oppressed low power group members’ essentialist beliefs about good or evil indirectly affected their tendency to feel hostile attitudes (prejudice) toward the oppressive high power outgroup, by increasing their tendency to make an outgroup blaming versus system blaming attribution for the outgroup’s oppressive behaviours. Specifically, we tested a 1-1-1 multilevel mediation model in parallel in which we examined whether beliefs about good or evil indirectly impacted outgroup prejudice through the two independent types of attributions that group members could make (i.e., outgroup blaming and system blaming attributions). The analysis was conducted with SPSS using the MLmed macro (Rockwood & Hayes, 2017) specifically designed for testing multilevel mediation models. Note that in the model, both “within group” effects (i.e., were members within a group who relatively higher/lower in X compared to other group members higher/lower in Y than other group members) and “between group” effects (i.e., were groups with relatively higher/lower aggregate levels of X relatively higher/lower in aggregate levels of Y compared to other groups). In this research, we focus on the within group effect because we are interested in how individual differences amongst group members’ essentialist beliefs and attributions impact their personal attitudes to the outgroup. Moreover, it is unclear whether the combined “aggregate” effects of such individual differences within a group necessarily would translate to a group outcome. In support of the mediation hypothesis, essentialist beliefs about good or evil were no longer a significant predictor of hostile attitudes (prejudice), after controlling for outgroup blaming attribution as a mediator, direct effect = 1.99, SE = 1.49, p = .18. Rather we found a significant indirect of essentialist

J. Bertin beliefs on hostile attitudes through group members’ tendency to make an outgroup blaming attribution, indirect effect = 1.56, SE = .66, 95% CI [.42, 2.97], p = .02. However, no significant indirect effect of essentialist beliefs on hostile attitudes through system blaming attributions was found, indirect effect = .01, SE = .14 95% CI [-.28, .34], p = .92. General Discussion In this research, we examined how individual differences in the essentialist beliefs and attribution styles of oppressed group members impact how oppressed group members develop outgroup prejudice toward their oppressors in response to experiencing oppression. To do so we conducted a unique and novel simulation experiment which set a hierarchical intergroup context in which we were able to experimentally manipulate a real oppression experience. Participants’ experience of the oppression as a real psychological effect was evidenced by their verbal and behavioral declarations of anger and dissatisfaction at their oppression. For instance, when given the opportunity to write letters to the oppressive high power outgroup, one group gave the experimenters instructions embedded in the letter to enact a revolution in which they would seize power from the outgroup In support of our first hypothesis, this study provided evidence that making an internal outgroup blaming attribution predicted greater outgroup prejudice. We found that oppressed group members who tend to make internal outgroup blaming attributions tend to hold more hostile attitudes towards the oppressive outgroup. This study extends existing knowledge about the relationship between attribution and prejudice. Stephen, Ybarra, and Morrison (2009) have argued that the experience of threat in the intergroup context would illicit cognitive biases in perception of the outgroup, such as increasing likelihood of making an internal attribution for an outgroup’s negative behaviour, but an external attribution for their own ingroup’s negative behaviour (Stephan et al., 2009). Additionally, previous research has found that prejudice predicted the tendency to make an internal, dispositional attribution for the negative behaviour of an outgroup (Pettigrew, 1979). The findings of this study suggest that this previously observed relationship between prejudice and attribution may be a reciprocal one. Just as prejudice leads to an increased tendency to make internal attributions, internal attributions may


Why Do You Treat Me This Way? also lead to increased prejudice. The study’s findings were also consistent with our second hypothesis that essentialist beliefs that people have either a fundamentally good or evil nature would predict people’s tendency to make an internal outgroup blaming. We find that when oppressed group members believed that people are fundamentally good or evil (versus shades of grey), they were also more likely to make an internal outgroup blaming attribution than those who did not. These findings expand on existing research about the role of lay theories and beliefs on the attribution process and specifically on the role of essentialist beliefs. Essentialism has been found to be related to the attribution process and specifically the process of making social attributions for behaviour (Yzerbyt & Rogier, 2001). However, Yzerbyt and Rogier focused on essentialist beliefs about group difference, that is, the belief that groups have an essence which every one of its members carries. In this study, we identify basic essentialist beliefs about peoples’ either fundamentally good or bad natures as another lay theory. Critically, this basic essentialist belief that is not specific to groups leads to an increased likelihood of making an internal, dispositional attribution for the negative behaviour of an outgroup. In other words, essentialist beliefs about human nature in general can also impact group level perceptions and attributions. Supporting our third hypothesis, we ultimately find that people who hold essentialist beliefs about people’s good or evil natures tend to hold more prejudiced attitudes towards an outgroup that has oppressed their ingroup, as a direct result of their increased likelihood to make an internal outgroup blaming attribution. Importantly, the principal theoretical contribution of this research is that it identifies the psychological mechanism for why essentialist beliefs may lead to increased prejudice – through the social cognitive process of attribution. In an oppressive intergroup context, this relationship between essentialist beliefs and social attribution translates to a generalization of negative dispositions to an entire group of people. People’s essentialist beliefs about good or evil natures in general become essentialist beliefs about the oppressive group’s core nature through the process of attribution, resulting in an increase in prejudiced attitudes toward that group. In other words, essentialist beliefs about individual natures become social essentialism in the intergroup context.

31 While essentialist beliefs about good or evil and attribution style led to greater prejudiced attitudes towards the oppressive outgroup, these individual differences did not impact the hostile emotions, such as anger and contempt, that group members felt toward the outgroup. Whether oppressed group members were low or high in essentialist beliefs and whether they were low or high in tendency to make an internal outgroup blaming attribution did not predict the extent to which they felt hostile emotions directed toward the oppressive outgroup. What did predict the extent to which oppressed group members felt hostile emotions toward the oppressive outgroup was the extent to which they experienced oppression – those who experienced greater oppression, experienced more hostile emotions. This suggests that the degree to which oppressed group members feel hostile emotions toward oppressive outgroup is a function of the type and degree of oppression they experience, rather than their individual differences in beliefs and attributions. This is consistent with findings in support of the intergroup threat theory which demonstrate that groups react to realistic and symbolic threats with hostile emotions (Stephan et al., 2009) The real world implications of this research are important. The research findings indicate that the oppressed will naturally always feel angry at their oppressors, which is a reality that could prove to be more positive than it may sound. Research has previously identified group emotion as one pathway to collective action (van Zomeren, Postmes, & Spears, 2008). This means that if oppressed groups react to their oppression with hostile emotions such as anger, this may motivate them to take action alleviate their oppression. What is more, this research distinguishes an angry response from a prejudiced response. While anger may drive collective action and ultimately prove to be a desired reaction, prejudice may instead play a role in the continuing cycles of friction and hostility that characterize a lot of intergroup relations, such as those between White and Black Americans in the USA. By identifying essentialist beliefs and the attribution process as mechanisms behind a prejudiced reaction to oppression, this research helps understand the means by which intergroup conflict may become characterized by reciprocal hostility and animosity. It can therefore serve as a step toward preventing a cycle of conflict, through policy interventions and media influence, for example, and opening up


32 avenues for reconciliation and allyship. Caveats and Future Directions While essentialist beliefs were significantly related to oppressed group members making an internal outgroup blaming attribution for their mistreatment, we found no relation between essentialist beliefs and making an external system blaming attribution. Additionally, unlike internal outgroup blaming attributions, external system blaming attributions were unrelated to holding hostile attitudes (outgroup prejudice). Moreover, our two items assessing internal and external attributions were unrelated to each other, r = .02, p = .76. It is possible that the item which we used to measure an external blaming attribution was unclear to participants and did not adequately capture the construct. The item used in the study read as follows: “Any group of people in the Arado’s position would have acted in the same way that they did during the experiment.” This item may not fully represent all facets of system blaming attribution (i.e. it was lacking in content validity). We defined system blaming attribution as attributing the outgroup’s oppressive behaviour to the inegalitarian system and societal structures that allows for oppressive behaviour between groups. However, this item made reference to the high power group’s position without directly referencing the system or power structure of Grabodia (the fictional society within the experiment). The absence of a clear reference to a system or power structure in the item may account for the possible content invalidity resulting in the insignificance of this variable in our study. What is more, while we assessed hostility (attitudes and emotions) toward the outgroup, we did not specifically assess whether oppressed group members directed hostility toward the inegalitarian Grabodian system and societal structure. Measuring this may have revealed effects of system blaming attribution that were not revealed in this research – a limitation that future research can address. One other explanation may be that the items measuring attribution were contained in a questionnaire that was given to participants after the power inversion manipulation in the experiment. The power inversion manipulation allowed the oppressed low power groups to gain power and make decisions impacting the other groups. This power inversion manipulation and the subsequent decisions that the groups made were not elements of

J. Bertin the experiment used in this research. However, the measure for this variable was given after this portion of the experiment and so its potential confounding effects should be considered. At the moment oppressed group members rated the attribution items, they were no longer low power groups. Rather, they had gained the power within Grabodia and had already made decisions that were either oppressive or not oppressive toward the formerly high power group and the other low power group. As such, in responding to whether they thought that “any group of people in the Arado’s position would have acted in the same way that they did during the experiment,” group members may have been influenced by their own group’s behaviour, especially if they had not been oppressive themselves. Future research can address the experimental and theoretical limitations of the present research. Although our simulation methodology created an engaging and immersive intergroup environment for participants, it did have the limitation of time. The experiment in this study ran for a duration of 2 to 2.5 hours, during which groups formed their identities, were oppressed, and then gained power. Contrarily, real world experiences of oppression certainly span more than a few hours and can in some cases span decades and centuries. Future directions for research can address this experimental limitation by conducting a study in which participants experience oppression over a longer period of time (such a few days or weeks). This would increase the similarity to a realistic real world experience of oppression and ultimately the ecological validity of the findings. This research is also limited in its theoretical scope. In this study, we focused on two outcomes of hostility (hostile emotions and hostile (prejudiced) attitudes). Importantly, we did not explore whether oppressed group members respond to their oppression by forming more extreme forms of hostile attitudes such as engaging in blatant dehumanization. Dehumanization is an increasingly important and critical factor to consider in relation to sustained and reciprocal intergroup conflict (Hodson, Kteily, & Hoffarth, 2014; Kteily & Bruneau, 2017). For example, Hodson and colleagues noted that outgroup dehumanization (that is seeing the outgroup as less human and more animal than one’s ingroup) leads to various forms of outgroup bias including prejudiced attitudes, stereotypes and discrimination (Hodson et al., 2014). Moreover, essentialist beliefs about good or evil as defined


Why Do You Treat Me This Way? in this research are effectively about beliefs in the constitution of human nature. Outgroup blaming attribution also draws from thinking about core, fundamental characteristics of a group people. It would therefore be of particular interest to examine whether and how these beliefs and processes may lead the oppressed group members to deny the humanity of their oppressors. Furthermore, while the possession of power tends to increase likelihood to engage in dehumanization (Lammers & Stapel, 2010), future research in this direction would add to the more recent literature specifically focusing on dehumanization of oppressive high power groups by oppressed low power groups as a response to oppression and mistreatment (Bruneau & Kteily, 2017; Kteily, 2016). Conclusion Will oppressed groups be hostile to their oppressors? The answer to this question is nuanced. Regardless of individual differences in essentialist beliefs and attribution styles, oppressed group members always feel hostile emotions such as anger toward the oppressive outgroup. However, these emotions may serve as a driving motive for collective action, rather than a source of continuing intergroup conflict. On the other hand, oppressed group members who make an internal outgroup blaming attribution for their oppression are more likely to be prejudiced towards the oppressive outgroup than those who do not. Additionally, having essentialist beliefs about people’s good or bad natures leads to greater outgroup prejudice because it is leads to oppressed groups making outgroup blaming attributions. Ultimately, oppression may always illicit some form of hostility, but the distinct ways in which it manifests – as emotions versus attitudes – carry significantly different consequences. References Ariyanto, A., Hornsey, M. J. & Gallois, C. (2009). Intergroup attribution bias in the context of extreme intergroup conflict. Asian Journal of Social Psychology, 12(4), 293–299. doi:10.1111/j.1467839X.2009.01292.x Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychological Methods, 11(2), 142-163. doi:10.1037/1082989X.11.2.142

33 Callahan, S. P., & Ledgerwood, A. (2016). On the psychological function of flags and logos: Group identity symbols increase perceived entitativity. Journal of Personality and Social Psychology, 110(4), 528-550. doi:10.1037/pspi0000047 Carroll, J. S., Perkowitz, W. T., Lurigio, A. J., & Weaver, F. M. (1987). Sentencing goals, causal attributions, ideology, and personality. Journal of Personality and Social Psychology, 52(1), 107-118. doi:10.1037/0022-3514.52.1.107 Castano, E., Yzerbyt, V., & Bourguignon, D. (2003). We are one and I like it: The impact of ingroup entitativity on ingroup identification. European Journal of Social Psychology, 33(6), 735-754. doi:10.1002/ejsp.175 Dweck, C. S., Chiu, C. Y., & Hong, Y. Y. (1995). Implicit theories and their role in judgments and reactions: A word from two perspectives. Psychological Inquiry, 6(4), 267-285. doi:10.1207/ s15327965pli0604_1 Finch, W. H., Bolin, J. E., & Kelley, K. (2014). Multilevel modeling using R. Boca Raton, FL: Crc Press. Gates, H. L. Jr. (2011). Life upon these shores: Looking at African American history, 1513-2008. New York: Alfred A. Knopf, L.L.C. Haddock, G., Zanna, M. P., & Esses, V. M. (1993). Assessing the structure of prejudicial attitudes: The case of attitudes toward homosexuals. Journal of Personality and Social Psychology, 65(6), 11051118. doi:10.1037/0022-3514.65.6.1105 Haslam, N., Rothschild, L., & Ernst, D. (2002). Are essentialist beliefs associated with prejudice? British Journal of Social Psychology, 41(1), 87-100. doi:10.1348/014466602165072 Ho, A. K., Sidanius, J., Kteily, N., SheehySkeffington, J., Pratto, F., Henkel, K. E., ... Stewart, A. L. (2015). The nature of social dominance orientation: Theorizing and measuring preferences for intergroup inequality using the new SDO₇ scale. Journal of Personality and Social Psychology, 109(6), 1003-1028. doi:10.1037/pspi0000033 Hodson, G., Kteily, N., & Hoffarth, M. (2014). Of filthy pigs and subhuman mongrels: Dehumanization, disgust, and intergroup prejudice. TPM: Testing, Psychometrics, Methodology in Applied Psychology, 21(3), 267-284. doi:10.4473/ TPM21.3.3 Hong, Y. Y., Chiu, C. Y., Dweck, C. S., Lin, D. M. S., & Wan, W. (1999). Implicit theories, attributions, and coping: A meaning system approach. Journal of


34 Personality and Social Psychology, 77(3), 588-599. doi:10.1037/0022-3514.77.3.588 Hooks, B. (2000). Feminism is for everybody: Passionate politics. Cambridge, MA: South End Press. Kachanoff, F. J., Taylor, D. M., Caouette, J., Khullar, T. H., & Wohl, M. J. A. (2018). The chains on all my people are the chains on me: restrictions to collective autonomy undermine the personal autonomy and psychological well-being of group members. Journal of Personality and Social Psychology. Advance online publication. doi:10.1037/pspp0000177 Kassem, S. (2011). Rise up and salute the sun: The writings of Suzy Kassem. [Kindle Version]. Retrieved from: https://www.amazon.ca/Rise-UpSalute-Sun-Writings-ebook/dp/B005CQB6XC/ ref=sr_1_1?ie=UTF8&qid=1524879969&sr=81&keywords=suzy+kassem Keller, J. (2005). In genes we trust: the biological component of psychological essentialism and its relationship to mechanisms of motivated social cognition. Journal of Personality and Social Psychology, 88(4), 686-702. doi:10.1037/00223514.88.4.686 Kelley, H. H. (1973). The processes of causal attribution. American Psychologist, 28(2), 107-128. doi:10.1037/h0034225 Kenny, D. A., & La Voie, L. (1985). Separating individual and group effects. Journal of Personality and Social Psychology, 48(2), 339-348. doi:10.1037/0022-3514.48.2.339 Kteily, N. (2016). The effects of dehumanization on reciprocal dehumanization and collective action. International Journal of Psychology, 51, 919. Lammers, J., & Stapel, D. A. (2011). Power increases dehumanization. Group Processes & Intergroup Relations, 14(1), 113-126. doi:10.1177/1368430210370042 Lerner, G. (1986). The creation of patriarchy. Oxford University Press. MacLean, N. (2009). The American women’s movement, 1945-2000: A brief history with documents. Boston, MA: Bedford/St. Martin’s. Morgan, R. (1977). Going too far: The personal chronicle of a feminist. New York, NY: Random House. Pettigrew, T. F. (1979). The ultimate attribution error: Extending Allport’s cognitive analysis of prejudice. Personality and Social Psychology Bulletin, 5(4), 461-476. Rockwood, N. J., & Hayes, A. F. (2017). MLmed:

J. Bertin An SPSS macro for multilevel mediation and conditional process analysis. Poster presented at the annual meeting of the Association of Psychological Science (APS), Boston, MA. Ross, L. D. (1977). The intuitive psychologist and his shortcomings: Distortions in the attribution process. Advances in experimental social psychology, 10, 173-220. doi:10.1016/S0065-2601(08)60357-3 Sidanius, J. & Pratto, F. (1999) Social dominance: An intergroup theory of social oppression and hierarchy. Cambridge University Press. Stephan, W. G., & Renfro, C. L. (2002). The role of threat in intergroup relations. In D. M. Mackie & E. R. Smith (Eds.), From prejudice to intergroup emotions: Differentiated reactions to social groups (pp. 191-207). New York, NY: Psychology Press. Stephan, W. G., Ybarra, O., & Morrison, K. R. (2009). Intergroup threat theory. In T. D. Nelson (Ed.), Handbook of prejudice, stereotyping, and discrimination (pp. 43-59). New York, NY: Psychology Press. Troester, R. R. (1993). Voices from the catholic worker. Philadelphia, PA: Temple University Press. van Zomeren, M., Postmes, T., & Spears, R. (2008). Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives. Psychological Bulletin, 134(4), 504-535. doi:10.1037/0033-2909.134.4.504 Vasturia, D., Webster, R. J., & Saucier, D. A. (2018). Demons with firepower: How belief in pure evil relates to perceptions and punishments of gun violence perpetrators. Personality and Individual Differences, 122, 13-18. doi:10.1016/j. paid.2017.09.037 Webster, R. J., & Saucier, D. A. (2013). Angels and demons are among us: Assessing individual differences in belief in pure evil and belief in pure good. Personality and Social Psychology Bulletin, 39(11), 1455-1470. doi:10.1177/0146167213496282 X, M., & Haley, A. (1965). The Autobiography of Malcolm X. New York, NY: Grove Press. Yzerbyt, V. Y., & Rogier, A. (2001). Entitativity, subjective essentialism, and social attribution. In J. T. Host & B. Major (Eds.), The psychology of legitimacy: Emerging perspectives on ideology, justice, and intergroup relations (pp. 103-134). Cambridge University Press.


Why Do You Treat Me This Way?

Table 1. Descriptive Statistics: Variable Means, Standard Deviations, and Correlations between Variables

Table 2. Summary of regression analysis results showing effect of beliefs on attribution (H1), attribution on hostile emotions and attitudes (H2), and beliefs on hostile emotions and attitudes (H3)

Figure 2. Path model illustrating significant positive indirect relationship between essentialist beliefs about good or evil and outgroup hostility, mediated by outgroup blaming attribution. Black arrows indicate a significant relationship. Dashed arrows indicate nonsignificance. N.s = non-significant. *p = < .05.

