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Journal of Youths in Science

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Volume 11 Issue 1

THE TINIEST SCIENTISTS Seongkyung Bae

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FINDING WALDO Andrew Hwang

QUANTUM COMPUTING Katherine Izhikevich


Contact us if you are interested in becoming a new member or starting a chapter. or if you have any questions or comments. Website: www.journys.org // Email: eic@journys. org Journal of Youths in Science Attn: Mary Anne Rall 3710 Del Mar Heights Road San Diego, CA 92130 1 | JOURNYS | SUMMER 2020


table of

CONTENTS Journal of Youths in Science Issue 11.1 - Summer 2020

3 5 12 15 16 20 22 25 26 27

Your iPhone And Your Teenage Brain: Are They Really Related? Shivek Narang The Tiniest Scientists Seongkyung Bae Finding Waldo: Two Routes for Processing Visual Search in Complex Scenic Images Andrew Hwang The Fine Line Between A.I.’s Bright Future and Doom Michele Kim Psychiatric Factors Influencing Cervical Dystonia Prerana Pai Bhande The Anti-Vaccination Movement: Paranoia or Necessity? Bill Yildiz Quantum Computing Today Katherine Izhikevich Building Consciousness in Artificial Intelligence: I think, therefore I am. Charvie Yadav TP Math Club Infographic Amy Ge An Analysis of Global Economic Indicators Pratyush Seshadri & Abraham Goldstein 2 | JOURNYS | SUMMER 2020


Your iPhone And Your Teenage Brain: Are They Really Related? By Shivek Narang || Art by Lesley Moon The babies born in the first decade of the twenty-first century, the children of generation Z, are now living in an era known as the “digital revolution”, a time when the growth in devices and technology are booming. Incidentally, the percentage of adolescents from 2004 to 2014 reporting recent clinical depression grew by a solid 37% [1]. That is no trivial amount! Almost every teenager, worldwide and especially in the United States, has access to the internet and an electronic device, with a vast majority of them having their own cell phones as well. Is it possible that this “digital revolution” as it is so aptly called, may be linked to this alarming increase in mental health issues pressuring teenagers? Before talking about the effects of the internet and electronic technologies, it is important to understand how the adolescent brain works. Many of you parents, fellow teenagers, or even younger siblings may have noticed certain characteristics in teens including heightened risk taking and recklessness, a desire to do what gives them happiness rather than what is right, and an unfortunate change in priorities from family to friends. Well, many of these changes are due to a phenomenon referred to as brain plasticity. Now you are probably surprised to hear that your brain is plastic, but what plasticity really means is that the brain is constantly changing and being molded by experiences. This means that 3 | JOURNYS | SUMMER 2020

synapses in the brain will be altered and changed leading to a sense of instability in the brain during that time frame. Because of this constant change that is occurring in the brain, teenagers tend to be less firm in their resolve in staying away from peer pressures and temptations, including those of electronics and the internet. This is coupled with the fact that very few individuals have actually embraced school teachings regarding how to properly navigate the unpleasant corridors of the world wide web, and thus adolescents may make risky choices and succumb to its various grotesque distractions. Given that the brain is plastic, these negative influences will start to build up in the brain, causing them to continuously spiral downward in mental health. This lack of education and guidance for teenagers when using electronic devices is certainly not beneficial or helpful to the students in any way. Needless to say, the entertainment industry has been massively bolstered by the exponential rise in technology. Adolescents spend an average of about 9 hours a day using electronics for entertainment purposes [2]. You may have noticed that it is often hard to peel them away from activities like video games. Why does this happen? Well, playing video games excites the same pathway that eating, drinking, and sex do: the reward pathway. When this reward pathway gets stimulated, dopamine,