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Blockade of Endogenous Opioid Systems Post-stress in Mice: Restraint vs Social Defeat KARIM ABOU NADER Psychoneuroendocrinology (PNE) of social behaviors, as intricate as it sounds, requires an understanding of different analytical perspectives in order to achieve sufficient comprehension of the topic at hand. In fact, the relationship between different social behaviors and the neural and endocrine processes underlying these social behaviors has been extensively studied. According to Aristotle, humans are “social animals” that are naturally cooperative and have evolved within a salient social world. From an evolutionary standpoint, cooperative behaviors have led to group living which provides enhanced protection against predation. Social behaviors are well represented in our societies and within a wide range of mammalian species. They appear as parental, sexual and more violent and aggressive behaviors. Such complex behaviors and their specific neuroendocrinology could not have been understood without the use of animal models: the wild-type and knock-out mice. In fact, different environmental factors affect social behaviors within their groups (Crusio, 2008). Psychological factors, like anxiety or stress, induce different social behaviors in humans and rodent which led to the discovery of an excellent candidate system underlying these behaviors: the endogenous opioid system (Koolhaas, Korte, Boer, Vegt, & Reenen, 1999) (Nummenmaa & Tuominen, 2018). In mice, the endogenous opioid system modulates a range of different functions related to arousal and motivation (Nummenmaa & Tuominen, 2018). Regulation happens at the level of the main three types of opioid receptors: µ-opioid receptor, d-opioid receptor and k-opioid receptors (Kieffer & Gavériaux-Ruff, 2002). Recent literature provides us important information regarding the tremendous role of this system in social behaviors: µ-opioid receptor knock-out mice show deficits in attachment behaviors (Anna Moles, Brigitte L. Kieffer, Francesca R. D’Amato, 2005), increased fear response (Sora et al., 1997) and

decreased reward (Hall, Sora, & Uhl, 2001). For a more naturalistic understanding of this system, it is interesting to use opioid receptors antagonists, such as naltrexone, to see whether similar effects still exist in wild-type mice. Generally used by physicians for treatment of addiction to opiates such as morphine or heroin, low dose naltrexone has found alternative usage as a novel anti-inflammatory for chronic pain (Younger, Parkitny, & McLain, 2014). The same effects have been discovered at first in mouse models where the blockage of opioid systems induced analgesia following specific acute stressors in rats with neuropathic pain (Roy, 2005). However, very few findings are reported regarding the effects of this powerful antagonist on social coping mechanisms in rodents following exposure to different stressors. Handling stress, as a main form of environmental stressor, and experience of defeat, as a main form of social stress, are potent elicitors of coping mechanisms and adequate anxiogenic behavior inducers in mice (Costa, Vicente, Cipriano, Miguel, & Nunes-De-Souza, 2016). In contrast, social support has been shown to actively buffer the effects of these stressors, thereby reducing acute cognitive impairments in adolescent mice (Kim et al., 2018). In this optic, it would be interesting to inject an opioid antagonist in mice and see whether it has important effects on its social coping system. Would the administration of naltrexone, an opioid antagonist, interfere with the endocrine coping mechanisms and subsequent behavioral functions occurring in response to different stressors? We postulate that normal social buffering will be inhibited following naltrexone administration. Moreover, restraint and social defeat would enhance behavioral distress as well as disrupt normal HPA functioning. This paper first requires us to set the adequate experimental methods and manipulations to then analyze the potential results and limitations that we will encounter to finally


Blockade of Endogenous Opiod Systems Port-Stress in Mice provide some arguments in favor of the application of the findings within the field of PNE and the social world in which we live. Methods Exposing mice to stress entails intermittent exposure to various situations that present opportunities to experience the stressor, learn it, and cope with it (Brockhurst, Cheleuitte-Nieves, Buckmaster, Schatzberg, & Lyons, 2015). The type of situation implies exposure to different stressor types, whether social or environmental, and permit us to assess the consequential social behavior, approach, and avoidance, and the underlying neuroendocrine functioning via cortisol assessment. To better understand the effects of naltrexone on those different stressors, two experiments will be conducted. Experiment 1 will focus on exposing mice to restraint stress while experiment 2 will focus on social defeat. In two blind studies, 20 mice (10 female, 10 males) housed individually were used to understand the effects of naltrexone on social behaviors. The experimental group, consisting of five male and five females, was first injected with naltrexone (10 mg.kg-1) while the control group, consisting of five male and five females, was injected with Saline (1 mL.kg-1). Experiment 1 Following injection, as mentioned above, mice were transferred to the behavioral testing room individually at least 30 minutes before restraint in order to accommodate to the new housing environment. Then, animals within both groups were restraint in well-ventilated 50 mL tubes to restrict their movements as much as possible and were left in an opaque box for 45 minutes daily. Then, they were moved in a clean housing cage with one agematched male or female for 2 hours daily following the restriction for 10 days (Figure 1). Cortisol levels prior and following testing, corresponding to the black arrows in Figure 1, were assessed daily and time spent either approaching or avoiding the novel mouse or bowl in the proximity zone was also measured. Experiment 2 In this experiment, an aggressive mouse, which is screened for aggression prior to conducting the paradigm, is housed for a few days in parallel in a cage with pierced transparent board that permits

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transfer of odors, pheromones, and vocalizations to the house cage (Toyoda, 2017). Following accommodation, the social mouse injected with saline or naltrexone is introduced into the new territory of the aggressive mouse. Therefore, the aggressive mouse typically attacks the social mouse. Aggression happens for 5 minutes before the social mouse is moved back to a cleaned housing cage with one age-matched male or female mouse for 2 hours daily following exposure to acute physical stressor, aggression, for 10 days (Figure 1). Cortisol levels prior and following testing were assessed daily (Black arrows Figure 1). Time spent either approaching or avoiding the novel mouse or object (bowl) in the proximity zone was also measured. When collecting the data, we coded for preference behaviors of the stressed mouse in the proximity zone of the social stimuli (novel mouse) and novel object (bowl) and plasma cortisol levels via blood samples extracted and centrifuged immediately post sacrifice at day 10. Following data collection, two 3-way ANOVAs will be performed in order to assess the interaction between our independent variables, injection type (naltrexone or placebo), stressor type (social or physical), social interaction (male or female) and our dependent variables individually, cortisol levels, in order to assess the neuroendocrine physiological response and proximity towards social mouse in order to assess approach avoidance behaviors. Expected results Following ANOVA 1, assessing the variations of cortisol levels, results vary between experiments. In experiment 1, we should observe a peak in cortisol levels at around 30 minutes following the restriction with high cortisol maintained for a long period later. Thus, in the naltrexone mouse condition, we observe a significant interaction of increased cortisol levels and restriction even following exposure to unfamiliar mouse. However, in the control condition, stress levels post restriction decrease steadily and efficiently during social interaction with the female social mouse while they are still slightly high in the presence of another male but decrease steadily post exposure. In experiment 2, following acute social defeat of the male mouse, we expect to observe a peak in cortisol levels following aggression with the peak remaining during the whole interaction with the unfamiliar mouse in the naltrexone injected mouse. This effect should not be observed in the


38 presence of placebo. Cortisol levels should decrease gradually following aggression and recovery of social interactions. Secondly, focusing on the results of ANOVA 2, in assessing approach-avoidance behaviors, we should observe a dramatic increase in social avoidance under naltrexone influence poststress. In experiment 1, following restraint stress, we should see an increased avoidance of con-specifics in both the naltrexone and placebo injected mice. However, in experiment 2, following social defeat, we should observe reduced proximity of the placebo mouse to the same sex unfamiliar mouse but not to the opposite sex mouse. The naltrexone injected mouse should experience reduced proximity to both mice, independently of their sex, showing generalized social avoidance (Iñiguez et al., 2018). According to the findings, naltrexone attenuates the stress-inhibiting effects of social support needed following social stress. The hypothesis will then be confirmed: restraint and social defeat disrupt normal cortisol adaptation within the HPA axis. The administration of an opioid antagonist interferes with social buffering of social stress independently of unfamiliar mouse sex. Future Limitations While mice models provide a solid framework for translational research in terms of opioid reward system and behavioral function, the endogenous opioid system as a main actor in social stress coping might still not be fully understood (Slavich, Tartter, Brennan, & Hammen, 2014). Many limitations can arise within this scientific model. Controlling as much as possible for variables, social stress might not be exerted to the same extent in each trial and in male or female mice, thus providing some cause for concern with regards to the effect that sex-specific stress variation and sexual motivation might have. Moreover, as the opioid system mediates social mood and behaviors, it might contribute to stress resilience in mice. Recurring social defeat provides an adequate model for the development of resilience seen in the mice with more exposure to the dominant subject. Higher approach and cortisol levels were observed in such mice, thereby skewing the results (Takahashi et al., 2017). Consideration of all the possible variables and modulations is then cardinal to the advancement of PNE in the social endogenous opioid response.

K. Nader Impact and Significance The opioid system, as mentioned previously, has an adaptive role in animals and humans. It does more than alleviate the physical pain, and modify social behaviors and neuroendocrine functioning. While the psychology of social bonding in humans and its underlying physiological networks are being extensively studied, rodent-models permit us to provide adequate results regarding the lack of mechanistic focus on the endogenous opioid theory of social attachment. The system blockade attenuates the effects that social supports exert in mice following stress. Careful extrapolation of the results might permit us someday to biologically alter disruptive and extreme social relationships in humans. If we understand opioid mechanisms in social relationships, agonist administration might facilitate social bonding. This can then prove beneficial in treating a vast range of disorders, ranging from early childhood autism to PTSD and various personality disorders. References Anna Moles, Brigitte L. Kieffer, Francesca R. D’Amato. (2005). Deficit in Attachment Behavior in Mice Lacking the µ-Opioid Receptor Gene. Science, 304(5679). Brockhurst, J., Cheleuitte-Nieves, C., Buckmaster, C. L., Schatzberg, A. F., & Lyons, D. M. (2015). Stress inoculation modeled in mice. Translational Psychiatry, 5(3), e537-5. https://doi.org/10.1038/ tp.2015.34 Costa, N. S., Vicente, M. A., Cipriano, A. C., Miguel, T. T., & Nunes-De-Souza, R. L. (2016). Functional lateralization of the medial prefrontal cortex in the modulation of anxiety in mice: Left or right? Neuropharmacology, 108, 82–90. https://doi. org/10.1016/j.neuropharm.2016.04.011 Crusio, W. E. (2008). Computational Neurogenetic Modeling. Genes, Brain and Behavior, 7(7), 831–831. https://doi.org/10.1111/j.1601-183X.2008.00424.x Hall, F. S., Sora, I., & Uhl, G. R. (2001). Ethanol consumption and reward are decreased in μ-opiate receptor knockout mice. Psychopharmacology, 154(1), 43–49. https://doi.org/10.1007/ s002130000622 Iñiguez, S. D., Flores-Ramirez, F. J., Riggs, L. M., Alipio, J. B., Garcia-Carachure, I., Hernandez, M. A., … Castillo, S. A. (2018). Vicarious Social Defeat Stress Induces Depression-Related Outcomes in Female Mice. Biological Psychiatry. https://doi.


Blockade of Endogenous Opiod Systems Port-Stress in Mice org/10.1016/j.biopsych.2017.07.014 Kieffer, B. L., & Gavériaux-Ruff, C. (2002). Exploring the opioid system by gene knockout. Progress in Neurobiology. https://doi.org/10.1016/ S0301-0082(02)00008-4 Kim, J. W., Ko, M. J., Gonzales, E. L., Kang, R. J., Kim, D. G., Kim, Y., … Shin, C. Y. (2018). Social support rescues acute stress-induced cognitive impairments by modulating ERK1/2 phosphorylation in adolescent mice. Scientific Reports, 8(1), 1–13. https://doi.org/10.1038/s41598-018-30524-4 Koolhaas, J. M., Korte, S. M., Boer, S. F. De, Vegt, B. J. Van Der, & Reenen, C. G. Van. (1999). Coping styles in animals: current status in behavior and stress-physiology. Neuroscience and Behavioral Reviews, 23. https://doi.org/10.1016/S01497634(99)00026-3 Nummenmaa, L., & Tuominen, L. (2018). Opioid system and human emotions. British Journal of Pharmacology, 175(14), 2737–2749. https://doi. org/10.1111/bph.13812 Roy, S. (2005). In Vivo Activation of a Mutant -Opioid Receptor by Naltrexone Produces a Potent Analgesic Effect But No Tolerance: Role of -Receptor Activation and -Receptor Blockade in Morphine Tolerance. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.0332-05.2005 Slavich, G. M., Tartter, M. A., Brennan, P. A., & Hammen, C. (2014). Endogenous Opioid System Influences Depressive Reactions to Socially Painful Targeted Rejection Life Events. Psychoneuroendocrinology., 49(12), 141–149. https://doi.org/10.1016/j.psyneuen.2014.07.009. Sora, I., Takahashi, N., Funada, M., Ujike, H., Revay, R. S., Donovan, D. M., … Uhl, G. R. (1997). Opiate receptor knockout mice define μ receptor roles in endogenous nociceptive responses and morphine-induced analgesia. Proceedings of the National Academy of Sciences, 94(4), 1544–1549. https://doi.org/10.1073/pnas.94.4.1544 Takahashi, A., Chung, J. R., Zhang, S., Zhang, H., Grossman, Y., Aleyasin, H., … Russo, S. J. (2017). Establishment of a repeated social defeat stress model in female mice. Scientific Reports, 7(1), 4–15. https://doi.org/10.1038/s41598-017-12811-8 Tomasello, M. (2014). The ultra-social animal. European Journal of Social Psychology, 44(3), 187– 194. https://doi.org/10.1002/ejsp.2015 Toyoda, A. (2017). Social defeat models in animal science: What we have learned from rodent models, (February), 944–952. https://doi.org/10.1111/

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asj.12809 Younger, J., Parkitny, L., & McLain, D. (2014). The use of low-dose naltrexone (LDN) as a novel antiinflammatory treatment for chronic pain. Clinical Rheumatology. https://doi.org/10.1007/s10067014-2517-2 Appendix A

Figure 1. Example of a Trial for each Experimental Procedure

Experiment 1. Mice is first injected with either placebo or Naltrexone. It is then transferred into the novel environment after 30 minutes of injection and undergo restraint stress for 45 minutes. Then it is transferred to the social preference paradigm. Each mouse goes through the procedure which lasts 230 minutes. Experiment 2. Mice is first injected with either placebo or Naltrexone. It is then transferred into the novel environment after 30 minutes of the injection and undergo social defeat for 5 minutes. Following this, mice are separated, and the defeated mouse is subject to the social preference paradigm. Each mouse goes through the procedure which lasts 185 minutes.


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In Types of Deceptive Communication, Where Does the Role of Machiavellianism Lie? VAISHNAVI KAPIL Abstract The current study examined the relationship between those types of lies and their functions, and Machiavellianism. Adults, aged 18 to 35 (Mage=20.88; SD=2.81), kept diaries of their lies told for 14 days, and completed the Mach-IV, a 20-item scale that measures the extent to which they endorse Machiavellian teachings. Overall, participants reported a mean of 10.32 lies over 14 days (SD=5.505), and reported more antisocial (M=7.74; SD=4.42) than prosocial lies (M=2.48; SD=2.17). The most common motive for lying was instrumental (i.e., to gain a reward) (M=3.12; SD=2.29), and the least common motive was lying to hurt others (M=0.06; SD=0.31). Moreover, it was found that Machiavellianism was a significant positive predictor of subtypes of antisocial lies (e.g., instrumental lies and lies to protect oneself), accounting for 10% of the variance in the frequency of antisocial lies told (p=.03). This was not the case for prosocial lies (p=.78). Findings suggest that while Machiavellianism is a significant predictor of antisocial lies, much of the variance remains unaccounted for. Machiavellianism could be linked to a specific profile of antisocial liar. Further implications for research on lie-telling are discussed. Keywords: lie-telling, motives, Machiavellianism In types of deceptive communication, where does the role of Machiavellianism lie? “[Y]ou must be a great liar…. [A] deceitful man will always find plenty who are ready to be deceived.” Niccoló Machiavelli extensively endorsed the use, when required, of duplicity and manipulation in his political writings (Machiavelli, 1995, ch. XVIII). Machiavelli’s writings have been highly influential in the realms of political theory, and have also expanded in their impact on law, business and psychology. His impact is divisive and controversial, with some more resistant than others to embrace his

emboldened avocations for the use of manipulation, such as cheating and telling lies for self-benefit. In everyday life, although dishonesty is generally frowned upon, it has been observed that adults admit to lying on a daily basis in their natural environments (DePaulo, Kashy, Kirkendol, Wyer & Epstein, 1996). Studies illustrate that adults report telling between 1 and 7 lies daily, pointing to the possibility that lying is a regular, ubiquitous human capacity (DePaulo et al., 1996). Some personality researchers, however, have attempted to draw attention onto individual differences in lying patterns, pointing to how certain types of individuals are more prone to lie than others. Such individuals, called prolific liars, produce more lies than others. Based on survey data, prolific liars have been found to be usually male, younger and of a higher occupational status (Serota, Levine & Boster, 2010). Some studies have even gone beyond isolating demographic factors when identifying individual differences in lying patterns. Recently, some personality researchers have suggested that the tendency to be honest and abstaining from lying is a distinct personality trait possessed by some and not others (Ashton & Lee, 2001). Further studies exploring the unique characteristics of those that lie more frequently would strengthen the position that lying is not just a behaviour carried out all the time by everyone, but only by certain types of individuals. The current study aims to investigate if there are indeed individual differences in these lietelling patterns. Specifically, we examine differences in the frequency and motives behind types of lies reported by individuals based on the extent to which they endorse Machiavellian teaching. Young Adults’ Naturalistic Lie-telling To date, most research on lying has been conducted in experimental settings, wherein researchers manipulate the types of situations in which individuals have to choose between lying or telling the truth (e.g., DePaulo, Blank, Swaim, &


The Role of Machiavellianism in Lie-Telling Hairfield, 1992; Riggio & Friedman, 1983). These experimental designs are limited to laboratory settings, however, and have thus been insufficient in providing information about the types of people who voluntarily and regularly elect to tell lies in their naturalistic settings. So far, there have only been a few studies looking into lie-telling in individuals’ daily lives. Self-reported data, such as that collected from daily lie diaries kept by participants, is a useful way to get much needed externally valid data on this behaviour. For example, a few researchers have used diary methods to study naturalistic lietelling, finding that the participants who told more lies possessed distinct personality characteristics, such as being more manipulative, more concerned with self-presentation, and more sociable (DePaulo et al., 1996). They also found that in the one week span of recording their lies, participants told more self-centered and antisocial lies than other-oriented, prosocial lies, except in dyads involving only women. In that study, self-centered and antisocial lies, were composed of lies told to protect or enhance the liars psychologically, to advance or protect the liars’ own interests, and to elicit a particular emotional response that the liars desired. Lies told to harm others were not included in their definition of antisocial lies. Other-oriented and prosocial lies, in contrast, were composed of lies told to protect others or benefit them psychologically and to advance or protect the interests of others. Although links were found between these two broader types of lies and several personality constructs (e.g. manipulativeness), there remains a dearth of research that has found links between specific personality constructs as they relate to the variety of different motives that underlie prosocial and antisocial types of lies. Lie-telling and Machiavellianism Early research, such as from diary studies in the 1990s (DePaulo et al., 1996) has pointed to correlations between frequent lying behaviour and the construct of manipulativeness. Such correlations warrant further inspection, especially in the changed current context of increasingly digital communication. Although research has found lie-telling to be normal, everyday behaviour that results from situational demands, there have also been attempts to examine whether certain personal qualities lead people to have more distinct lying patterns than others (DePaulo et al., 1996). Given that lies can be told in pursuit of personal rewards

41 or to give off certain impressions of oneself, it could be that there is a subset of individuals who are more willing to lie to achieve their goals. This type of antisocial lie-telling, could fall in line with the personality construct of Machiavellianism. In layman conceptions, Machiavellianism is the use of guile, manipulation and deceit for personal gain (Geis & Moon, 1981). Although Machiavelli did not advocate lying as the best practice in all circumstances in his writings, he wrote that one should maintain an image of virtue while practicing whatever means were required to achieve one’s ends (Geis & Moon, 1981). So, when the truth is detrimental to the pursuit of one’s goals, a lie should be told instead. Inspired by Machiavelli’s writings and their observation of subsets in the population that seem to embody these values, Christie & Geis (1970) developed the Mach Scale, a measure of individuals’ self-reported alignment with Machiavellian qualities. They consequently found that those high in Machiavellianism view others cynically, show little concern for conventional morality, and openly admit that they will manipulate others to get what they want (Christie & Geis, 1970). A number of empirical studies have since demonstrated that high scorers on the Mach Scale (henceforth referred to as high Machs) tell lies more convincingly when induced to lie in an experimental set-up (e.g., Geis & Moon, 1981) as well as show differentiated physiological reactions, such as galvanic skin responses and pulse rates, to low Machs when induced to lie in simulations (Bradley & Klohn, 1987). In contrast with these experimental findings, only one naturalistic study to date has examined the relationship between manipulativeness (measured using the Mach Scale) and lie-telling frequency. DePaulo et al. (1996) found that those high Machs report telling more lies in their lie diaries. Building upon these early inspections of differences between high and low scorers on the MachIV, the current study aims to find data to further investigate the relationship between lie-telling and Machiavellianism using a longer, two-week diary period. The Current Study The current study aims to corroborate early findings of diary studies, and to investigate the underlying motives behind the broad antisocial and prosocial categories as they relate to participants’


42 Machiavellianism. We attempt to investigate how the personality construct of Machiavellianism relates to the different motives underlying the two broad prosocial and antisocial lie types. By measuring participants’ Mach-IV scores as it relates to their self-reported lies, the study intends to determine whether lie-telling might have different patterns for those with high and low Mach-IV scores. This entails looking at frequency of lies told as well as the different motives behind antisocial and prosocial lies told. In the current study, the diary-keeping period was double that of early studies. This allowed us to collect a larger volume of reported lies to discern a stable estimate of the frequency with which different types of motives behind prosocial and antisocial lies are told, to find more intricate links between participants’ Mach scores and patterns of lie-telling and the specific motives behind antisocial and prosocial lies. Because of previous research findings illustrating that those with high scores on the Mach-IV report telling more lies in daily diary studies (DePaulo et al., 1996), we hypothesized that young adults with higher scores will also tell more lies. We also hypothesized, in line with previous research, that high Mach-IV scores will be linked to a specific type of lie— the antisocial lie. Method Participants A total of 50 young adults (NFemale= 39) between the ages of 18 and 35 (M=20.88; SD=2.81). Participants were primarily undergraduate students in a Canadian University. 
 Materials Diaries. The current study made use of a diary method to capture daily lies in participants’ natural settings. Diaries were provided to participants. In the current study, participants filled out the diaries by hand or digitally, daily, for a twoweek period. The diaries included space for the participants to include a description of lies told in an interaction or the situation in which they were told, the eventual outcome of the stated interaction, their mood during the interactions, and to whom the lies were told. This method is useful for a number of reasons. Firstly, it allows for bottom-up accounts of deceptive communication in participants’ daily environments, with opportunities for participants to give additional contextual information such as the mood of the interaction, the outcome of the lie-

V. Kapil telling, and who was involved in the interaction. Secondly, since participants record their lying behaviours close to the time that it occurs, the problem of retrospection bias is minimized. Coding of diaries. Two researchers coded the motives behind the reported lies. Each independently coded 30% of the lies (153 out of 520 total lies; Cohen’s κ=0.902). Disagreements were resolved by consensus. After reaching adequate inter-rater reliability, the primary researcher coded the remaining lies independently. Table 1 presents the motives used for coding and examples of each. MACH-IV (Christie & Geis, 1970). The Mach-IV scores of participants were used as a reference to their Machiavellianism. The MACH-IV is a scaled assessment used to measure “Machiavellian Intelligence” skills of participants. It includes three subscales, namely (1) the use of deceit in interpersonal relationships, (2) a cynical view of human nature and (3) the lack of morality. These are composed of 20 items in total. Scores on the Mach-IV range from 0 to 100 with higher scores indicating higher rates of Machiavellianism. Procedure Participants were recruited online and through print ads. Once they expressed interest in participating, they were asked to sign a consent form and were given their diaries to keep for a two-week period. Afterwards, participants were asked to come into the lab to drop off the diaries and complete the MACHIV questionnaire and collect their compensation. Following this, the lies were coded and data was analyzed in SPSS. Results Descriptive statistics Participants reported a mean of 10.32 lies (SD=5.505) in total. Antisocial lies were more frequent than prosocial lies. The most frequent subtypes of antisocial lies were instrumental lies, followed closely by lies to give off a false identity or impression. No lies told out of entitlement were reported. Table 2 and Table 3 present descriptive statistics relative to the frequency of anti-social and prosocial lies reported, respectively. Interestingly, a number of the lies were reported to be told on calls over the phone, or via text messages. An example of such a lie was “I texted my boyfriend I was 5 minutes away when in fact I had just left


The Role of Machiavellianism in Lie-Telling my house”. An exact number for how many lies were told digitally was not calculated, as although participants sometimes specified that they lied over text or phone, most of the time the language used was too ambiguous to determine the medium of lying. Machiavellianism as a Predictor of Types of Lies Two simple linear regressions were run, one for each type of lie. Total Mach-IV scores were entered as the predictor variable (M=58.80; SD=4.74). Antisocial lies. A simple linear regression was calculated to predict types of antisocial lies told based on total Mach-IV scores. A significant regression equation was found, F (1, 48) = 4.961, p = .031). Approximately 10% of the variance (R2 =.094) in antisocial lies told was explained by Machiavellianism. Participants’ predicted number of antisocial lies told is equal to -9.036 + .285 lies when Mach scores are measured on a scale of 1 to 100. Participants’ number of antisocial lies increased .285 for each increase in Mach-IV scores. Prosocial lies. A simple linear regression was also calculated to predict types of prosocial lies told based on Mach scores (high or low scores). The regression equation was not significant, F (1, 48) = 0.77, p = .782). Only 0.2 % (R2 =.002) of the variance in prosocial lies told was explained by Machiavellianism. Discussion In the current study, participants’ total scores on the Mach-IV were calculated to observe differences in their frequencies and patterns of lying, with high scores on the Mach-IV representing a higher level of comfortability using guile and deceit. In addition to exploring the motives behind the use of prosocial and antisocial lies in young adults, the current study also examined Machiavellianism as a predictor of both antisocial and prosocial lies.Results showed that the higher participants scored on the Mach-IV, the more likely they were to tell lies that served to benefit them at the expense of others (i.e., antisocial lies). This finding was in line with early research that informed the current study (DePaulo et al., 1996), even with a longer two-week period of lie diarykeeping. Additionally, the modal types of antisocial lies told were those motivated by instrumental gain and desire to present a false identity. These findings are novel in that they provide deeper insight into the profile of the Machiavellian individual, suggesting

43 their utilization of deception has to do with selfish gain and the management of others’ impressions of their identity. Furthermore, the current study was conducted about two decades after the earlier diary studies. We contend that the recent shift towards digital communication has brought about a new medium for lying than just face-to-face deception, and the manifold digital lies reported in the current study have shown support for this change. Future studies continuing to study deceptive communication via text messaging, email or social media will prove useful in tracing a timeline of naturalistic lie-telling patterns through the ages. Moreover, Machiavellianism was not found to be a significant predictor of prosocial lies. The lack of significance could simply mean that individuals, regardless of scoring high or low on Machiavellianism, tell prosocial lies as and when the situation demands it. Prosocial lies are those that strengthen bonds and promote harmony, and thus telling them may provide long-term social rewards to individuals who tell prosocial lies. In other words, prosocial lie-telling may not be purely selfless, other-oriented deception. Prosocial lies could be told by high and low Machs alike, for it may not only serve to benefit others but also to make others like the prosocial liar. More research is thus needed to explore other personality correlates of prosocial lie-telling. We suggest that constructs such as social desirability be examined, to study whether prosocial lie-telling might be a behaviour related to individuals’ need for social approval if not their level of Machiavellianism. Limitations and Future Directions The current study does present some limitations. The sample size of 50 participants was rather small, leading to concerns about generalizability of our findings. Our effect size for Machiavellianism and antisocial lie-telling, while statistically significant, was modest. However, it is worth considering that in personality research, large variations are normal in that only moderate effect sizes can often be welcome results, as described by Abelson’s (1985) variance explanation paradox, in which even smaller effects can be meaningful. A limitation of the study was that due to convenience sampling, 39 out of 50 participants were female. We suggest that future studies aim to gather larger samples of participants, with a more balanced sex ratio to corroborate the findings of the current study.


44 Another limitation of the diary method used in the current study was that it could not be determined how many opportunities participants had to tell lies in their social interactions in the diary-keeping period. It may have been that some participants had unusually few social interactions during this period, so the number of lies they reported may not have been an adequate reflection of their general tendency to lie. It was our contention, however, that since we asked participants to record their lies in their naturalistic environments for a relatively long period of fourteen days, their reported number of lies would be a close estimate of how many social situations they would typically engage in and tell lies in, given that the alternative of measuring all social interactions of participants was outside the scope of our study. Redeemably, DePaulo et al. (1996), in their study, asked participants to keep a diary of all their social interactions in a seven-day period, not just interactions in which they told lies, and they found a similar relationship as in our study in terms of Mach scores and antisocial lie-telling frequency. Future studies could thus corroborate the current findings by not only extending the period of diary-keeping, but also measuring participants’ lies as a percentage of total social interactions in a given period of time. Lastly, whether or not participants adhered to their diary-keeping was a concern and an-other potential limitation. Although we made it a point to remind participants about the importance of writing their lies down promptly after telling them, we could not monitor this. Some participants may not have remembered to record their lies as they told them, and consequently omit-ted them from their diary or recalled the lies incorrectly after a few days due to retrospection bias. For future studies, we suggest that researchers alleviate this limitation by developing mobile apps for lie diary-keeping or ecological momentary assessment, as such digital methods can remind participants on their phones every few hours to write down any recently told lies. Conclusion The current study showed preliminary support for the possibility that not only is Machiavellianism linked to prolific lying, but that Machiavellianism may also be linked to a specific profile of antisocial liars, particularly those that tell lies concerning their identity and lie for instrumental gain. Despite the limitations discussed above, the current study was the first of its kind to have a two-week period

V. Kapil of diary keeping, and the first to explore specific motives behind lie-telling types as they relate to Machiavellianism in recent years. Accordingly, the results provide insight into the current context of antisocial lie-telling, extending the lie-telling literature beyond data on face-to-face deception while exploring antisocial lie-telling related to Machiavellianism. Statement of Contribution The current study was a part of a larger project titled ‘Self-Report Diaries: Tracking the Trajectories of Lying Across the Lifespan’ under the Talwar Child Development Lab at McGill University. The principle investigator of the project is Dr. Victoria Talwar. A PhD student under the lab, Karissa Leduc, helped supervise Vaishnavi Kapil, the principal investigator of the current study. Leduc contributed to coding and analyzing of data as well as to the writing of the final manuscript. References Abelson, R. P. (1985). A variance explanation paradox: When a little is a lot. Psychological Bulletin, 97, 129-133. Ashton, M. C., & Lee, K. (2001). A theoretical basis for the major dimensions of personality. European Journal of Personality, 15(5), 327-353. Bradley, M. T., & Klohn, K. I. (1987) Machiavellianism, the Control Question Test and the detection of deception. Perceptual and Motor Skills, 64, 747–757. Christie, R., & Geis, F. L. (1970). Studies in Machiavellianism. New York: Academic Press. Coleman L, Kay P. 1981. Prototype semantics: the English word lie. Language, 57, 26–44. DePaulo, B. M., Ansfield, M. E., Kirkendol, S. E., & Boden, J. M. (2004). Serious lies. Basic and Applied Social Psychology, 26, 147–167. DePaulo, B. M., Blank, A. L., Swaim, G. W., & Hairfield, J. G. (1992). Expressiveness and expressive control. Personality and Social Psychology Bulletin, 18, 276-285. DePaulo, B. M., Jordan, A., Irvine, A., & Laser, P. S. (1982). Age changes in the detection of deception. Child Development, 53(3), 701-709. DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996). Lying in everyday life. Journal of Personality and Social Psychology, 70, 979–995. DePaulo, B. M., & Kashy, D. A. (1998). Everyday


The Role of Machiavellianism in Lie-Telling lies in close and casual relationships. Journal of Personality and Social Psychology, 74, 63â&#x20AC;&#x201C;79. Geis, F. L., & Moon, T. H. (1981). Machiavellianism and deception. Journal of personality and social psychology, 41(4), 766. Machiavelli, N., & Wootton, D. (1995). The prince. Indianapolis: Hackett Pub. Co. Riggio, R. E., & Friedman, H. S. (1983). Individual differences and cues to deception. Journal of Personality and Social Psychology, 45, 899-915. Serota, KB., Levine, TR. & Boster, FJ. (2010). The prevalence of lying in America: Three studies of selfreported lies. Human Communication Research, 36, 2-25.

Table 1. Examples of Antisocial Lies Reported

Table 2. Range and Means (SD) of Motives Behind Antisocial Lies Reported

Table 3. Range and Means (SD) of Motives Behind Prosocial Lies Reported

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Loving Intelligently in an Age of Smartphones: The Association Between Social Media Use, Quality of Alternatives, and Relationship Closeness SOPHIE Y. ZHAO Abstract With the ubiquity of social media like Facebook, users are exposed to numerous potential romantic partners with ease. How might this be related to relationship closeness for those already in romantic relationships? The purpose of this research was to determine whether social media use moderates the relationship between perceived quality of alternatives and relationship closeness. Participants in romantic couples completed self-report questionnaires assessing the frequency of social media use, their perceptions of alternatives to their relationship, and perceived relationship closeness. Using a linear mixed model, we found a marginal interaction between social media use and quality of alternatives predicting relationship closeness. Specifically, when social media use was high, having a higher quality of alternatives had a strong negative association with relationship closeness. At lower levels of social media use, the association between quality of alternatives and relationship closeness was no longer significant. Overall, greater social media use may indirectly harm relationship closeness by potentially enhancing the negative impact of perceiving a moderate-high quality of attractive alternatives. Loving Intelligently in an Age of Smartphones: The Association Between Social Media Use, Quality of Alternatives, and Relationship Closeness The rituals and traditions of love have been changing in response to technological advancements like the invention of social media. Social media is relevant to how people search for, maintain, and end romantic relationships (Morey, Gentzler, Creasy, Oberhauser & Westerman, 2013). For example, social media use was related to reduced intimacy in a relationship (Hand, Thomas, Buboltz, Deemer, & Buyanjargal, 2013). One highly pertinent factor to relationship longevity and closeness is quality

of alternatives, which measures perceived levels of potential romantic alternatives, like being alone or dating someone else, capable of fulfilling our psychological and physical needs (Rusbult, Martz, & Agnew, 1998). Although previous research has examined how perceiving potential romantic alternatives and social media use are each separately related to relationship closeness, there is a dearth of research targeting the interplay between quality of alternatives and social media use in predicting relationship closeness. Therefore, the present investigation looked into whether social media use may impact the association between quality of alternatives and perceived relationship closeness. People have social relationships that vary in closeness; most people have some close relationships to a romantic partner and a few friends or family members, somewhat looser relationships with other friends, and even looser ones with numerous acquaintances (Gächter, Starmer, & Tufano, 2015). The subjective closeness of a relationship, like one with a significant other, has implications for social behaviour and relationship satisfaction. Previous research has found a negative association between smartphone use and relationship closeness; this may be linked to how partner phubbing, the act of snubbing someone in a social setting by concentrating on things like social media on oneâ&#x20AC;&#x2122;s smartphone, has become more common with the rise of smartphones (Chotpitayasunondh & Douglas, 2018; Roberts & David, 2016). Partner phubbing has been related to conflict over such use of oneâ&#x20AC;&#x2122;s smartphone which, in turn, has been associated with lower reported relationship satisfaction and ultimately personal well-being (Roberts & David, 2016). Increased phubbing significantly and negatively links to perceived communication quality and relationship satisfaction, and these correlations were mediated by reduced feelings of belongingness and both positive and negative affect (Chotpitayasunondh & Douglas, 2018).


Social Media Use and Relationship Closeness Also related to relationship satisfaction is a person’s quality of alternatives, the number of potential romantic alternatives available, such as being alone or dating somebody else: specifically, this factor has been associated with relationship closeness (Rusbult et al., 1998). Although links have been established between relationship closeness and both social media use and quality of alternatives, research has not yet considered the interplay between frequency of social media use and level of perceived quality of alternatives in predicting relationship closeness. One reason that greater social media use may be associated with reduced relationship closeness is that when usage is high, there may be a greater impact of perceiving a higher quality of alternatives on relationship closeness, as these alternatives might be more salient and appear more available. We tested this hypothesis in the current study. Social Media Use Social media has become integral to the way people communicate with one another, especially in the context of romantic relationships (Morey et al., 2013). The three most-used applications among millennials (18 to 34-yearolds) are Facebook, Instagram, and Snapchat; we took frequency of usage on these three platforms as samples of social media use in this study (Abbasi & Alghamdi, 2018). The platforms offer differing pros and cons. Snapchat provides a more private medium for interaction than Facebook as the messages and pictures are ephemeral and not persistent (Abbasi & Alghamdi, 2018). Resultantly, Snapchat users experience diminished need for self-censorship and tend to share more intimate pictures and messages (Perez, 2014). However, Facebook has added many similar features to win over the millennials and be more private – this includes stories which expire after 24 hours in Facebook Messenger, as well as the option of Secret Chat which allows for the option of having disappearing messages (Kelly, 2016). A comparison of Facebook and Snapchat showed that Snapchat was used more for flirting and finding new love interests, in addition to eliciting higher levels of jealousy than did Facebook when researchers presented users with a series of potentially jealousyprovoking scenarios (Sonja, Nicole, & Cameran, 2015). Facebook focuses on self-expression, privacy, and romantic relationships as the current most popular and dominant hedonic social media platform worldwide,

47 with more users than the rest of the remaining social media platforms combined (Stenovec, 2015). The strongest gratification factor for Facebook use was found to be the interpersonal habitual entertainment, which represents a mix of relationship maintenance, entertainment, and the habit motive (Valentine, 2012). A growing body of research has found that Facebook use is related to relationship conflicts, compulsive Internet use, physical and emotional infidelity, online portrayals of intimate relationships, increase in romantic jealousy, relationship dissatisfaction, low commitment levels, breakups, and divorce (Abbasi & Alghamdi, 2018). Facebook use showed a negative correlation with marriage quality and happiness and a positive correlation with the experience of troubled relationships as well as with thoughts of separation (Abbasi & Alghamdi, 2018). Instagram is the other of the three most-used applications among millennials (Abbasi & Alghamdi, 2018). A survey of 420 Instagram users found that increased Instagram selfie posting was associated with Instagram-related conflict, which related to increased negative romantic relationship outcomes (Ridgway & Clayton, 2016). Instagram has also been found to spark conflicts due to problematic discrepancies in use between partners, such as differences in the amount of use, the timing of the use, the type of connections maintained, or the content shared on a site (Osborn, Fox, & Warber, 2012). For example, James may feel uncomfortable with Stephanie’s insistence on posting all their intimate honeymoon pictures publicly on Instagram, because James prefers to keep his Instagram presence professional (Osborn et al., 2012). To worsen this hypothetical conflict, James could “like” several of his exes’ photos, with the awareness that Instagram would make this visible to Stephanie. These are but a few examples of conflicts which Instagram may engender. Differences between platforms notwithstanding, social media has been known to facilitate infidelity behaviours such as flirting, emotional intimacy, and sexual affairs (Clayton, Nagurney, & Smith, 2013). The most consistently reported online infidelity behaviors included emotional disclosure, cybersex (connecting via a computer network and sending sexually explicit messages describing an erotic experience), hot chatting (communicating with the intent to sexually arouse), and viewing pornography (Dijkstra,


48 Barelds, & Groothof, 2010). Behaviours related to interacting with potentially attractive alternatives on social media included friending one’s expartner, commenting on attractive users’ pictures, sending private messages, and posting an incorrect relationship status (Clayton et al., 2013). Not all research, however, pointed to negative associations between social media use and relationship quality. Individuals who perceived their relationship more positively also reported more frequent communication through technology, both overall and using specific types of channels. For example, social media use and texting were linked to reports of greater relationship satisfaction, intimacy, and support (Morey et al., 2013). Although using social media to communicate with one’s partner may be beneficial, general social media use, especially use that does not include one’s partner, may be detrimental. Online social networks are highly popular, allowing users to interact with a variety of people in an organized format. Although studies found a negative relationship between intimacy and the perception of a romantic partner’s use of online social networks, intimacy mediated the relationship between online social network usage and overall relationship satisfaction, suggesting that social network use decreased relationship satisfaction via decreased intimacy (Hand et al., 2013). This finding may indicate an attributional bias in which individuals are more likely to perceive a partner’s usage as negative compared to their own usage (Hand et al., 2013). When partners were emotionally engaged with their virtual connections, their dependence on the significant other decreased; reduced relationship closeness was a consequence of decreased dependence (Abbasi & Alghamdi, 2018). On the other hand, dependence on the partner increased when people felt satisfied with their relationship, thought unfavorably about the quality of available alternatives, and believed that they have made great investments in their relationship (Abbasi & Alghamdi, 2018; Rusbult et al., 1998). Technological advancements of the present era have spawned a wide array of social media platforms that display boastfully curated, overly glossed profiles of virtual connections which may lead social media users to feel deficient in their lives (Abbasi & Alghamdi, 2018). Previous research has shown that Facebook use can reduce relationship satisfaction by providing potential romantic alternatives and deflecting