the pleasure neurotransmitter, increases in quantity in the nucleus accumbens, leading to the user feeling extra happy and light hearted. We crave this feeling and this leads to addiction, the force that makes it so hard to peel ourselves away from our games. A common hypothesis that many individuals have is that an increase in violent video games leads to an increase in teenage crime and it is not hard to see why this may be. However, as studies have noted, while there has been an increase in violent video games (quadruple as much in fact), and the number of violent crimes have increased, murders committed by teenagers decreased by almost 72% and violent crimes by 49% [3]. This by no means indicates a causal relationship (and it is evidently hard to imagine one) but it is interesting to observe the incongruity between expected and actual results and highlights the ambiguity that is present in analyzing this correlation between viewing material and behavior. Yet, as teenagers get addicted to these video games, both violent and non-violent, they push away other things, such as homework and schoolwork, to regions of lesser importance. Additionally, there is the increase in rates of pornography, as sexually explicit sites are some of the more frequently visited ones. Yet, while it is assumed that teenagers make up a substantial portion of pornographic viewers, it is quite surprising to observe that there has actually been a decrease in teenage pregnancies recently. Thus, one cannot prove a direct causal relationship but it does not take away from the fact that teenagers spend a lot of unnecessary time watching explicit material that they should not be watching, with the impulsive brain being a big reason why. A final aspect to consider when linking teenage behavior to the internet is the social aspect. You may have heard the common phrase “humans are social animals” and thus it may not come as a surprise why social media platforms are so popular and powerful. Millions of teenagers around the world have some form of social media, from Facebook, instagram, snapchat, to twitter. Yet, the prefrontal cortex, the part of our brain responsible for making good social decisions, only finishes maturing towards the end of puberty, which corresponds to the very ends of adolescence and the teenage years. This may explain why teenagers tend to make risky social decisions like posting information that is not respectable, sending rude or risky texts to a friend, or even engaging in online bullying while meaning it as a joke. Since their social filters are still developing, giving teenagers a social forum where they are essentially free to express whatever they want is bound to have some negative effects. Another debate is whether social media actually makes teenagers more social or less social. On one hand, teenagers may decrease the amount of time they spend physically interacting

with friends to catch up instead with their friends on the internet. On the other hand, it gives others who are often afraid of voicing their opinions in front of their peers a forum to share more about themselves. It encourages more connections, as demonstrated by the fact that the average teen has over 425 friends on Facebook [4], but it also decreases the number of deep and meaningful connections, as less than one fourth of their facebook friends are truly friends that they connect with. From a negative perspective, it has led to harsh cyber bullying affecting so many young children, but on the positive side, it has given others a forum to voice their problems and their pains, enabling them to seek help and comfort from counselors or peers who are experiencing similar difficulties to them. This conundrum has continued to puzzle and baffle sociologists as they have been unable to find a clear and definitive answer to this social aspect of the internet. In the end, we have still not been able to come with a firm answer on whether or not the digital revolution has been a welcoming positive, or a harsh negative, influence on the teenage brain. What we do know is summarized quite beautifully as a report done by The Pew Internet and American Life Project Foundation states “Millennials will benefit and suffer due to their hyperconnected lives” [4]. The social world spurred on by the digital revolution has certain positives that will improve the lives of many teenagers, but it also has many undebatable negatives that disguise themselves perfectly to lure and harm teenagers. We do not have a clear answer if the electronic media and smartphones are really a detriment or an aid to teenage neurobiology or society, but we can come to a consensus that it is a little bit of both.

References: [1] Benham, Barbara, and JH Bloomberg School of Public Health. “Depression Rates Growing Among Adolescents, Particularly Girls.” Johns Hopkins Bloomberg School of Public Health, 16 Nov. 2016, www.jhsph.edu/news/news-releases/2016/ depression-rates-growing-among-adolescents-particularlygirls.html. [2] Fox, Maggie, and Erika Edwards. “Teens Spend ‘Astounding’ Nine Hours a Day in Front of Screens: Researchers.” WVEA, www.wvea.org/content/teens-spend-astounding-nine-hoursday-front-screens-researchers. [3] Giedd et al. (2012). The Digital Revolution and Adolescent Brain Evolution. Journal of Adolescent Health, 51, 101-105. [4] Sterling, Greg. “Pew: 94% Of Teenagers Use Facebook, Have 425 Facebook Friends, But Twitter & Instagram Adoption Way Up.” Marketing Land, 22 July 2014, marketingland.com/pewthe-average-teenager-has-425-4-facebook-friends-44847. 4 | JOURNYS | SUMMER 2020