S. Zhao time and emotional investments away from the committed relationship (Abbasi & Alghamdi, 2018). These increased opportunities to interact with high quality alternatives, be they potential partners or friends and family (both options count as alternatives to a relationship), could then have been associated with a decrease in relationship closeness as conceived through an inclusion of the other in the self framework. A study by Elphinston and Noller (2011) explored the association these websites have on romantic relationships and found that a user’s excessive attachment to Facebook is associated with increased jealousy and dissatisfaction. This investigation suggested that young people’s levels of Facebook intrusion can be correlated with poorer outcomes in romantic relationships. There have been costs to individuals and their intimate relationships if people develop a reliance on social media for positive social outcomes; in addition, as more adults, many of whom are likely to have long-term, committed relationships outside the university context, enter the world of social media, there is an increasing possibility that these relationships may become strained and influenced by jealousy and dissatisfaction (Elphinston & Noller, 2011). The amount of time a user has spent using online social networks is associated with a higher number of online friends, but not emotional closeness in face to face relationships; this suggested that a user’s investment of time and energy into social media may not have translated into real life social benefits like relationship closeness (Pollet, Roberts, & Dunbar, 2011). A study by Fox and Moreland (2015) showed that social media users often experience negative emotions and feel pressured to access social media frequently due to the fear of missing out and to keep up with relationship maintenance demands. These features also afforded constant social comparison to other network members, which triggered jealousy, anxiety, and other network emotions (Fox & Moreland, 2015). Additionally, relational turbulence occurred due to the public nature of conflict over social media like Facebook (Fox & Moreland, 2015). There are myriad ways in which social media use is associated with users’ interpersonal processes. Overall, past research appeared to suggest that this association is negative. Quality of Alternatives All major theories of relationship commitment


Social Media Use and Relationship Closeness specify that the availability of attractive alternatives should be a negative factor undermining relationship commitment and relationship survival (Lydon, 2010). Quality of alternatives measures the degree to which participants viewed alternatives to their current romantic partner as desirable in meeting psychological and physical needs; the alternative to a relationship could have been the state of being single, being with family and friends, or being with a romantic partner with whom one is not currently involved (Rusbult et al., 1998). In other words, it examines the perceived desirability of the best available alternative to a relationship (Rusbult et al., 1998). For instance, if one has low perceived quality of alternatives, he or she does not think that the needs for intimacy and companionship could be gratified elsewhere, thus relating to an increased dependence on his or her romantic partner. Quality of alternatives is one of the foundations of the Investment Model of relationships, which has considered commitment level, satisfaction level, quality of alternatives, and investment size as being crucial to determining whether a relationship will last. These Investment Model variables were moderately associated with other measures reflecting superior couple functioning, such as trust level and both liking and love for the partner, and thus the variables predicted later levels of dyadic adjustment and whether a relationship persisted or ended (Rusbult et al., 1998). In fact, one study has found that there was no better predictor of relationship failure than high attentiveness to alternatives (Miller, 1997). Quality of alternatives has been negatively associated with commitment level and moderately associated with relationship closeness (Rusbult et al., 1998). Low levels of relationship satisfaction and high levels of Facebook use predicted high alternative monitoring (Abbasi & Alghamdi, 2018). The perceived quality of available alternatives was positively associated with the number of romantic connections on social media (Drouin, Miller, & Dibble, 2014). Other investigations have found that vigilance towards high quality alternatives is negatively correlated with investment in, commitment to, satisfaction with, and adjustment in a dating relationship (Miller, 1997). Rusbult and colleagues (1998) have found some gender differences, though results have been inconclusive: women have tended to report poorer qualities of alternatives and reported less online monitoring of alternatives than men. For men more than for women, social media connections have

49 been found to act as memory primers for recognition of potential sexual alternatives (Drouin et al., 2014). Relationship closeness could be further decreased by a lower time investment, as the time spent by a social media user with virtual friends trades off with the time that could have been invested with the significant other (Abbasi & Alghamdi, 2018). Inattentiveness to alternatives may be a maintenance mechanism that helps to preserve and protect desirable relationships – in other words, even if the grass is greener on the other side of the fence, happy gardeners will be less likely to notice (Miller, 1997). Quality of alternatives was also conceptualized in relation to relationship closeness by researchers who subscribe to the cohesiveness model of relationships, which holds that two social forces (attractive forces and barriers) determine the strength of relationship commitment (Levinger, 1965). These attractive forces are further divided into present and alternative attractions (such as other people with whom one could be involved romantically); present attractions (need fulfillment and love) bring the partners close, while alternative attractions decrease relationship closeness (Levinger, 1965). An individual’s commitment and closeness may fluctuate to reflect the ever-changing balance between attractive forces and barrier forces, which restrain partners from quitting a primary relationship (Levinger, 1965). The cohesiveness model shares with the investment model a focus on the dynamic interplay between what contributes to commitment and closeness (be this satisfaction and investments or “present attractions”) and what decreases them (a high quality of alternatives or “attractive forces”). Measuring quality of alternatives involves determining the extent to which people find alternatives to their romantic situations appealing and satisfying for psychological and physical needs – this includes both the possibility of dating someone else and that of being single (Rusbult et al., 1998). One form of relationship alternatives is the “back burner”: desired prospective romantic or sexual partners that people communicate with to establish a future romantic or sexual relationship; both singles and people in committed relationships have been found to have back burners (Dibble, PunyanuntCarter & Drouin, 2018). The pervasiveness and ease of use of Internet-based media has likely facilitated communication between people in relationships with potentially attractive alternatives; two-thirds of young adults — the most prolific users of social


50 media and devices — maintained communication with back burners through technological mediums (Dibble et al., 2018). This has been found through social media like Facebook and Tinder, which has increased exposure to other people by permitting users to keep in touch with numerous people both publicly and privately. Although social media use may not have entirely met one’s physical and psychological needs, it may have related to an increased chance of a meaningful, satisfying real life interaction through progressive closeness over online communications. A person’s perceived quality of alternatives are influenced by levels of commitment, which drives relationships even in the face of adversity; as such, a person is expected to engage in various cognitive and behavioural responses to relationship threats that reduce the threat and sustain the relationship, such as attractive alternatives (Lydon, Naidoo & Fitzsimons, 2003). If faced with an alternative, a person should have dismissed either the availability or the attractiveness of the alternative, as this devaluation is a way to protect the relationship (Lydon et al., 2003). This devaluation occurred particularly when the level of commitment is calibrated with the level of threat presented by the alternative (Lydon et al., 2003). Commitment is but one of a multitude of factors associated with perceived quality of alternatives. Attachment style is another. Avoidant-attached individuals were found to be at higher risk for alternative monitoring because they are quick to notice attractive alternatives in their environment; however, partners continually monitored relationship alternatives irrespective of whether they are in a committed relationship or not (Fletcher, 2002). This investigation examined how social media usage factors into the relationship between quality of alternatives and relationship closeness. Comparing the idealized images of potential partners to the flaws of a current partner may be at the root of relationship problems – social media can enable and intensify this dynamic (Wallace, 2007). Researchers have found that merely thinking about potential alternatives in one’s social circle reduced relationship satisfaction and commitment with the current partner (Drouin, Miller, & Dibble, 2015). Similarly, as quality of alternatives increases, there should be less of a dependence on a primary romantic partner for need fulfillment, in turn decreasing closeness (Rusbult et al., 1998). Thus, one reason that a higher quality

S. Zhao of alternatives may be linked with less relationship closeness when people use social media frequently is that social media facilitates interpersonal comparisons and increases the salience of attractive alternatives, thereby increasing the negative impact of attractive alternatives on relationship closeness. The Present Investigation While there has been abundant research on the individual relationships between social media use and quality of alternatives on relationship satisfaction and closeness, no research has yet examined how these two factors together relate to relationship processes. This study aimed to bridge this gap by examining how social media use and quality of alternatives together predict relationship closeness. It was expected that greater quality of alternatives would be related to reduced relationship closeness, but that this relationship would be moderated by social media use. Specifically, we expected that the negative relationship between quality of alternatives and relationship closeness would be stronger when social media use was more frequent. Methods Participants A total of 33 romantic couples were studied in this sample (N = 67, 36 females, 28 males, 3 others; Mage = 22.51, SDage = 3.87), recruited from the Montreal and McGill University communities. We welcomed couples of all romantic and sexual orientations to participate in the study. To have qualified for the study, couples needed to have been in this current romantic relationship for at least three months and must have each currently owned a smartphone. We recruited participants throughout the year and gathered data through the Qualtrics research software. Procedures Couples were asked to complete a series of self-report questionnaires assessing several characteristics, including social media use, quality of alternatives, and relationship closeness. This occurred as part of a much broader Couples and Technology Study, which consisted of four parts: 1) a self-report questionnaire which examined well-being, relationships, and general technology use, 2) a lab visit which examined couples’ empathic accuracy, 3) a week of daily diaries which examined how couples interact with one another in daily life, and 4) two followup questionnaires which examined the long-term


Social Media Use and Relationship Closeness relationship patterns. This investigation focused on the initial self-report questionnaire, which examined the associations between general levels of social media use, quality of alternatives, and relationship closeness. In addition, information was gathered about participants’ age, gender, and well-being, which were controlled for in the main analyses. The participants were paid $15 for completing the initial questionnaire with cash or Amazon gift cards. Measures Social media use. The Media and Technology Usage Attitudes Scale scale was used to measure social media use (Rosen, Whaling, Carrier, Cheever & Rokkum, 2013). It included items such as “How often do you use Facebook?” using a 1 (never) to 10 (all the time) scale. The current investigation focused on the frequency of use of three social media sites: Facebook (M = 6.78, SD = 1.83; see Table 1), Instagram (M = 4.2, SD = 2.79), and Snapchat (M = 4.22, SD = 2.71). Since these three sites were all highly correlated (all ps < .05, r range: 1-9), we combined them together to create a composite social media use variable (M = 5.07, SD = 1.86, α = 0.61), which was then used in our analyses. This represented our moderating variable of social media use. Quality of alternatives. The 10-item Quality of Alternatives Facet and Global Items scale (Rusbult et al., 1998) was used to create a composite quality of attractive alternatives variable to measure participants’ perceived desirability of the best available alternative to a relationship (M = 2.63, SD = 0.96; see Table 1). Questions like “The people other than my partner with whom I might become involved with are very appealing” were asked using a 1 (don’t agree at all) to 7 (agree completely) scale. This represented the independent variable of quality of alternatives. Relationship closeness. The Inclusion of the Other in the Self (IOS) scale was used to measure relationship closeness (M = 6.52, SD = .79; Aron, Aron & Smollan, 1992; see Table 1). This scale consisted of a single item assessing the degree of one’s perception of interpersonal interconnectedness as specifically applied to the romantic relationship (see Figure 1). Participants viewed a series of two increasingly overlapping circles meant to symbolize the rater’s and partner’s selves and were asked to select the pair of circles that best describes their relationship. The responses were then coded from 1

51 (separate circles) to 7 (greatest amount of overlap). The degree of overlap was indicative of the level of perceived closeness. For example, selecting two circles which did not overlap would have indicated low perceived closeness in the relationship, while choosing the two which almost entirely overlapped would have indicate high perceived closeness. This operationalized the dependent variable of relationship closeness.

Figure 1. Inclusion of the Other in the Self Scale

Well-being. We also accounted for participants’ levels of well-being, given that past research has found associations between well-being and quality of alternatives, social media use, and relationship closeness (Roberts & David, 2016). This was measured through self-esteem, subjective well-being, and relationship well-being. Self-esteem. We used the 10-item SelfEsteem Scale (Rosenberg, 1965), which featured items such as “I feel that I am a person of worth, at least on an equal basis with others” and “I am able to do things as well as most other people” using a 1 (disagree strongly) to 7 (agree strongly) scale (M = 4.66, SD = .89; see Table 1). This scale was unidimensional and featured several reverse-scored questions; overall, higher scores indicated higher self-esteem (Rosenberg, 1965). Subjective well-being. The 5-item Satisfaction with Life Scale was used to measure subjective well-being (Diener, Emmons, Larsen, & Griffin, 1985). This measured global cognitive judgement of one’s life satisfaction rather than either positive or negative affect, or satisfaction with life domains such as health or finances (Diener et al., 1985). Respondents were asked to rate such items as “so far I have gotten the important things I want in life” and “If I could live my life over, I would change almost nothing” using a 1 (disagree strongly) to 7 (agree strongly) scale (M = 4.98, SD = 1.10; see Table 1). Relationship well-being. The Short Positive Relations with Others Scale was used to measure relationship well-being (Ryff, 1989). This


52 3-item scale used the same 1 (disagree strongly) to 7 (agree strongly) scale described above. This questionnaire asked participants to rate statements such as “maintaining close relationships has been difficult and frustrating for me” and “people would describe me as a giving person, willing to share my time with others,” (M = 5.10, SD = 1.12; see Table 1). Other factors. We also collected data about age and gender, though they did not significantly alter our results.

Table 1. Descriptive statistics for social media use, quality of alternatives, relationship closeness, age, and well-being.

Data analytic procedure. We used a multilevel modeling procedure allowing social media use and quality of attractive alternatives to vary by dyad to account for the dependence within the data (participants nested within couples). We conducted a series of multilevel regressions with the statistical program R to examine the interaction between social media use and quality of alternatives predicting relationship closeness. We then conducted follow-up simple slopes analyses to understand the nature of the interaction. Results Overall correlations. We first examined the bivariate correlations among each variable of interest. The correlation between relationship closeness and social media use was not significant, r = 0.05, .95CI[- 0.19, 0.29] (see Table 2). Relationship closeness and quality of alternatives were significantly negatively correlated, r = - 0.32, .95CI[- 0.52, - 0.09]. Social media use and quality of alternatives were not

S. Zhao significantly correlated, r = - 0.04, .95CI[- 0.30, 0.20].

Table 2. Pearson’s correlations and confidence intervals between social media use, quality of alternatives, and relationship closeness.

Interactions. Using a linear mixed model, we found a marginal interaction between social media use and quality of attractive alternatives predicting relationship closeness (b = -.09, z = -1.91, p = .06; see Table 3). At mean levels of social media use, quality of attractive alternatives had a significant negative association with relationship closeness (b = -.27, z = -2.742, p = .0061; see Table 3), in line with past work. However, at low levels of social media use, the association between quality of attractive alternatives and relationship closeness was non-significant (b = -.10, z = -0.77, p = .44). In other words, having a high quality of attractive alternatives did not significantly predict relationship closeness for those who did not use social media frequently. However, at high levels of social media use, there was a significant negative association between quality of attractive alternatives and relationship closeness (b = -.44, z = -3.170, p = .0015; see Figure 2), even stronger than that seen at mean levels of social media use. Therefore, having a higher quality of attractive alternatives was more strongly related to decreased relationship closeness for those who used social media frequently. All associations held after controlling for age, gender, and well-being. These results were consistent with the hypothesis of social media use as a moderating variable of the relationship between attractive alternatives and relationship closeness. Individual social media sites. We also assessed how the individual social media sites related to relationship closeness to examine if any site showed unique associations. We found a significant interaction between quality of alternatives and Instagram use predicting closeness (b = -.08, z = -2.42, p < .05). However, the interactions between quality of alternatives and Facebook Use


Social Media Use and Relationship Closeness and Snapchat Use predicting closeness were nonsignificant (all ps > .11). We also examined the simple slopes separately for Facebook, Snapchat, and Instagram to examine consistency across sites. Significant negative associations were found between quality of alternatives and relationship closeness at both mean (b = -.28, z = -2.78, p < .01) and high (b = -.41, z = -2.98, p < .01) levels of Facebook use. The same was found at mean (b = -.22, z = -2.22, p < .05) and high (b = -.44, z = -3.54, p < .001) levels of Instagram use. The relationship between quality of alternatives and relationship closeness was positive but not significant at low levels of Instagram use (b = .004, z = .03, p = 0.98). Quality of alternatives was negatively, although non-significantly, associated with relationship closeness at low levels of Facebook use (b = -.15, z = -1.23, p = 0.22). In sum, then, results showed a highly similar pattern for all three platforms: a negative association between relationship closeness and quality of alternatives for moderate to high levels of social media use; for none of the platforms was this relationship significant when social media use was low.

Table 3. Interaction between quality of attractive alternatives and social media use indicators predicting relationship closeness

Discussion Overall, results supported the hypothesis that social media use moderates the negative association between quality of attractive alternatives and relationship closeness. Quality of attractive alternatives was found to have a significant negative association with relationship closeness, but only at moderate and high levels of social media use, whereas this association was not significant at lower levels of social media use. This was in line with

53

Figure 2. The relationship between quality of attractive alternatives and relationship closeness as moderated by social media use

past literature suggesting that social media use may be associated with a deleterious influence on the quality of romantic relationships (Fox & Moreland, 2015), though in the present study we only see this effect indirectly, such that social media use may exacerbate the negative effects of perceived quality of attractive alternatives on relationship closeness. Before considering the moderating role of social media use on the links between attractive alternatives and closeness in more depth, I first discuss the direct relationships between each of these variables. Quality of Alternatives and Closeness Consistent with previous literature (Rusbult et al., 1998), we found a significant negative correlation between quality of alternatives and closeness. This coincided with previous research which suggested that the easy accessibility of online alternative attractions deprives the primary dyadic relationships of time and emotional investments and eventually weakens the dependence of partners on their primary relationship (Abbasi & Alghamdi, 2018). Both the investment model and cohesiveness model can be used to understand this finding, in which a higher quality of alternatives lowers commitment and closeness in the investment model (Rusbult et al., 1998), and where a higher level of “alternate attractions” changes the balance of “alternate” and “present” attractions against committed relationships in the cohesiveness model (Levinger, 1965).


54 Social Media Use and Closeness We did not find that social media use was directly related to closeness, as there were non-significant negative correlations for virtually all of types and frequencies of social media usage. This could have been due to the small sample size of the present investigation. Also, because the study only accepted couples who have been together for three months or longer, these results are unlikely to be representative of all couples. Perhaps couples who have been together for less than three months would have a less solidified or committed relationship, which would then allow social media to be more strongly associated with closeness; literature and current results suggested that this association would be in a negative direction. There is another possible explanation for this lack of association: the literature suggested that intimacy mediates the relationship between online network usage and overall relationship satisfaction, whereby social network use decreased relationship satisfaction via decreased intimacy (Hand et al., 2013). A similar association may be found for social media like Facebook. In terms of intimacy, the average IOS score for the participants was 6.52 out of a maximum of 7 (see Table 1), indicating high levels of relationship closeness in this sample. The high levels of intimacy could have been associated with a weakened link between social network use and decreased relationship satisfaction. Another factor to consider is that this study’s measurement of social media use was broad, addressing only frequency; there may be specific types of social media use that are more or less detrimental. Since past literature does not conclusively link social media use and relationship closeness, this non-significant correlation does not go against any established consensus and may be attributed to a wide array of factors. Social Media Use and Quality of Alternatives Social media use was not directly associated with quality of alternatives. This went against the literature which suggested a positive correlation between social media use and quality of alternatives (Abbasi & Alghamdi, 2018). Again, our small sample could have weakened our power to detect an effect and been unrepresentative of the population of couples. That couples had to have been together for more than three months to qualify for this study may have only permitted couples with a higher level of commitment to sign up, which may be

S. Zhao negatively related to a person’s attention towards quality of alternatives – greater commitment is linked to a person’s downplaying the attractiveness and availability of alternatives (Lydon et al., 2003). Quality of alternatives is also suggested by the investment model to associate negatively with commitment (Rusbult et al., 1998). Note that the scale we administered to assess social media usage does not require users to have been interacting over social media; merely being on social media does not necessitate actively interacting with potentially attractive alternatives. Some of the research suggests that quality of alternatives becomes linked with social media use specifically through the channel of having more romantic connections (Abbasi & Alghamdi, 2018). As we had not isolated the variable of romantic connections on social media, it is possible that general use will not conform to the negative association expected with quality of alternatives. Overall, there were limitations to the scales assessing both variables, as they were self-reported and asked about behaviours which participants may have difficulty remembering correctly (like “How often do you use Facebook?”). These are many of the factors which could have contributed to the absence of a significant correlation between social media use and quality of alternatives. The Moderating Role of Social Media Use on Quality of Alternatives and Closeness For the interaction findings, our linear mixed model showed a marginal interaction between social media use and quality of alternatives predicting relationship closeness. There was a significant negative association between quality of attractive alternatives and relationship closeness at both mean and high levels of social media use, while this relation was not significant at low levels of use. Facebook, Instagram, and Snapchat all demonstrated this general trend as individual variables before they had been combined into the social media variable; the interaction was only significant for Instagram (see Table 3). These findings were in line with the hypothesized model of moderation. This negative relationship between quality of alternatives and relationship closeness could have emerged when there were higher levels of social media use because users were reminded of alternatives by seeing them on social media – research supports the role of social media as a memory primer for attractive alternatives (Drouin et al., 2014). It may have resulted from participants’


Social Media Use and Relationship Closeness outsourcing their interpersonal needs to others with whom they interact over social media, which may have a deleterious association with relationship maintenance behaviours, thereby degrading the communication and trust of a relationship; this can make a relationship more susceptible to the influence of alternatives. The investment model (Rusbult et al., 1998) may be applied to this situation: as social media is linked to relationship dissatisfaction (Elphinston & Noller, 2011), that reduces one of the factors contributing to relationship closeness and commitment; the factor of quality of alternatives then may play an expanded role in decreasing closeness when there is less satisfaction keeping the relationship worthwhile. The cohesiveness model (Levinger, 1965) can also be used to explain the results. For people who already have a high perceived quality of alternatives, using social media more could be considered more detrimental – after all, more time spent on social media is less time devoted to maintaining the relationship, not to mention that social media has been related to relationship conflicts (Abbasi & Alghamdi, 2018). In these cases, social media use may exacerbate the negative effects of perceived quality of attractive alternatives on relationship closeness. This changes the balance of relationship closeness in favour of the alternate attractions, which would be associated with less closeness overall. The investment model and cohesiveness model may both be used to conceptualize these results without contradiction. Why might the relationship between quality of alternatives and relationship closeness be insignificant at low levels of social media use? Perhaps lower levels of social media use prevent people from constantly being exposed to curated profiles of other people in a way which has been shown to foster jealousy and other negative emotions (Abbasi & Alghamdi, 2018). There might be fewer comparisons between one’s present relationship and other potential ones with less social media use. This would also leave more time for relationship maintenance behaviours like communicating about problems and insecurities, in addition to possibly spending more time doing things together in real life, thereby minimizing the impact of attractive alternatives on relationship closeness – these other options may still exist and be perceived, but they may simply hold less sway over a person emotionally. According to the investment model (Rusbult et al., 1998), more satisfaction can

55 lead to increased relationship closeness. If these relationship maintenance activities are undertaken, then the variable of satisfaction as well as investment (of time and emotional energy) can overshadow the variable attractive alternatives which can reduce closeness in a relationship. The reasons above could explain why the relationship between quality of alternatives and relationship closeness is significant only when social media use is moderate or higher. Our findings suggested that there is an association between social media use, quality of alternatives, and relationship closeness. This was further broken down into an independent relationship between quality of alternatives and relationship closeness, which was in line with the previous literature. We did not find significant correlations between social media use and either quality of alternatives or relationship closeness, contrary to what had been suggested by some of the literature, though research on this topic has been somewhat mixed. These results are vital to understanding the role of social media in moderating the relationship between quality of alternatives in relationship closeness, as platforms like Facebook and Instagram have become deeply entrenched in many people’s lives. The negative associations confirm the conventional wisdom that spending less time on social media and more time with a significant other could improve relationship closeness, in this case through possibly protecting a relationship against the power of potential alternatives. Limitations and Future Directions Several limitations are present in this investigation. Given that this study is in the early stages, the sample size was small. This runs a higher likelihood of under-estimating the magnitude of an association due to lower statistical power, as well as of overestimating the magnitude of an association from the sample producing false positive results; this makes interpretation of significance and results more difficult than it would be with a larger sample size. Sampling variability exists whenever researchers are selecting a subgroup of the population to study, so the fewer the number of people sampled, the less likely they are to conform to population estimates. A larger sample size would make it more likely for the sample of couples studied to match the characteristics of the population of couples. Many of the participants are from the McGill community, and the average age is in the early twenties. At this


56 stage of life, people are less likely to be married and have yet to go through numerous life experiences which impact their relationship dynamics (such as having a child). We hope to collect data from 200 couples by the end of data collection, including a more representative sample of older couples. Furthermore, this investigation used self-report questionnaires to assess the relationship between quality of attractive alternatives, social media use, and relationship closeness, thus making it possible for participants to inaccurately assess themselves on each domain. Self-report measures may be colored by participantsâ&#x20AC;&#x2122; desire to appear consistent or present oneself favorably (Rusbult et al., 1998). Future studies can examine a more objective measure of social media use, perhaps by assigning participants to use social media more or less frequently, or through supplementing selfreports with observational or survey data taken from those who know the participant well. In addition, longitudinal studies could help track these relationships over time and decipher directions of causality. As a part of the larger Couples and Technology study, we are collecting daily diary and longitudinal data to determine how social media use relates to relationship processes in daily life and over time. Intervention studies can also be very helpful; however, these studies must be designed carefully to ensure that experimental manipulation does not exacerbate the relationship problems (Abbasi & Alghamdi, 2018). It is important to note that the present investigation was correlational, therefore it is possible that directionality works differently from what we predicted. The link between relationship closeness and quality of alternatives may operate in the opposite direction of what our hypothesis suggests: instead of attractive alternatives possibly preceding a decrease in relationship closeness, perhaps those who perceive their relationship as less close have reason to invest in alternatives to the relationship. The association between quality of alternatives and closeness is possibly bidirectional and may be influenced by third factors which this investigation did not examine. In addition, this analysis did not account for non-linear effects, which may be informative in accounting for trends that linear effects fail to capture. For instance, the extreme lower end of social media use could mean that an individual spends more time in face to face interactions instead of socializing with others over social media, thereby

S. Zhao exposing a person to a high quality of alternatives not unlike those experienced through high social media use, thus decreasing relationship closeness. The sample, however, was not large enough to look at non-linear trends yet. These limitations present opportunities for building upon this investigation in subsequent studies. In terms of future directions, researchers may build on the results of this investigation by examining the role of social media use and relationship closeness as relating to mental health and satisfaction. Individuals differ in the degree of perceived and desired closeness in their relationships, despite experiences of closeness in romantic relationships having been associated with heightened levels of relational well-being and mental health (Frost & Forrester, 2013). Perhaps social media use is associated with levels of desired closeness, even if we had not found significant correlations between social media use and perceived closeness through the IOS. A longitudinal survey of partnered individuals has shown that optimal levels of relational wellbeing and mental health existed when individuals had minimal discrepancies between the actual and ideal IOS, regardless of their actual levels of IOS (Frost & Forrester, 2013). Social media use may be related to differing levels of discrepancies between these two measures. Individuals whose actual levels of IOS moved closer to their ideal levels over a two-year period reported improved relational wellbeing and mental health, individuals with little to no discrepancies between actual and ideal IOS were also less likely to break-up with their partners over time (Frost & Forrester, 2013). It would be interesting to build on findings from the IOS in a study like this one to see the role of social media use as related to ideal IOS, and whether that associates with relationship satisfaction when compared to the actual perceived closeness of a relationship. Examining whether this studyâ&#x20AC;&#x2122;s findings apply crossculturally would also shed light on the association between social media use, quality of alternatives, and relationship closeness. Specifically, conducting the study while controlling for cultural background to see whether couples from individualistic and collectivistic cultures display different results will provide insight about the degree to which these phenomena are universal. These findings may then be used in a clinical or educational context to help couples remain close to each other in an age of smartphones and Snapchat.


Social Media Use and Relationship Closeness Conclusion Social media allows people to connect with one another at the click of a button. Simultaneously, relationships can become less intimate as reallife conversations turn into Facebook messages, impassioned disputes are boxed into status updates, and misunderstandings are magnified in the absence of body language. This investigation sought to understand how social media relates to our perceptions of relationship closeness in response to the thought of attractive alternatives. As hypothesized, we found that with higher frequency of social media use, there is a negative relationship between quality of alternatives and relationship closeness, but this association is weaker when social media use is low. For people with many alternatives, being on social media frequently could expose them to worlds which seem much brighter, more romantic, and more beautifully filtered in comparison to the ups and downs of their unedited relationships. This can be dangerous, as it appears that for some, the illusion of connectivity has come at the cost of the real thing. Statement of Contribution This study had been conducted in the Social Interaction and Perception Lab at McGill University. Graduate student Jennifer L. Heyman designed and ran the Couples and Technology Study on which the present investigation had been built. She spearheaded participant recruitment, compiled the questionnaires which formed the basis of this study, and collected data via Qualtrics over the past two semesters. I joined the lab right before the Couples and Technology Study began running; along with several other honours students and volunteers, I assisted in the running of the experiment. For this investigation into social media, quality of alternatives and relationship closeness, I conducted the literature review, selected the variables to be examined, and wrote up the methods and discussion sessions based on available data. Jennifer L. Heyman and Dr. Lauren J. Human kindly conducted the data analyses, including constructing the graph and tables featured in the results section. Dr. David Ostry provided helpful feedback throughout the year regarding both the design of this investigation and the writing of the thesis. Special thanks go to both Jennifer L. Heyman and Dr. Lauren J. Human for their patience and insights in editing my thesis.

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58 Elphinston, R., & Noller, P. (2011). Time to Face It! Facebook Intrusion and the Implications for Romantic Jealousy and _Relationship Satisfaction. Cyberpsychology, Behavior, And Social Networking, 14(11), 631-635. http://dx.doi. org/10.1089/cyber.2010.0318 Fletcher, G. J. O. (2002). The new science of intimate relationships. Malden, MA: Blackwell. Fox, J., & Moreland, J. (2015). The dark side of social networking sites: An exploration of the relational and psychological stressors associated with Facebook use and affordances. Computers In Human Behavior, 45, 168-176. http://dx.doi. org/10.1016/j.chb.2014.11.083 Gächter, S., Starmer, C., & Tufano, F. (2015). Measuring the Closeness of Relationships: A Comprehensive Evaluation of the ‘Inclusion of the Other in the Self’ Scale. PLOS ONE, 10(6), e0129478. http://dx.doi.org/10.1371/journal. pone.0129478 Hand, M., Thomas, D., Buboltz, W., Deemer, E., & Buyanjargal, M. (2013). Facebook and Romantic Relationships: Intimacy_and Couple Satisfaction Associated with Online Social Network Use. Cyberpsychology, Behavior, And Social Networking, 16(1), 8-13. http://dx.doi.org/10.1089/ cyber.2012.0038 Kelly, H. (August, 2016). Facebook’s new feature is yet another Snapchat ripoff. Retrieved from http:// money.cnn.com/2016/08/05/technology/facebooksnapchat-video/ Levinger, G. (1965). Marital cohesiveness and dissolution: An integrative review. Journal of Marriage and the Family, 27, 19–28. doi: https://doi. org/10.2307/349801 Lydon, J. (2010). How to Forego Forbidden Fruit: The Regulation of Attractive Alternatives as a Commitment Mechanism. Social And Personality Psychology Compass, 4(8), 635-644. http://dx.doi. org/10.1111/j.1751-9004.2010.00283.x Lydon, J., Naidoo, L., & Fitzsimons, G. (2003). Devaluation Versus Enhancement of Attractive Alternatives: A Critical Test Using the Calibration Paradigm. Personality And Social Psychology Bulletin, 29(3), 349-359. http://dx.doi. org/10.1177/0146167202250202 Miller, R. (1997). Inattentive and contented: Relationship commitment and attention to alternatives. Journal Of Personality And Social Psychology, 73(4), 758-766. http://dx.doi. org/10.1037//0022-3514.73.4.758

S. Zhao Morey, J., Gentzler, A., Creasy, B., Oberhauser, A., & Westerman, D. (2013). Young adults’ use of communication technology within their romantic relationships and associations with attachment style. Computers In Human Behavior, 29(4), 1771-1778. http://dx.doi.org/10.1016/j.chb.2013.02.019 Osborn, J., Fox, J., & Warber, K. M. (2012, November). Balancing the benefits and drawbacks of social networks: A dialectical analysis of the role of Facebook in romantic relationships. Paper presented at the 98th Annual Conference of the National Communication Association, Orlando, FL Perez, S. (2014, August 11). Snapchat is now the #3 social app among millennials. Retrieved from http:// techcrunch.com/2014/08/11/snapchat-is-now-the-3social-app-among-millennials Pollet, T., Roberts, S., & Dunbar, R. (2011). Use of Social Network Sites and Instant Messaging Does Not Lead to Increased Offline Social Network Size, or to Emotionally Closer Relationships with Offline Network Members. Cyberpsychology, Behavior, And Social Networking, 14(4), 253-258._http:// dx.doi.org/10.1089/cyber.2010.0161 Problematic Use of Mobile Phones Scale: Merlo, L. J., Stone, A. M., & Bibbey, A. (2013) Ridgway, J., & Clayton, R. (2016). Instagram Unfiltered: Exploring Associations of Body Image Satisfaction, Instagram #Selfie Posting, and Negative Romantic RelationshipOutcomes. Cyberpsychology, Behavior, And Social Networking, 19(1), 2-7. http:// dx.doi.org/10.1089/cyber.2015.0433 Roberts, J., & David, M. (2016). My life has become a major distraction from my cell phone: Partner phubbing and_relationship satisfaction among romantic partners. Computers In Human Behavior, 54, 134-141._http://dx.doi.org/10.1016/j. chb.2015.07.058 Rosen, L., Whaling, K., Carrier, L., Cheever, N., & Rokkum, J. (2013). The Media and Technology Usage and Attitudes Scale: An empirical investigation. Computers In Human Behavior, 29(6), 2501-2511. http://dx.doi.org/10.1016/j.chb.2013.06.006 Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Rusbult, C., Martz, J., & Agnew, C. (1998). The Investment Model Scale: Measuring commitment level, satisfaction level, quality of alternatives, and investment size. Personal Relationships, 5(4), 357387. http://dx.doi.org/10.1111/j.1475-6811.1998. tb00177.x


Social Media Use and Relationship Closeness Ryff, C. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal Of Personality And Social Psychology, 57(6), 1069-1081. http://dx.doi. org/10.1037//0022-3514.57.6.1069 Sonja, U., Nicole, M., & Cameran, K. (2015). Snapchat Elicits More Jealousy than Facebook: A Comparison of Snapchat and Facebook Use. Cyberpsychology, Behavior, And Social Networking, 18(3), 141-146. http://dx.doi. org/10.1089/cyber.2014.0479 Stenovec, T. (2015). Facebook is now bigger than the largest country on earth. Retrieved from http:// www.huffingtonpost.com/2015/01/28/facebookbiggest-country_n_6565428.html Valentine, A. (2012). Uses and gratifications of Facebook members 35 years and older (Masterâ&#x20AC;&#x2122;s thesis). Available from ProQuest Dissertations and Theses database. (UMI No. 1511466) Wallace, G. H. (2007). Commitment in healthy relationships. The Forum for Family and Consumer Issues, 12(1) 1â&#x20AC;&#x201C;8.

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Weighing My Options: How Attentional Resources Affect Robust Averaging NIMRA ADIL Abstract On a daily basis, we must tackle the noise inherent from the environment to properly discern and identify stimuli. Our perceptual system needs to overcome this noise to discriminate between various choices and form accurate decisions under uncertainty. These perceptual decisions are accompanied by subjective feelings of confidence. Previous research posits that human decision-making is robust to outlying evidence. However, those studies did not implicate the role attention. Here, we investigate if robust averaging holds as a function of attentional resources in a dual-task paradigm. We hypothesize that the direction of the extreme value in the array will act as a salient cue and impact perceptual sensitivity and metacognition. Our behavioural analysis shows that decisions under cognitive load are not robust, i.e. they don’t down weight outlying evidence. However, contrary to our expectations we find that performance is better when the outlier is incongruent to the array mean compared to when the two are congruent. Introduction The external world is full of noisy information. Decision-makers need to overcome this noise and assess decision-relevant stimuli to form accurate perceptual decisions. The difficulty in the real world is deciding what information in the environment is important. Humans are said to make ‘near-optimal decisions, that is, the best possible decisions given the level of uncertainty in the stimulus (Summerfield & Blangero, 2017). However, this task can be challenging when one’s attention resources are divided. For example, imagine you are deep in conversation with a friend when you hear someone call your name while the room is buzzing with chatty people. In this context identifying the person having said your name poses a challenge. Perceptual decision making represents the series of processes involved in the detection,

discrimination and categorization of sensory events (Hanks & Summerfield,2016). yet discriminating between what we consider correct or incorrect becomes difficult with uncertainty and lack of attention. Decision-making typically involves two components: the objective identification of the stimuli from the environment and its subsequent subjective feeling of confidence (Koriat, 2007). The objective identification of stimuli involves integrating available evidence and grounding the decision process onto the most relevant information, a process known as robust averaging (de Gardelle & Summerfield, 2011). For example, previous research has looked at robust averaging and found participants disregard deviant expressions in a crowd of faces, outlying color values in an array of shapes and outlying evidence of symbolic numbers (Haberman & Whitney, 2010; de Gardelle & Summerfield, 2011; Vandormael, Castanon, Balaguer, Li, & Summerfield, 2017). However, it is unclear what role attention plays in robust averaging. To that end, the current study aims to investigate if robust averaging holds as a function of attentional resources in a dual-task paradigm. Perceptual Decision-Making Our perceptual system is tasked with overcoming the noise inherent to sensory events to successfully analyze the multitude of stimuli available (Rahnev, 2017; Yeung & Summerfield, 2012; Boldt, de Gardelle, & Yeung, 2017). There are competing signals of varying intensity that need to be processed, yet detailed processing is only possible for a few objects at a time (Peelen & Kastner, 2014; Rahnev, 2017). In the process of ‘robust averaging’ individuals exclude unreliable data to overcome uncertainty (de Gardelle & Summerfield, 2011). One experimental strategy consists of exploring this process by presenting participants with a multielement array of targets where they are required to indicate the average value on a feature dimension.


Weighing My Options In this regard, research shows that observers based their choices more on inlying evidence i.e. evidence that falls close to the array mean, compared to outlying evidence i.e. evidence that falls at the extremes, far from the array mean (de Gardelle & Summerfield, 2011). This result entails that outlying evidence is down weighted or excluded completely by participants. However, salient objects regardless of their relevance have been found to dominate selection in a visual scene because they differ significantly from the neighboring object (Treisman & Sato, 1990). The saliency of objects in our environment can be manipulated using two orthogonal perceptual factors: signal strength and signal reliability (de Gardelle & Summerfield, 2011; Boldt, de Gardelle, & Yeung, 2017; Summerfield & Tsetsos, 2015: Zylberberg, Roelfsema, & Sigman, 2014). Signal reliability corresponds to the variance of the evidence whereby greater variance reduces the reliability. Signal strength represents the quantity of the evidence and a wider average orientation improves the signal conveying more evidence (Example Figure 1.1). Thus, signal strength and reliability are two moderators that influence our decision and confidence under uncertain environments (de Gardelle & Summerfield, 2011; de Gardelle & Mammassian, 2015; Summerfield & Tsetsos, 2015; Zylberberg, Roelfsema, Sigman, 2014).