the tiniest scientists:

what do infants know & how do they learn about the world? Seongkyung Bae art by Kevin Song

by

abstract Infants are one of the richest sources of information about the origin of human thinking and learning. Although gathering data from infants regarding their brain functions and behaviors is incredibly insightful and valuable, infants are a challenging group to study. Unlike adults, they cannot verbally communicate with researchers, make explicit responses, or control their bodies. Given these limitations, developmental scientists have employed smart study paradigms and research technologies to better understand the developmental trajectories of perception, cognition, and social understanding in infants. The first section of this review paper provides an overview of the study methods, including classical looking time measurements and modern neuroimaging techniques. The second section discusses what these approaches have found regarding the core knowledge and skills that allow infants to explore, learn, and interact with the external world surrounding them.

1. introduction Every person starts their life as a baby. As time goes by, they become older, and they eventually become an adult. By the time a person is an adult, they can do a lot of things: walk, eat, drink, talk, read, write, etc. But how and when do infants learn to do these things, as they do not seem to have much information about the world? Developmental scientists suggest that there is something known as core primitives, also known as core knowledge, that

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infants are born with and rely on to understand when or what to learn [1]. To learn about the nature of core knowledge in infants, developmental scientists conduct research using various approaches ranging from behavioral tests measuring looking time and neuroimaging techniques. This review paper will present an overview of infant research methods and some discoveries from these methods regarding the development of infants in perceptual, cognitive, and social domains.

2. measuring looking time in infants Because infants cannot tell scientists what they think or understand, developmental scientists have long relied on “looking time measurement� as a clue [2-4]. Examining how long infants look at a particular event or an object can indicate to scientists the attention, interest, and engagement of the infant towards it. Two important properties of the infant’s mind are novelty preference and expectation violation. First of all, novelty-seeking behaviors are observed both in grown-ups and infants, suggesting that novel information, as compared to repeated information, are treated differently in the human mind. While novel information tends to get more attention and a longer looking time, repeated stimuli tend to receive progressively less processing [5]. Novelty preferences in looking time have been reported and discussed in many research studies of infant perception and cognition. Cognitive psychologists refer to the


process of giving less attention and processing time for repeated stimuli are called by cognitive psychologists “habituation [6,7].” In one classical study [8], a visual stimulus was presented repeatedly, and then the infants’ looking time for the stimulus over the repetitions was recorded to estimate the degree of “habituation (or familiarization).” The main finding was that the more times the stimulus was repeated, the shorter amount of time infants looked at the stimulus, indicating habituation. Later, numerous studies began to utilize looking time measurements as a research tool to examine infants’ abilities to remember a stimulus that was presented previously and discriminate it from a novel stimulus. In such studies, researchers typically showed infants both repeated and novel stimuli, then examined whether and how much the infants preferred to look at the novel as compared to the repeated stimulus. In one study [9], experimenters showed one group of infants a series of displays of eight items and the other group of infants a series of displays of 16 items during the habituation phase. In the following test phase, infants viewed a pair of images, one from the eight items and the other from the 16 items, side by side. They then compared the durations of the infants’ looking time for the two images to test whether each group showed novelty preference. They found that the group of infants who were habituated with a series of images from the eight items looked longer at the test image of 16 items. Conversely, the group who was habituated with images of 16 items looked longer at the test image from the eight items. These results indicate that the infants could discriminate between the two choices: they had a memory of the repeated stimuli. Therefore, the researchers concluded that babies who were as young as six months have intuitive senses to be able

to discriminate between the sets of items. Another property of the infant mind underlying the looking time measurements is that infants, just like adults, are more interested in something that violates their expectations [10,11]. The logic is that infants get more interested in things that are unexpected or surprising to see, based on their core knowledge and understanding of the world. The infants might want to know what could have caused that violation of expectation. The longer they look at it, the more they are engaged in the object, which allows their learning to be enhanced. The notion of “violation-of-expectation” has allowed many researchers to make inferences about infants’ perceptual and cognitive abilities [12-14]. This technique has been used to examine infants’ understanding of the basic physical principles such as the existence of objects, and