Metacognition ‘Metacognition’ is our ability to have insight into our own thinking (Fleming, 2014). Humans have a unique skill to introspect our own thoughts and actions, thereby allowing us to adjust our performance accordingly. Classic models of subjective confidence state that metacognitive judgements are based on the difference in favor of each choice available, however, evidence suggests that subjects form decisions solely on the evidence for the selected choice and are blind to evidence

61 for the non-selected choice (Zylberberg, Barttfeld, & Sigman, 2012). Rahnev (2017) posits that confidence ratings directly reflect an individual’s feeling of uncertainty about perceptual judgements for a single stimulus. Sometimes, we can accurately form subjective confidence evaluations of the perceptual content we saw, this is known as ‘metacognitive accuracy’ which depends on postdecision processing (Yeung and Summerfield 2012). If we can correctly judge our decision, then one can be said to have high metacognitive accuracy (Sherman, 2016). Confidence judgments reflect the subjective belief that a correct decision has been made or not (Sherman 2016). Usually, the accuracy of metacognitive judgments correlate with decision accuracy (Grimaldi, Lau & Basso 2015). However, when we factor in uncertainty, one might become over- or under- confident even when conditions are matched for accuracy (Rahnev, 2017). The sensitivity of confidence judgments is the degree to which people can distinguish between their correct and incorrect responses, leading to greater subjective confidence which predicts greater objective accuracy (Boldt, de Gardelle, & Yeung, 2017). Recent findings show that this self-assessment of our mental representation of our decision is based on more than just signal strength, as individuals are more sensitive to signal reliability when faced with multiple sources of information (Yeung & Summerfield, 2012; Boldt et al., 2017). In particular, signal reliability appears to influence ‘metacognitive readout’ which is our ability to map subjectively experienced certainty to expressed confidence (Boldt, Yeung & Gardelle, 2017). The Role of Attention Attention can be mediated through automatic or voluntary control. Within this framework, seminal work showcases the sensitivity of voluntary attention to secondary task, such as working memory task (Jonides, 1981). Human information processing is limited in capacity (Yang, 2017; Broadbent, 1965). Selective attention thereby allows us to amplify decision-relevant objects while ignoring irrelevant information, as attention acts as a processing filter in tune with our behavioural goals through which less salient information is attenuated (Gazzaley & Nobre 2011;Yang, 2017; de Gardelle & Kouider, 2009). Importantly, attention leads to improved performance by boosting the sensory signal and dampening the


62 surrounding (Cohen, Cavanagh, Chun & Nakayama, 2012, Knudsen, 2007). Therefore, attending to stimuli improves discriminability and decision bias (Rahnev et al., 2011; Lu & Dosher, 1998). To that end, we aim to explore how outlying evidence acts as a salient cue that grabs our attention and creates a decision bias favoring that decision choice in a dualtask paradigm. Dual Task Paradigm A highly contested issue in the literature is the relationship between attention and consciousness. Some authors say that attention and awareness are intimately linked ( Mack & Rock, 1998; Dux & Marois, 2009; Cohen, Cavanagh, Chun, & Nakayama 2012), whereas, others believe they are fully dissociable (Cohen, Cavanagh, Chun, & Nakayama 2012; Lamme, 2010). Dual-task represents one attentional paradigm that proposes that attention and consciousness are related. Cohen (2012) describes a dual-task paradigm involving two tasks that are performed separately and then concurrently, with their respective attentional requirements measured by looking at the difference between performance on the single- and dual task settings. Top-down attention is under the subject’s volitional control (Theeuwes, 2010). As Gardelle and Summerfield (2011) showed people base their decisions on inlying perceptual evidence. Top-down attention allows one to do this as it improves quality of information by adjusting the signal-to-noise ratio for relevant targets by deeming which features are to be prioritized (Sherman, 2016; Knudsen, 2007). It is known that signal strength and signal reliability affect perceptual decision making and metacognition when top-down attentional resources are unavailable. However, it is not known how robust averaging takes place in a dual-task where attentional resources are lacking. Therefore, we utilized a dual-task paradigm to see the effect of cognitive load on the objective and subjective components of decision making. Signal Detection Theory To look at the relationship between decision accuracy and decision confidence, one can apply Signal Detection Theory (SDT). SDT allows us to evaluate confidence judgements simultaneously with discrimination decisions (Mamassian, 2016), based on type 1 response which correspond to the discrimination performance and the Type 2 task which is the subjective confidence judgement

N. Adil (Maniscalco and Lau, 2012). SDT allows the separation of the independent contributions of sensitivity and response bias in type 2 response (Maniscalco and Lau, 2012). SDT construe decision processes via two components: Perceptual discrimination (d’) and a cognitive strategy (C; Macmillan & Creelman, 2004; Maniscalco & Lau, 2014; Rahnev, 2011; Mamassian, 2016). Likewise, SDT views metacognition through separate components: Meta-d’, which reflect an estimation of metacognitive sensitivity, and the type 2 criteria (i.e., there are two criteria), which reflect metacognitive bias (Maniscalco and Lau, 2014). This analytic approach therefore provides a reliable strategy to explore how perception and metacognition vary as a function of dual-task performance. Hypotheses The current research proposes to explore robust averaging when attentional resources are limited. To this end, we will concurrently use a target discrimination task and an attentional load task manipulation by asking participants to attend to a letter appearing at the fixation point and a cluster of Gabor targets at the periphery. In the dual-task condition, participants will be required to identify the target and indicate the average orientation of the Gabor targets. Critically, we will manipulate signal strength (i.e., the average orientation) and signal reliability (i.e., the variance of the orientation) of the targets. In line with the purpose of the current research, we plan to examine how the most extreme outlier value of the cluster of Gabor targets influence perceptual decisions and metacognitive judgments processes. In particular, our strategy will rest on evaluating conditions where the outlier is congruent versus incongruent with the average orientation. Robust averaging predicts that the direction of the most outlier item should not influence perceptual and metacognitive decisions. Moreover, in the case where the outlier influences perception and metacognition, we expect that this influence will be greater when attentional resources are limited. Methods Participants 32 participants were recruited using SONA – psychology participant pool at McGill University. They were healthy, undergraduate students with normal or corrected-to-normal vision, who participated in the study for course credit. Each


Weighing My Options participant completed the high-load and no-load condition in a two-hour lab session for two course credits. All participants were between the ages of 18 and 35 (M=20.5, SD=1.146, N = 33 (Female = 29, Male = 4). The experiment was approved by the McGill University ethics committee. Apparatus & Stimuli The study included 4 blocks of 160 trials per each condition, which were presented on a 17.5-in (40. 64 cm) monitor CRT (ViewSonic Graphics Series G90fB) from an approximate distance of 60 cm. To control the amount of light in the room, it was darkened with only a lamp as the light sources, which was placed at the back of the desk. Two participants were tested at a time with computers placed at extremities of the room. To record their responses, they were asked to use the F and J keys on a keyboard, with F indicating left using the left finger and J indicating right using the right finger. The forced-choice options for the objective and subjective questions were counterbalanced. The direction of the perceptual judgment of Gabor stimuli and forced-choice confidence response keys were counterbalanced across participants. Stimuli which were generated using the Psychophysics toolbox for MATLAB (version 2015a), were presented at the center of the visual field of the computer whose screen was grayscale and uniform. T or L were presented at the location of the fixation point and six Gabor targets surrounded the letter that appears at the center. The orientation for the mean average of the Gabor stimuli varied between 5º and 10º, while the signal variance ranged from SD = 10 and 20. The Gabors were presented at a contrast of 100%. Design The experiment implemented a dual-task paradigm which included a target discrimination task and a visual search task. The critical task was to report the average orientation, whether left or right, of the six Gabor targets presented, followed by its confidence judgement. The load manipulation was whether the letter T was present or absent in the presence of distractors (letter L). There were four conditions formed by the manipulation of signal strength and signal reliability (Figure 1.2). Attentional resources were manipulated in two conditions: High Load vs No Load. Both these manipulations of perceptual factors and load were done within subjects. The participants had to attend to both a letter and Gabor

63 stimuli which were presented simultaneously in the high load condition. The Gabor targets were manipulated in two distinct ways. First, signal strength was reduced by using orientations that are on average closer to 5 degrees rather than clear angles of 10 degrees. Secondly, by increasing the signal variance i.e. making the evidence less reliable, by using orientations that are a mix of leftward angles that range from 5º and 30º, and angle that are rightward that vary from 5º and 30º). Stimulus mean, and stimulus variance parameters of our experimental stimuli were controlled.

Procedure On arrival, participants were instructed orally as to what their task was. They were also asked to wear noise-cancelling ear muffs. The two load conditions were done in succession and were approximately 60 minutes long. There were ten practice trials at the start of each condition. Participants completed four blocks in total (counterbalanced) culminating to 1280 trials in total (640 trials per conditions and 160 trials per block). At the onset of the trial in each condition, a fixation point was presented at the center for 750 milliseconds which changed into a letter which rotated at random (0º, 90 º, 180º, or 360º). Concurrently, an array of six Gabor targets appeared oriented either towards the left or right. The time for which the letter and the Gabor targets remained on-screen was 750 milliseconds and the subsequent questions stayed on the screen till a response was recorded. In the High Load condition, participants were presented with the letter stimuli and Gabor targets simultaneously. On each trial they had the three forced-choice questions: in red they were asked what the average cluster orientation of the Gabor’s was (Left or Right) and how confident they were (Confident or


64 Not Confident) and in blue whether the letter was present or absent. The No Load condition differed as they were no longer required to attend to both the Gabor targets and letter stimuli because there was no subsequent question on the presence or absence of the letter. They were only asked in red what the average cluster orientation of the Gabor’s was and their confidence in that decision. Participants indicated their responses using F with their left index finger and J with their right index finger. They were instructed on the second condition once they finished the first. Variables Based on the purpose of the current research, we looked at how dual-task manipulation (single versus dual task) and the direction of the most outlier item relative to the average of the cluster (congruent versus incongruent) alter perception and metacognition.

Figure 1.3.Image (A) represents the high load condition. Participants viewed a fixation point followed by a letter-either an ‘L’ or a ‘T’ placed at the fixation point at either 0o, 90o, 180o, or 260o. Simultaneously, participants viewed an array of six Gabor patches oriented towards the right and/or left. Three qustions were asked after the presentation of the Gabor stimuli ­‑ the average orientation of the Gabor stimuli, followed by a confidence rating and lastly the attentional load (was the letter ‘T’/’L’; absent or present). Image (B) represents the ‘no load’ condition. Participants viewed the same stimuli, but they were asked only two questions average orientation of the Gabor stimuli and confidence judgment.

Analysis Strategy We used hierarchical regression models to analyze the data where we included dual-task manipulation (single versus dual-task) and direction of outlier (congruent versus incongruent) as fixed factors in a stepwise fashion, while subjects were included as random factors. This analytical strategy was applied to reaction time, type 1 perceptual sensitivity (d’) and the decision Criterion (c), as well as metacognitive

N. Adil accuracy. We evaluated the goodness-of-fit using chi-square test. Results Perceptual Accuracy We applied hierarchical logistic regression model to assess perceptual accuracy following the aforementioned step-wise procedure (see Figure 2). Here, the most reliable model included main effects of dual-task (Beta = -.23, SE = .035, 95% CI [-.30 -.16] ), outlier congruency (Beta = - .59, SE = .032, 95% CI [-.65 -.52]), as well as their interaction (Beta = .14, SE = .046, 95% CI [.05 .23]) , X2(1) = 9.299, p < .01. This primary analysis confirms that the congruency of the outlier influences perceptual decisions, and that this effect varies as a function of attentional resources. Unexpectedly however, participants were better in the incongruent condition. We will discuss this unexpected result in the discussion section. Metacognitive Accuracy We applied the very same strategy for metacognitive accuracy (see Figure 2). Here, the best model includes main effects of dual task (Beta = -.10, SE = .022, 95% CI [-.14 -.06]) and congruency of the outlier Beta = -.10, SE = .022, 95% CI [-.32 -.24]), X2(1) = 163.61, p < .001. Critically, the model involving the three-way interaction was not reliable, X2(1) = 2.38, p = .12.

Figure 2. Perceptual Accuracy and Metacognitive Accuracy as a function of attentional load and congruency of outlier.

Perceptual sensitivity Perceptual sensitivity (see Figure 3) was calculated for each participant across all relevant parameters according to the following equation: Z(hit) – Z(false alarm). We then evaluated the best fitting model using hierarchical linear regression model. Again, the best model included dual task (Beta = -.21, SE = -.66, 95% CI [-.34 -.07], and congruency of outlier, (Beta = -.66, SE = .068, 95%


Weighing My Options CI [-.80 -.53], X2(1) =65.049, p < .001. Decision Criterion The decision criterion (see Figure 3) was calculated for each participant across all relevant parameters according to the following equation: -0.5 * (Z(hit) + Z(false alarm)). We then evaluated the best fitting model using hierarchical linear regression model. Here the best model solely included dual task (Beta = -.08, SE = .02, 95% CI [-.13 -.023], X2(1) =7.29, p < .01.

Figure 3. Perceptual Sensitivity and Cognitive Bias as a function of attentional load and congruency of outlier.

Discussion Perceptual decisions are clouded with uncertainty, making us doubt our decisions and their chances of being correct or in line with our goals. We wanted to explore what role attention plays in perceptual decisions and metacognition by specifically looking at robust averaging. Overall, our aim was to investigate whether robust averaging would hold as a function of attention when individuals are faced with multiple noisy stimuli. We expected to find that in the single task, i.e. when attentional resources are fully available, robust averaging would be present as it has for previous research. However, in the absence of attentional resources in the dual task, we expected to find the direction of the extreme evidence to act as a salient cue that would affects the average direction of the array reported. This prediction would be reflected as reduced performance for incongruent outliers. Here, we provide evidence against robust averaging as outlying evidence is not discarded. The outlying evidence acts as a salient cue for the average direction. Indeed, results show that the congruency of the outlier with respect to correct response alters perceptual accuracy and sensitivity, while a similar pattern emerges for metacognitive accuracy. However, contrary to our expectation, performance was actually better when the direction of the extreme value is incongruent with the

65 average of the array. In sum, we have evidence that perceptual decision-making is not robust against the extreme, yet, our finding did not go in the expected direction. Our expected findings were in line with previous research that suggests that an object in the visual field that differs significantly from its neighboring objects would be a salient, exogenous cue. For perceptual accuracy, we found an interaction as a function of load and congruency of outlier. A main effect of load and congruency of outlier on Dâ&#x20AC;&#x2122;prime and Meta Accuracy. For decision criterion, we found a main effect of load showing a response bias. However, we found that the when the direction of the extreme value and the actual average of the cluster was congruent, people performed worse. Our findings clearly challenges the idea that participants drop or down weight outlying evidence, as per the robust averaging hypothesis (de Gardelle & Summerfield, 2011). One may therefore speculate about whether robust averaging generalizes to all tasks. Indeed, original findings found this pattern in tasks involving color and shapes. However, recent assays did replicate this finding using Gabor targets (Li et al., 2017). A more likely explanation is that the current experiment rests on different sigma values for the variance of Gabor targets. Indeed, out task comprises more noise. Our results therefore potentially highlight some of the limitations of robust averaging, whereby greater noise or extreme values possess greater influence over perceptual decisions. Robust averaging is strategically used by people to maximize their accuracy on a task when faced with noise, despite it discarding decision information, it provides a benefit (Li, 2017). Our findings build on previous research of robust averaging, by incorporating a dual task paradigm load. Our expectations were that robust averaging would not be adopted as an optimal strategy to discard outlying evidence when faced with noisy stimuli under limited attentional resources. Participants would use the salient cue to form decisions under load. The outlying evidence i.e. the direction of the extreme value, in the array acts as a cue which biases the subsequent decisions formed. However, our results do not satisfy our expectations. There seems to be something else as at play that affects participant performance when faced with extreme evidence congruent to the expected direction of the cluster average. One possibility is that incongruent


66 outliers serve as a salient stimulus marking a clear distinction with the average pattern, thereby indicating the mean direction of the Gabor targets through a sharp contrast. In that sense, participants would be able to use incongruent outliers to determine the average orientation of the global pattern. Conversely, congruent outliers would not be as salient, as they are going in the same direction as the global pattern. Overall, this construal would require a further investigation, but seems plausible. Especially given that we found the same pattern for metacognitive accuracy. As attentional resources are divided between the letter as the central cue and the array of six Gabor’s that surround it, the outlying evidence should stand out when the signal is weak i.e. the noise is high. However, we did not find an interaction between load, congruency of outlier and signal for d’. As per, the exogenous attention system which posits that external stimulation drives our attention (Chica, 2013), we expected to find that participants, in order to attend to both stimuli, would end up being more sensitive to the outlier in the array. They did seem to show sensitivity to the outlier but not in the fashion we expected. It is known that in the absence of attention, we tend to overestimate our perceptual sensitivity, whereby unattended stimuli may be perceived with a strong sense of detail and vividness (Rahnev, Maniscalco, Graves, Huang, de Lange, Lau, 2011). Our findings are complementary to this idea yet, being sensitive to the outlier did not aid the participants to perform better under congruent conditions. Being biased to the outlier may seem counterintuitive when asked for the average of the array, but it makes sense under the load manipulation as the participant is trying to dampen the noise in the stimuli by looking for cues in the visual scene. The current research paradigm allowed us to explore the interaction between load and the two perceptual factors i.e. Signal Strength and Signal Reliability, orthogonally. Compared to robust averaging, which has been found to down weight outlying evidence, our research posited that the outlier would act as a salient cue aiding the decisionmaking process. This is more intuitive as real-world decisions are affected by salient environmental cues. Furthermore, we explored the role of attention using a dual-task paradigm. This strategy allowed us to manipulate attention and other perceptual variables compared to the commonly used Posner paradigm (Posner, 1994) which is quite difficult

N. Adil to accomplish when studying robust averaging, as our pilot experiments showed. When using Posner cueing combined with a dual-task strategy, our pilot study demonstrated that performance was quite low, possibly because targets only take up half the screen. Our current task allowed the targets to be on the entire screen. To build on the corpus of literature on attention and consciousness, we analyzed type 1 (decision discrimination/objective) and type 2 (subjective) performance. The current study despite its strengths in using a dual-task paradigm and robust perceptual factors such as signal strength and signal reliability has its limitations. Firstly, in a given session, the participants did high load and low load successively (i.e. high load followed by low load or vice versa), this could affect their performance in either condition as repetition is known to positively or negativity impact performance. Secondly, the reaction time recorded is not at the onset of the stimuli but after a blank screen presented for 150 milliseconds. The stimuli are presented simultaneously but they require separate responses which are recorded after a time delay. Furthermore, the questions are a forcedchoice response. This works well for the cluster average value, but for the subjective response forces individuals to choose between confident and nonconfident, when they might not be either. It would be interesting to see the results on a confidence scale that goes from confident to non-confident. In addition, we only look at prospective metacognitive judgments which is a second-order response based on the objective response that precedes it. Analyzing retrospective metacognition could be of interest as it would show the participants subjective evaluation of the stimuli in isolation. Lastly, we used Signal Detection Theory, a robust measure to tease apart the components of decision-making, however, this is only descriptive and doesn’t inform us about the mechanisms employed by the individual. Lastly, as we can’t currently explain why our results were contrary to our expectations, replication of the current study needs to be done to analyze what factors contributed to the effects found. Moving forward, our current results allow us to look at robust averaging across varying levels of attention. Our research design included two conditions, however, in the future the implication of three conditions in the form of no load, low load (where the letters would be P and Q) and high load would be interesting to analyze. This would allow


Weighing My Options us to investigate further the role of robust averaging across attentional resources. Moreover, as our research was purely behavioural, the underlying neuronal mechanisms are missing. Thus, in the future, neuroimaging studies can be used to explore how robust averaging differs neuronally across tasks. To conclude, we demonstrate that robust averaging is not sustained across varying levels of attention. Although the results are not what we expected, they require further investigation. At the perceptual and metacognitive level, the incongruent condition lead to better performance whereas the congruent condition was worse off. The load manipulation interacts with congruency to affect reaction time which reflects uncertainty. Overall, the present findings reveal another facet of perceptual decision-making relating to the real world where decisions formed are not optimal but based on the cues present. Appendix Sample size: A total of eight participants were excluded from the final sample. The adjusted sample size is of thirty-three participants. Of the eight participants, one was excluded because they stopped the task half way. Four were excluded for pressing the wrong keys in more than 10% of the trials. One participant was excluded for pressing the wrong keys in more than 15% of the trials. All eight participants had less than 50% accuracy rate. Statement of Contribution The current research is part of a larger research project on attention and consciousness. The study was designed by Mathieu Landry who was my direct supervisor in the project. I worked on this project with another undergraduate research student. Both of us conducted testing using SONA Participant Pool. Mathieu and Jason Da Silva Castanheira (research assistant at the lab) taught me how to clean the raw data, analyze it and helped create the figures for this thesis. They were instrumental in teaching me the inner workings of research. The thesis was written entirely by me, with editorial help from Mathieu and Jason. References Boldt, A., de Gardelle, V., & Yeung, N. (2017). The impact of evidence reliability on sensitivity and bias in decision confidence. Journal of Experimental

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Attentional Bias to Threat in Offspring of Alcoholics: An Examination of a Shared Etiology in Alcohol Use Disorders and Anxiety Disorders MICAELA WISEMAN There is a wealth of research demonstrating high rates of comorbidity between alcohol use disorders and anxiety disorders (Regier et al., 1990, Kessler et al., 1997, Grant et al., 2004). A meta-analysis calculated an odds ratio characterizing the comorbidity between these disorders ranging between 2.1 and 3.3. This means that the two conditions occurred together 2-3 times more often than would be predicted by chance alone (Smith & Randall, 2012). Over 85% of long-term abstinent alcoholics have a lifetime psychiatric diagnosis, which was a significantly higher rate than was found in normal controls (Di Sclafani et al., 2007). Additionally, there is evidence to suggest an expressly unique relationship between Alcohol Use Disorder (AUD) and anxiety disorders. For example, in a study looking at the prevalence of substance use problems and comorbid psychiatric syndromes, anxiety was the most rampant comorbid disorder (Mericle et al., 2012). As well, a review of epidemiological studies found an approximate doubling to quadrupling of a risk for alcohol dependence given a diagnosis of an anxiety disorder (Di Sclafani, Finn, & Fein, 2007). Alcohol use has also been demonstrated to correlate with anxiety symptom severity (Delgadillo et al., 2013). Understanding comorbidity is incredibly valuable as it helps to better understand the source of a patient’s myriad of symptoms and can help to inform treatment decisions. There is evidence to suggest that comorbid disorders encounter a discrepant clinical outcome than one disorder alone (Carey, Carey, & Meisler, 1991). Treatment might be improved by understanding how the symptoms of one disorder may contribute to the prognosis of the other (Lynskey, 1998). What is more, efforts at prevention can be reconfigured to incorporate the tactics by which two disorders emanate together, rather than targeting one ailment in isolation. The attempts to explain the disproportionate level of co-occurrence of the two disorders fall within three general themes. The first being that

anxiety symptoms lead individuals to self-medicate with alcohol (George et al., 1990). The second commonly invoked explanation is that high dose alcohol consumption, and subsequent withdrawal, interacts with the brain in ways that induce anxiety symptoms (Quitkin et al., 1972). Finally, it has been proposed that there may be a common factor that underlies the development of both anxiety and AUD (Merikangas, 1998). The present study aims to expound on the literature surrounding the common factor model by examining familial transmission of the disorders. Marquenie and colleagues (2007) assessed the relative merit of the three theories by looking at the temporal onset of the disorders, as well as parental transmission. The authors found that in two thirds of the cases examined, anxiety precedes alcoholism. This suggests that the presence of an anxiety disorder increases one’s risk of becoming alcohol dependent, but not the other way around. This result can be interpreted as confirming both the self-medication hypothesis, as well as the common factor model. The authors also found that subjects with “pure” (non-comorbid) anxiety disorders were more likely to have a parent with both comorbid alcoholism and anxiety or a parent with “pure” AUD, contrasting healthy controls. However, patients with “pure” AUD were not significantly more likely to have parents with “pure” anxiety relative to controls. Despite the evidence that alcoholism is less likely to precede anxiety within the individual, parents with AUD are significantly more likely to have children with “pure” anxiety. Hence, comorbid anxiety in AUD is unlikely due to a causal role of alcohol given the time course of the disorders. Taken together, this may indicate the presence of a shared basis for the two disorders that parents pass on to their children. The aforementioned data on familial transmission does not preclude the possibility that anxiety disorders in offspring are due to a stressful environment created by parental drinking. However,


Attentional Bias to Threat in Offspring of AUD there is evidence to suggest that parents with AUD can transmit anxiety to their offspring without exerting an environmental influence. A relevant study looked at the effects of paternal alcoholism and found that offspring showed internalizing behaviour at as early as 18 months, relative to children with healthy parents (Edwards, Eiden, & Leonard, 2006). These early differences may hint at an innate predisposition for internalizing. Another study looked at subjects with grandparents who suffered from AUD. These individuals were twice as likely to experience an emotional disorder when controlling for comorbid emotional disorders in the grandparent group (Leventhal, Pettit, & Lewinsohn, 2011). Grandparents serve as an interesting experimental group because they will transmit a genetic influence without necessarily impacting oneâ&#x20AC;&#x2122;s environment as much as a parent would. However, it should be considered that the parents in the study were also more likely to have AUD. The adoption study by Kendler et al. (2015) looked at traits of not-lived-with biological parents and step-parents as predictors of AUD. They found that psychiatric illness in not-lived-with parents, but not in step-parents, was a predictor for AUD in offspring. However, this study did not isolate for the role of anxiety alone. An earlier twin study attempting to disentangle the role of environment in comorbid anxiety and AUD found that the correlation between alcoholism and anxiety could largely be explained by the heritability rates determined for the two traits by comparing monozygotic and dizygotic twins (Tambs et al., 1997). Recent bivariate linkage studies have even looked at genetic loci that jointly influence the comorbid disorders. Hodgeson et al., (2016) isolated significant bivariant linkage peaks at 9q33.2 â&#x20AC;&#x201C; 9.33.1 with a genetic correlation ranging from 0.55 â&#x20AC;&#x201C; 0.66. Hence, the cross transmission of AUD and anxiety disorders may have biological underpinnings independent from environmental factors. Additionally, research into the neurobiological mechanisms of the two disorders have uncovered overlapping phenomena. Corticotropin releasing factor (CRF) is an important component of the stress response as it activates the hypothalamic pituitary thyroid axis (HPA). CRF has also been demonstrated to impact the behavioural response to stress (George F Koob & Heinrichs, 1999). Hence, it has been looked at extensively in the context of anxiety disorders

71 (Arborelius, Owens, Plotsky, & Nemeroff, 1999; Risbrough & Stein, 2006). Benzodiazepines, which show clinically significant effects on anxiety, have been demonstrated to decrease CRF concentrations (Owens, Vargas, Knight, & Nemeroff, 1991). Thus, it has been proposed that the efficacy of these drugs may be partly attributable to their interaction with CRF. As well, animal research suggests that CRF is more relevant in sustained responses to stress, rather than acute fear responses, which more closely aligns with concepts of human psychopathology (Walker, Miles, & Davis, 2009). Recent research by Natividad et al. (2017) looked at the effects of innate overexpression of CRF receptors in rats and found augmented displays of anxious behaviours. CRF has also been considered in AUD research. G. F. Koob (2003) reviewed the literature on the neurotransmitters involved in alcoholism and proposed that an increase in CRF, as well as decrease in neuropeptide Y, are responsible. Accordingly, inhibition of the CRF receptor CRH1R through pharmacological agents reduced the alcohol consumption of rats (Cippitelli et al., 2012). The same receptor was blocked in mice who were conditioned to become alcohol dependent and they too attenuated their intake (Lowery-Gionta et al., 2012). Protein kinase C epsilon regulates CRF activity in the amygdala and Lesscher et al. (2008;2009) looked at its role in both anxiety disorders and AUD. They demonstrated that mutant mice lacking PKCe, show reduced anxiety-like behaviour as well as less CRF activity in the amygdala. The researchers also locally enhanced PKCe activity which stimulated amygdala CRF. Regarding alcohol consumption, they found that PKCe reduction in the amygdala diminished alcohol ingestion and preference in mice. In addition, they observed that mice which are heterozygous for the PKCe allele have less than double the amount of amygdala PKCe than wildtype mice and also consume less alcohol. Thus, PKCe and CRF signalling may serve as an example of a genetic alteration that contributes to both AUD and anxiety disorders. Structural investigations into the brains of anxiety and AUD patients also demonstrate commonalities. A study looking at grey matter volume of various brain regions in alcohol dependent subjects found significant reductions in amygdala, hippocampal and ventral striatal volumes relative to controls. However, only amygdala


72 volume was associated with subsequent craving and alcohol consumption (Wrase et al., 2008). Reductions in amygdala volume have also been found in both adolescent and young adults who were deemed “at risk” for alcoholism based on their family histories. (Hill et al., 2001) These volumetric abnormalities also appear in at-risk subjects who are completely alcohol naïve (Benegal, Antony, Venkatasubramanian, & Jayakumar, 2007). Dager et al., (2015) examined whether amygdala volumes are indicators of a genetic vulnerability to AUD. They found reduced volumes in AUD patients who were both recovered and currently suffering, as well as reductions in relatives of AUD patients. The authors also performed statistical analyses and found that amygdala volume was genetically correlated with AUD risk. They concluded the possibility of a shared genetic mechanism for both amygdala volume and AUD. Amygdala volumes show deficits in the realm of anxiety disorders as well. MRI research with panic disorder (PD) patients found atrophy in the amygdala (Massana et al., 2003) and a smaller amygdala in PD was positively correlated with state anxiety (Hayano et al., 2009). Pediatric anxiety patients are also associated with volumetric amygdala reductions (Milham et al., 2005) as well as adults with generalized social anxiety (Irle et al., 2010). Studies looking at autistic children found that amygdala volume differentiated subjects with and without anxiety, where anxious subjects saw debits (Herrington et al., 2017). Similarly to AUD, it has been suggested that amygdala volume could serve as a genetic risk factor for anxiety disorders. Pairs of monozygotic twins in which only one twin suffers from an anxiety disorder had symmetrical amygdala reductions, which discriminated them from healthy controls (Alemany et al., 2013). The premise that amygdala volume is implicated in the vulnerability for both disorders, provides further substantiation for a common etiology among AUD and anxiety disorders. If AUD and anxiety share an underlying causal component, then offspring of alcoholics should be demonstrably more vulnerable to an anxiety disorder. Thus, it is important to inspect prospective signs for later anxiety. Attentional bias paradigms have been extensively studied in attempts to isolate individuals at risk for developing anxiety disorders (Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van, 2007). Specifically,

M. Wiseman anxious patients tend to differentially allocate their attention towards threatening stimuli when compared to healthy controls. Attention is usually “facilitated” at the immediate presentation of a stimulus, whereby one orients towards threatening stimuli disproportionately faster than towards a neutral stimulus (Cisler & Koster, 2010). This initial response may be followed by a difficulty to disengage from the stimulus (E. H. Koster, Crombez, Verschuere, & De Houwer, 2004; Salemink, van den Hout, & Kindt, 2007) or avoidance of the source of threat (Garner, Mogg, & Bradley, 2006; E. H. W. Koster, Crombez, Van Damme, Verschuere, & De Houwer, 2005; Mogg, Bradley, Miles, & Dixon, 2004). For example, in emotional Stroop tasks, anxious individuals will be delayed in naming the colour of a printed word when that word carries a threatening connotation, because subjects will tune in to the semantic value (Bar-Haim et al., 2007). In a more robust task, the dot probe paradigm, participants are presented with a probe shortly after viewing a threatening or neutral face. This probe will either appear in the same location that the face had been, or in an adjacent location. A bias in attention is measured based on the reaction times to the probe when it appears in a congruent location to the threatening face relative to an incongruent location. If subjects consistently respond faster to a cue that replaces a threat, it denotes that on incongruent trials, their attention was unevenly allocated towards the location of the threat. This response bias should be more pronounced on trials where they must disengage from a threatening stimulus rather than a neutral one (MacLeod, Mathews, & Tata, 1986). Bar-Haim et al.,’s (2007) meta-analysis found a significant effect (d=0.37, CI = 0.28,0.46) of a threat-related bias on the dot probe task which only appeared in anxious individuals. The divergent reactions to the stimulus depending on the time course of presentation reflect different neural operations. Specifically, the amygdala is assumed to underlie the initial vigilance that causes one to attend more readily to the presence of threat (Cisler & Koster, 2010). In dot-probe experiments in which the threatening stimulus is presented below conscious awareness, the amygdala shows increased activation (Carlson, Reinke, & Habib, 2009). As well, displayed attentional biases to threat have been shown to predict amygdala reactivity during an fMRI scan (van den Heuvel et al., 2005).


Attentional Bias to Threat in Offspring of AUD Pishnamazi et al. (2016) looked at a patient with developmental bilateral amygdala damage and tested her performance on an attentional bias task. The patient showed an earlier orientation towards a fearful face relative to healthy controls. Therefore, the between-subjects variance in reactions to threatening stimuli in anxious and non-anxious individuals may reflect neurological idiosyncrasies relating to the amygdala’s threat detection system. These biases have also been demonstrated in children of parents with anxiety disorders who have a high risk of developing these disorders themselves. Children of mothers with anxiety disorders were assessed using a dot-probe task and preferentially oriented towards angry stimuli in relation to both happy and neutral faces. This bias was not observed in children of non-anxious mothers (Montagner et al., 2016). Children of PD patients also showed skewed responses towards health-related threats – a pattern that did not emerge in daughters of healthy mothers (Mogg, Wilson, Hayward, Cunning, & Bradley, 2012). Notably, it has been discovered that children’s reaction times are not only dependent on their parents’ psychiatric history, but also parental attentional bias patterns (Waters, Forrest, Peters, Bradley, & Mogg, 2015). A follow-up study found that a maternal attentional bias to threat was correlated with childhood anxiety symptoms in non-anxious, but high-risk, offspring (Waters, Candy, & Candy, 2018). Measures of pupil dilation in children of anxious mothers were augmented when processing angry faces but not sad or happy faces (Burkhouse, Siegle, & Gibb, 2014). These results corroborate differential physiological processes when perceiving threat that exist in those at risk for anxiety. In a non-clinical sample, pre-natal anxiety symptoms were associated with infants’ heightened attention towards emotional faces as measured by an eye-tracking paradigm (Kataja et al., 2019). Moreover, the attention that has been garnered for threat attention has led researchers to seek a genetic mechanism for this phenomenon (Fox, Ridgewell, & Ashwin, 2009; Pine, Helfinstein, Bar-Haim, Nelson, & Fox, 2008). Pérez-Edgar et al. (2010) found that adolescents who carry the short allele of the serotonin transporter gene (5-HTTLPR) were faster at responding to angry faces. A metaanalysis performed on the topic confirmed a relationship between a 5-HTTLPR polymorphism and preferential attention to stimuli with negative

73 emotional valences (Pergamin-Hight, BakermansKranenburg, van Ijzendoorn, & Bar-Haim, 2012). Fittingly, the 5-HTLPR has also been linked to decreased amygdala grey matter and HPA axis dysregulation (Avshalom Caspi, Ahmad R Hariri, Andrew Holmes, Rudolf Uher, & Terrie E. Moffitt, 2010) which, as mentioned earlier, have been observed in both anxiety and AUD. Various attempts have been made to isolate observable indicators of AUD vulnerability. Importantly, many of these candidate endophenotypes might operate according to similar principles as the attentional biases to threat in anxiety patients. Gorka and Shankman (2017) found that subjects with current AUD, those in remission, and at-risk offspring all displayed an inflated startle response to uncertain threat in comparison to controls. This pattern has also been observed in those with internalizing disorders and is related to fear responses in the amygdala and related structures (Carleton, 2016; S. M. Gorka, Lieberman, Shankman, & Phan, 2017b; Grupe & Nitschke, 2013). Another experiment required children of individuals with AUD to match faces to a target image (Lindsay, Pajtek, Tarter, Long, & Clark, 2014). Contrasting offspring of healthy adults, atrisk adolescents had elevated BOLD activation in the amygdala when matching fearful and angry faces. These paradigms provide evidence that an abnormal threat detection mechanism may be an intermediate phenotype for AUD risk as well. To our knowledge, attentional bias to threatening stimuli as only been examined in the context of AUD as it relates to one’s impetus to drink to cope with social anxiety (Bacon & Ham, 2010; Carrigan, Drobes, & Randall, 2004). The paradigm as never been used to screen for a latent vulnerability marker for AUD in the same way it has been used for anxiety disorders. If AUD and anxiety have an overlapping etiological mechanism, established risk indicators for anxiety disorders should also appear in those at risk for AUD. There is a dearth of literature on the purely genetic cross-transmission of AUD and anxiety disorders. Furthermore, research is presently lacking regarding the offspring of alcoholics and their endophenotypes for future anxiety. Many alcoholics achieve long term sobriety before having children, yet these children are currently excluded from the literature on parental AUD. This a useful cohort to observe because living with a parent who is struggling with AUD may create a


74 stressful environment which, alone, might account for childhood anxiety. Conversely, children of recovered alcoholics may have been shielded from these stressors. Consequently, this study aims to answer two questions. The first one being whether offspring of sober alcoholics, who do not experience the experience the environmental effects of their parents’ disorder, are more vulnerable to anxiety. This research will also attempt assess whether attentional bias to threat serves as a relevant endophenotype for the maladaptive neurocircuitry that may underlie a common etiology for anxiety and alcoholism. We hypothesize that when assessed on a dot probe task, children of recovered alcoholics will show enhanced attention towards angry faces relative to age matched controls. This bias will not significantly differ between offspring of parents with “pure” AUD and those with comorbid anxiety. Furthermore, recovered alcoholics will show the same attentional bias as it reflects a dormant susceptibility. This cross-sectional study will consist of five experimental groups: a) Children of recovered “pure” alcoholics b) Children of recovered alcoholics with comorbid anxiety c) Children without no familial history of psychiatric illness d) Adults who have recovered from AUD e) Adults with no psychiatric history. We will include offspring of “pure” AUD patients to ensure that their response biases are not a function of parental anxiety. However, there results should be comparable to children whose parents have comorbidities. To recruit participants, we will reach out to various recovery organizations and self-help groups, such as Alcoholics Anonymous and non-12-step groups, as these programs encourage long-term participation after sobriety has been achieved. We will also reach out to Treatment and Alumni Groups which cater to individuals who were prior recipients of addiction treatment. Through these outlets, we hope to uncover individuals who have been sober for at least 5 years before having a child, without a period of relapse. Provided they meet the inclusion criteria, their children well be recruited as participants. Control participants will be parents and children recruited from local public elementary schools and will be screened to ensure psychological health. All children should be between the ages of 9-14. Several studies have noted that attentional biases appear in children at risk for anxiety and depression at as early as 5 years of age (Bar-Haim

M. Wiseman et al., 2007; Joormann, Talbot, & Gotlib, 2007). At 9 years old, subjects will be old enough to understand the task. However, after age fourteen they become more susceptible to environmental psychosocial stressors which could interfere with measures of inherited anxiety. Children will be screened on the Screen for Child Anxiety Related Emotional Disorders – Revised (SCARED-R) (Muris, Merckelbach, Schmidt, Mayer, & Differences, 1998) as well has the Childhood Depression Inventory (CDI) (Kovacs, 1981). Both measures have child and parent versions, which will both be administered to ensure an absence of any diagnosable psychiatric disorder. Children of alcoholics must not have experienced their parents drinking related behaviours. Adult alcoholics will meet the DSM 5 criteria for Substance Use Disorder and will not be currently seeking treatment for any psychological disorders. The Beck Anxiety Inventory (BAI) and the Beck Depression Inventory (BDI) (Beck, Steer, & Brown, 1996; Steer & Beck, 1997) will be used as screening tools. Both the “pure” AUD group and the healthy control group should show no indication of depressive or anxiety symptoms. All experimental groups will be assessed using the dot-probe task. This task required participants to fixate on a pair of faces for 1,500 ms. A dot will then appear in the location of one of the faces and participants will have to indicate whether the dot appears on the left or right side of the screen. 48 pairs of pictures will be presented, and each pair will feature the same actor displaying a neutral-angry expression, neutral-happy, or neutral-neutral. The stimulus set will feature an equal gender distribution and will be ethnically inclusive. The emotional faces will appear with equal probability on either side of the screen and all images will appear with the same frequency, in a random order. After viewing the picture-pairs participants will be told to respond to the dot as quickly and accurately as possible, which will remain on the screen until subjects indicate a response. Participants will undergo 144 trials where each pair will be presented 4 times. In order to calculate attentional bias (AB), we will subtract the mean response time (RT) for trials when the probe and the emotional face are on the same side of the screen (congruent trials) from the mean RTs for trials when the probe and the emotional face are on opposite sides (incongruent trials). If RTs are shorter for emotional faces, scores will be positive


Attentional Bias to Threat in Offspring of AUD whereas if subjects avoid emotional faces, it will take them longer to respond to congruent trials and scores will be negative. If our hypotheses are correct, RTs will be shortest on congruent trials with angry faces whereas longer RTs will occur on incongruent angry trials. This is because attention will be captured by the face. There will also be longer RTs on neutral-neutral and neutral-happy trials, for both congruent and incongruent conditions. Children of AUD will show significantly shorter RTs on congruent angry trials relative to controls, and thus greater AB scores. As well, those with parents with “pure” AUD will not differ from those with comorbidly anxious parents. Moreover, adult alcoholics without current AUD or anxiety will have higher AB scores relative to healthy adult controls. This study carries noteworthy implications. If children of alcoholics demonstrate an attentional bias to threat, they also show a vulnerability for an anxiety disorder. Given their concurrent susceptibility for AUD, as evidenced by their parents’ disorder, this provides proof of a common etiology for alcoholism and anxiety. As well, if recovered alcoholics without anxiety demonstrate attentional bias to threatening stimuli, this may reflect their own latent vulnerability to anxiety. Correspondingly, attentional bias to threat may exist as an observable reflection of the aberrant brain processes that are responsible for comorbid alcoholism and anxiety. Given the research on amygdala CRF and grey matter volumes in both disorders, as well as the amygdala’s participation in threat detection, these cortical systems are likely to blame (Alemany et al., 2013; Arborelius et al., 1999; Hill et al., 2001; LoweryGionta et al., 2012; Risbrough & Stein, 2006). More research must be conducted to determine the exact brain-level mechanism that contributes to these disorders’ concurrence. For example, neuroimaging of both amygdala reactivity and volume should be performed on at-risk individuals to determine if biological anomalies correlate with their risk status and attentional bias scores. It should also be noted that research on amygdala functioning is mixed in the milieus of AUD and anxiety disorders. For example, some studies have found larger amygdala volumes to be associated with anxiety, which contradicts the research cited earlier in this paper (Juranek et al., 2006; MacMillan et al., 2003; Schienle, Ebner, Schäfer, & neuroscience, 2011). As well, the exploration of amygdala

75 reactivity in alcoholism yields varied findings. For example, experimenters have found blunted amygdala responsivity in those at risk for AUD, which is proposed to be reflective of behavioural disinhibition (Glahn, Lovallo, & Fox, 2007). This opposes research in anxiety disorders that supposes behavioural inhibition in early development is a warning of future risk, and his related to heightened amygdala reactivity (Muris et al., 2001). It is possible that there exists heterogenous neurological profiles for different instances of anxiety disorders and AUD. Perhaps the comorbid presentation of these ailments has specific neural machinery which need to be further elucidated. This study faces other limitations as well. Despite attempts to control for the possibility that attentional biases are not an after-effect caused by parental AUD, this study does not preclude the possibility that abnormal responses are a scar of parental drinking. Alcohol consumption in high doses may produce epigenetic changes in the parent group which are passed onto offspring. These processes could be responsible for endophenotypic abnormalities in children of AUD sufferers, and not a signal of liability. This is a concern that exists in all quests for endophenotypes. However, due to the fact that the phenomenon exists in anxious individuals with no comorbidities, (Bar-Haim et al., 2007) and that anxiety is so often correlated with AUD (Regier et al., 1990, Kessler et al., 1997, Grant et al., 2004), we are going to assume that the endophenotype represents a vulnerability for the two disorders. Further, the relationship between AUD and anxiety disorders might be different in men and women. According to epidemiological reports, alcoholdependent women may be more likely to experience co-morbid anxiety (Smith & Randall, 2012). It has also been proposed that emotional problems in males have a tendency to manifest themselves as alcoholism, while women are will develop an anxiety or mood disorder in response to the same internal sensations (Nolen-Hoeksema, 1987). The present study, however, will not assess sex differences and look at both males and females equivalently. A future line of inquiry may be to examine sex differences in threat detection as a marker of discrepant comorbidity rates. As well, in the group of adults who experienced “pure” AUD, we must rely on self-report to ensure no prior history of an anxiety disorder. Subjects may be biased in their self-assessments of psychiatric


76 history for many reasons. It is possible that their alcohol problems were more salient and, thus, they were less aware of comorbid anxiety symptoms. Or, their memories for their anxiety problems are overshadowed by alcohol related memories. Similarly, high doses of alcohol in the system may impair memory processes and hinder one’s ability to report on one’s past. Unfortunately, there is a risk that many of the “pure” AUD subjects, may have in fact experienced anxiety that went unrecognized or was forgotten. Additionally, due to the nature of recruitment, the recovered AUD subjects will be active in posttreatment communities. These are individuals who elected to continue attending self-help groups despite long term sobriety, or to attend community centres for alumni of treatment programs. Hence, there may be a selection bias for individuals who take their sobriety very seriously and are particularly fearful of a potential relapse. This group may show enhanced vigilance as a stable trait which is why they are especially effortful in maintaining their sobriety. This trait could bias scores on the dotprobe paradigm as this task involves one’s alertness for threatening material. Another limitation is the existing evidence suggesting that different forms of anxiety may show different attentional bias patterns (Bar-Haim et al., 2007). For example, those with social phobia may be more responsive to facial stimuli. However, there is also evidence for non-specific transmission of internalizing disorders (Starr, Conway, Hammen, & Brennan, 2014). Therefore, a markedly enhanced response to threatening faces in social phobia relative to other anxiety disorders, may not be reflective of an inherited risk factor. Rather it might only emerge after a latent internalizing component has manifested as social phobia. Thus, when looking at children of AUD patients with comorbid social phobia, we should not expect to see a particularly pronounced attentional bias score in the facial dot probe task relative to those at risk for other anxiety disorders. Ultimately, the research serves as a novel approach in the quest to explain comorbid AUD and anxiety. Once we can prove that these disorders share a common etiology, we can open up the search for innovative interventions. It will also give us impetus to monitor children of alcoholics more closely, specifically in the context of anxiety, as they may be uniquely vulnerable from a genetic basis. It may also

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79 children. 26(1), 99-112. Natividad, L. A., Buczynski, M. W., Herman, M. A., Kirson, D., Oleata, C. S., Irimia, C., . . . Parsons, L. H. J. B. p. (2017). Constitutive increases in amygdalar corticotropin-releasing factor and fatty acid amide hydrolase drive an anxious phenotype. 82(7), 500-510. Nolen-Hoeksema, S. J. P. b. (1987). Sex differences in unipolar depression: evidence and theory. 101(2), 259. Owens, M. J., Vargas, M. A., Knight, D. L., & Nemeroff, C. B. (1991). The effects of alprazolam on corticotropin-releasing factor neurons in the rat brain: acute time course, chronic treatment and abrupt withdrawal. Journal of Pharmacology and Experimental Therapeutics, 258(1), 349-356. PĂŠrez-Edgar, K., Bar-Haim, Y., McDermott, J. M., Gorodetsky, E., Hodgkinson, C. A., Goldman, D., . . . Fox, N. A. (2010). Variations in the serotonintransporter gene are associated with attention bias patterns to positive and negative emotion faces. Biological Psychology, 83(3), 269-271. doi:https:// doi.org/10.1016/j.biopsycho.2009.08.009 Pergamin-Hight, L., Bakermans-Kranenburg, M. J., van Ijzendoorn, M. H., & Bar-Haim, Y. (2012). Variations in the promoter region of the serotonin transporter gene and biased attention for emotional information: A Meta-Analysis. Biological Psychiatry, 71(4), 373-379. doi:https:// doi.org/10.1016/j.biopsych.2011.10.030 Pine, D. S., Helfinstein, S. M., Bar-Haim, Y., Nelson, E., & Fox, N. A. (2008). Challenges in Developing Novel Treatments for Childhood Disorders: Lessons from Research on Anxiety. Neuropsychopharmacology, 34, 213. doi:10.1038/ npp.2008.113 Pishnamazi, M., Tafakhori, A., Loloee, S., Modabbernia, A., Aghamollaii, V., Bahrami, B., & Winston, J. S. (2016). Attentional bias towards and away from fearful faces is modulated by developmental amygdala damage. Cortex; a journal devoted to the study of the nervous system and behavior, 81, 24-34. doi:10.1016/j. cortex.2016.04.012 Quitkin, F. M., Rifkin, A., Kaplan, J., & Klein, D. F. (1972). Phobic anxiety syndrome complicated by drug dependence and addiction. A treatable form of drug abuse. Arch Gen Psychiatry, 27(2), 159-162. Regier, D. A., Farmer, M. E., Rae, D. S., Locke, B. Z., Keith, S. J., Judd, L. L., & Goodwin, F. K. (1990). Comorbidity of mental disorders with alcohol and


80 other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA, 264(19), 2511-2518. Risbrough, V. B., & Stein, M. B. (2006). Role of corticotropin releasing factor in anxiety disorders: a translational research perspective. Hormones and behavior, 50(4), 550-561. doi:10.1016/j. yhbeh.2006.06.019 Salemink, E., van den Hout, M. A., & Kindt, M. (2007). Selective attention and threat: quick orienting versus slow disengagement and two versions of the dot probe task. Behav Res Ther, 45(3), 607-615. doi:10.1016/j.brat.2006.04.004 Schienle, A., Ebner, F., Schäfer, A. J. E. a. o. p., & neuroscience, c. (2011). Localized gray matter volume abnormalities in generalized anxiety disorder. 261(4), 303-307. Smith, J. P., & Randall, C. L. (2012). Anxiety and alcohol use disorders: comorbidity and treatment considerations. Alcohol research : current reviews, 34(4), 414-431. Starr, L. R., Conway, C. C., Hammen, C. L., & Brennan, P. A. (2014). Transdiagnostic and disorderspecific models of intergenerational transmission of internalizing pathology. Psychol Med, 44(1), 161172. doi:10.1017/S003329171300055X Steer, R. A., & Beck, A. T. (1997). Beck Anxiety Inventory. Tambs, K., Harris, J. R., & Magnus, P. (1997). Genetic and environmental contributions to the correlation between alcohol consumption and symptoms of anxiety and depression. Results from a bivariate analysis of Norwegian twin data. Behav Genet, 27(3), 241-250. van den Heuvel, O. A., Veltman, D. J., Groenewegen, H. J., Witter, M. P., Merkelbach, J., Cath, D. C., . . . van Dyck, R. (2005). Disorder-specific neuroanatomical correlates of attentional bias in obsessive-compulsive disorder, panic disorder, and hypochondriasis. Arch Gen Psychiatry, 62(8), 922933. doi:10.1001/archpsyc.62.8.922 Walker, D. L., Miles, L. A., & Davis, M. (2009). Selective participation of the bed nucleus of the stria terminalis and CRF in sustained anxiety-like versus phasic fear-like responses. Progress in NeuroPsychopharmacology and Biological Psychiatry, 33(8), 1291-1308. doi:https://doi.org/10.1016/j. pnpbp.2009.06.022 Waters, A. M., Candy, E. M., & Candy, S. G. (2018). Attention bias to threat in mothers with emotional disorders predicts increased offspring anxiety

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81

Only The Young?

HANNA WARITH The research article centres around the popular belief that in order to be effective at helping children with behavioural problems, interventions must be carried out early on in the child’s life (Gardner et al, 2018). The critical questions of this research article are whether or not young kids benefit from these interventions more than older children, the second, whether the effects of age can be translated into children’s development stages specifically? and finally, should interventions be developmentally specific? (Gardner et al,2018). The authors compare the effects of intervention for psychological mechanisms at three different stages in child development (Gardner et al, 2018). Parenting interventions are a key example of an intervention to help behavioural issues. They involve fostering a stronger parent-child relationship and thus improve the child’s chances of decreasing the impact of their mental and physical problems. In order to show how significant the timing of parental intervention is in reducing behavioural problems, the author uses a method called Individual Participant Data (IPD) meta-analysis, where they combine all the data from every family who did the same kind of intervention: The Incredible Years (IY) for children ages 2-12 allowing them to see a full age range and compare the effectiveness of parenting intervention. The study also accounted for income status and race, 60% of the children had low socio-economic status and 30% were from ethnic minorities (Sellgren, 2018). The study concluded that there was a main effect of intervention. They found that the parenting intervention succeeded in reducing the children’s conduct problems by about 95% (Gardner, 2018); they did not find any differential effect of the IY on age, meaning that it had the same beneficial effect on children no matter the age. While trying to answer the second research question regarding developmental specificity for interventions, they found no effect of age as well (Gardner, 2018). The news article begins by describing the premise

of the study and the general idea that interventions should be carried out as early as possible in a child’s life because that is when their behavior and mental functioning are most malleable (Sellgren, 2018). The article accurately discusses the main conclusion of the study that states that intervention is significant no matter the age of the child contrary to popular thought. The article also mentions that interventions for a more narrow range of ages were no more effective than interventions that aimed at helping children within a wider age range (Sellgren, 2018). However, the article does not mention the third research question about whether interventions should be developmentally specific or not. The article also brought up a major point of the study that the goal is not to delay intervention, rather the results of the study merely state that if a child’s behavior is an issue, interventions can still be carried out and the results will be the same no matter how old the child is (Sellgren, 2018). The article, despite explaining the findings of the study and accurately reporting them, failed to explain why the results were the way they were and why the findings are not consistent with the central theory that earlier intervention equals better outcomes for the child. Notably, a key explanation from old studies was that parent- child interactions become entrenched so it is harder to change a child’s behavior (Gardener, 2018). The results of the current study do not support this conclusion and instead point out that the parent- child interactions are vital to helping reduce a child’s problematic behavior. The article further discusses what the results mean for future policies such as the main idea that “it’s never too early, it’s never too late” (Gardener, 2018). Another policy recommendation is that services should be focused on identifying and supporting younger and older children as opposed to focusing solely on young children to reduce behavioral problems using interventions (Gardner, 2018). The article also discusses the final policy


82 recommendation that it may not be necessary to have different kinds of programs for different development stages because as shown from the results, they are no more effective than using the same program that is adjusted accordingly to the age of the child. This could also have cost-saving advantages for therapists and for how the intervention is carried out (Gardner, 2018). However, the news article fails to address a key point which is that the ethnicity of the child does not matter, meaning the same intervention can be carried out across cultures and have similarly beneficial effects. In terms of improving the media report, I would add the finding about how it is not required to have greater specificity of intervention in regards to different cultural groups (Gardner, 2018). This is a important point to add because it is relevant for Eastern cultures where the intervention may be different. The results across different nations and cultures is the same, and this could be relevant for readers who live in another part of the world wanting to know how to help improve their child’s behavior. Another improvement that could be made is that the article could add in visual representations of the results that were included in the report itself, so then readers would be able to visually understand just how significant the results of the study were. This would give a greater understanding of the goals and successes of the study, which could be highly applicable to their families. References Gardner, F,& Melendez-Torres, G.J, & Mann, J, & Hutchings, J, & Leijten, P, ... Scott, S (2018). The Earlier the Better? Individual Participant Data and Traditional Meta- analysis of Age Effects of Parenting Interventions. Child Development, p 1-13. https://onlinelibrary.wiley.com/doi/pdf/10.1111/ cdev.13138 Sellgren, Katherine (2018, September 26). It’s “never too late” for parenting advice, study says. BBC News. Retrieved from https://www.bbc.com/ news/education-45652699

H. Warith


83

Alicia Florrick: The Good Wife (Seasons 1-4)

BIANCA MERCADANTE “A good woman is hard to find, and worth far more than diamonds. Her husband trusts her without reserve, and never has reason to regret it. Never spiteful, she treats him generously all her life long.” (Proverbs 31:10-31) When one reflects upon what characteristics a “good” wife possesses, one might imagine a woman who is kind, loyal and supportive. She is the glue that keeps her family together; strong and unyielding. Without her compliance to the duties her role entails, order is lost and chaos threatens to ensue. Alicia Florrick —the good wife of “The Good Wife”— is first introduced at a press conference held by her husband, where she stands by his side as he apologizes to the public following charges of corruption and for his involvement in a sex scandal during his time as State’s Attorney. This critical scene seems to truly encompass the show’s title. After all, what more could a man ask for than a wife who stands unwaveringly by his side after his sex tapes are publicly leaked? If one were to be exclusively exposed to this one scene from the show, they might believe that Alicia Florrick floats toward the lower end of extraversion, at least in terms of social dominance— she is staying with her husband despite being publicly humiliated as a consequence of his actions. However, as outlined in class, low social dominance as a predictor for remaining in an unhappy marriage only applies to men. Of course, despite lack of empirical evidence, one might still argue that this could also hold true for women. Yet, as the show unravels, it becomes very clear that Alicia Florrick is not low on social dominance at all. So then, why does she stay? Let us begin by teasing apart the first layer of Alicia’s personality— her dispositional traits. As a social actor, Alicia presents as high in conscientiousness. When her husband is sent to prison on charges of corruption, she is forced to assume the role of her family’s breadwinner, and after over ten years of not

practicing law, is hired as a lawyer at Stern, Lockhart, & Gardner. Alicia becomes a major asset to the firm rather quickly. Her high levels of conscientiousness seem to be, in part, predictive of her success. She is hard-working, self-disciplined, responsible, reliable and well-organized. She is invested & exacting in her work. As mentioned by McAdams, “people high in conscientiousness tend to see work as central to their identity.” As Alicia continues to work at the firm, her identity as the “good wife” becomes less and less salient. It is vocationally where she truly shines. Alicia also displays high levels of agreeableness. She is cooperative, accommodating, and helpful. She is sincere, honest, and extremely likeable. On various occasions, she is assigned to cases with clients with more difficult temperaments, as a result of her patience and strong interpersonal skills. Furthermore, Alicia displays low levels of neuroticism, and shows no indication of being high nor low on openness to experience. These traits appear to be less central to her dispositional makeup. We are left with what I believe is Alicia’s most intriguing trait dimension—extraversion. As mentioned previously, one may conclude that Alicia scores low on the social dominance component of extraversion since she remains in an unhappy marriage with a husband who has cheated on her on multiple occasions. However, as the show unravels, viewers will find that this initial impression of Alicia is unsubstantiated. For example, minutes after her husband’s press conference ends, Alicia is standing alone in a hallway, the perfect composure she wore for those at the press conference quickly melting away, revealing how distraught she actually is. When her husband approaches her to ask if she is okay, she responds by slapping him across the face. In this one action, it is clear that although Alicia is willing to be a “good wife” in the public eye in order to protect her husband’s career, she will not remain complaisant in protecting his ego. As the show


84 goes on, one realizes that Alicia is not introverted at all—she is just an extremely high self-monitor. When she is required to play “the good wife,” she presents as quiet and accommodating, allowing her husband to monopolize the spotlight. However, at work, she presents much differently, asserting her social dominance in court, with colleagues, and with clients. Alicia masterfully plays whichever role is necessary given the social context. In Lippa’s 1976 follow-up study on the participants from Bem’s 1974 original study on consistency of behaviour and level of friendliness, he has both extraverts and introverts who were either high or low on selfmonitoring teach a class, playing the part of either a highly extraverted teacher or a more introverted teacher. He found that high self-monitors were able to play both roles, and that extraverts were even better at this then introverts. This is further evidence confirming that Alicia is an extravert—any notion pointing toward her being introverted is simply a result of her adeptness in self-monitoring. So again, we ask, why does she stay in her marriage? What could possibly motivate her? The answer (although more subtle at the beginning of the series) seems to lie in Alicia’s power motivation. Now, at the beginning of season one, this may not appear to be her most salient motive. When she begins working at Stern, Lockhart, and Gardner, it becomes quite clear that she is high on achievement motivation. She shows a recurrent preference for wanting to do her job well, readily accepting challenges, responsibility, and feedback. One might also believe Alicia to be low on affiliation motivation—she does not seem to care about being liked. Furthermore, being high on affiliation motivation could actually be disadvantageous as a lawyer. If one hopes to win a case for a client, they cannot be preoccupied with the opposing side’s fondness of them—they must only be preoccupied with winning, which could result in the establishment of enemies, and this would definitely be off-putting to someone high on the affiliation motive. However, one could argue for Alicia being at least moderately intimacy-motivated. Evidence for this would be the affair she engages in with her boss following her husband’s infidelity. Perhaps because she is lacking a connection with her husband, she feels inclined to seek it out with someone else. Interestingly enough, Alicia’s choice in who she has an affair with points back to her being high on power motivation—she has an affair with her boss,

B. Mercadante a managing partner at her firm. Her husband also possessed a great deal of power as State’s Attorney. In fact, upon marrying him, she gave up her career as a lawyer, a component of her life that greatly fuelled her achievement motivation, to stay at home to raise her children. It seems that although Alicia’s power motivation initially presents as less salient, it is what ultimately dictates a lot of the choices she makes. This is what answers the question of why she stays with her husband. She is motivated by his power, and the power she is also granted as his wife. There are also hints that foreshadow Alicia’s increasing power motivation—for example, her acquisition of prestige symbols. Alicia resides in a beautiful apartment, drives a nice car and is always welldressed in designer brands. As the show persists, her power motive becomes increasingly noticeable. For example, when offered partnership in her fourth year with the firm, Alicia initially reacts very receptively and with enthusiasm. However, upon finding out that she was one of five fourth- year-associates offered partnership as part of a plot to raise money for the firm who is going through a spout of bankruptcy (partnership involves a $600, 000 buy-in), her excitement quickly dissipates. The position is no longer enticing, as it is no longer associated with the same amount of prestige. Soon after the associates are offered these positions, the firm emerges from bankruptcy, and suddenly becomes rather profitable. In an attempt to avoid having to share their wealth, the partners vote to delay the offer of partnership by a year. Of course, the associates are not pleased upon receiving this news, and they decide to join forces and meet with the firm’s top clients in hopes of the news of their meetings getting back to the partners who might think they are trying to poach clients and start their own firm (a scare tactic that they hope will result in being re-offered their partnerships). When the partners get whiff of their scheme, they decide to try to break the associates’ unity by re-offering only one of them partnership, and this associate ends up being Alicia. In McAdam’s description of power motivation, he expresses that those high on this motive will “take large risks in order to attain visibility.” Despite the loyalty she feels she must preserve toward the other associates, she accepts the offer, risking her relationships with these colleagues, once again demonstrating her orientation towards power. There are many dimensions to Alicia’s personality that contribute to how fascinating she is as a


Alicia Florrick: The Good Wife character, as we have barely chipped the surface of levels one and two, let alone three). However, it is my belief that her motivation for power governs the expression of the other components of her personality—a strong example of this being how and when she self-monitors. Contrary to one’s initial assumption, the title of the show does not encompass her identity and it does not refer to her reverence as a wife. Rather, “the good wife” is merely a role Alicia plays because it is advantageous in her pursuit of power. Alicia Florrick may be known as “the good wife,” but what she truly is, is an excellent actor.

85


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