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that they rarely disappear, even when they become unseen or occluded by other objects (termed, “object permanence” [15,16]), as well as the fact that they cannot pass through each other [17,18]. In one experiment, a researcher first showed infants two toys, then hid them behind a screen placed between the two toys (see Figure 1 for visual illustrations). After a while, the experimenter removed the screen so that the toys that were previously hidden became visible again. Importantly, the researcher showed the infant’s two different test events. In one event, only one toy was revealed, which is an “impossible” outcome and violates the infants’ expectations, given that two toys were hidden at the beginning of the trials. In the other event, both toys were revealed after the removal of the screen, which is the expected outcome. The infants looked longer at the impossible outcome compared to the possible outcome. This finding was interpreted as the infants being more interested in the impossible outcome, violating their expectations, suggesting that infants have the naïve arithmetic knowledge that 1 + 1 = 2. Together, these studies illustrate the power of looking time measurements as tools that allow researchers to make meaningful inferences about the infant mind.

3. A neuroimaging approach to explore the function and structure of the infant’s brain Over the past few decades, neuroimaging research has made significant progress in identifying the structure and functions of the human brain. Our understanding of the human mind has significantly increased from studying different sub-areas of the human brain and their associated functions and neural characteristics. Such understanding requires reliable approaches that involve non-invasive methods for measuring brain function and activations in awake, behaving humans. One of these approaches that has become increasingly popular is functional Magnetic Resonance Imaging (fMRI). fMRI has the unique ability to precisely and reliably map the entire brain,

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Figure 1: Visual illustration of an experiment in which researchers employed the looking time measurement based on the notion of violation of expectation.

including subcortical and cortical structures. Only recently have important extensions of the previous work on functional specialization of brain subregions in human adults have begun to be made, exploring the emergence of these functions and their developmental origins through infant fMRI research. Since infants cannot stay still in the fMRI scanner, the majority of fMRI studies with infants have been conducted when they are sleeping [19,20]. The patterns of brain activation measured with fMRI in sleeping infants have been examined to understand how the infant brain works during the “restingstate” ([21,22] not doing any task or mental work). Although resting-state data from infants allows for direct comparisons with data from adults, further research needs to be done with awake, behaving infants to better understand how the human brain develops specialized to support cognitive tasks and complex behaviors. One example of a fMRI study examining brain


Figure 2: Visual illustration of experimental procedures described in Liu et al. (2017)’s study. Source: [33]

activation in awake infants is a motion perception experiment [23]. Using virtual reality goggles, researchers tested whether newborn infants could extract the directions of coherent motion from individually moving dots and discriminate them from random motion (examples of these moving dots is shown in Figure 2) as adults would. In another study, researchers tested whether the brains of 4-6 months old infants showed adult-like selectivity based on the categories of visual images [24]. In this study, researchers found that brain responses to images of faces and scenes showed separable patterns, suggesting that the infants’ brain could differentiate the two categories within a few months after birth. Although more and more infant fMRI studies are being conducted in the fields of cognitive psychology and developmental science, this is also a challenging task for several reasons. Overall, infants do make good participants in psychological experiments because they cannot follow instructions or provide clear verbal responses. Researchers have strived to establish indirect measurements such as heart rate [25], looking time, and resting-state fMRI signals. However, some challenges are more specific to conducting infant fMRI studies, as outlined below. First of all, infant fMRI research can be quite challenging because infants tend to move a lot and cannot be instructed to stay still. This is a serious issue, as head and body movement is a substantial challenge

to fMRI research in general. To adult participants, experimenters tend to provide special instructions to minimize their movement throughout the fMRI study, which is not a realistic solution for infant fMRI. Researchers have been seeking strategies to reduce movement, and some of them appear to be at least partially successful. Some researchers have observed that infants move less when they see engaging stimuli, at least for short periods of time [26]: thus, they try to present something that can attract infants’ attention during an fMRI study. Others have used foam padding and vacuum pillows to reduce incidental motion possibly caused by constriction of the infant in the fMRI scanner [27,28]. Finally, many researchers also use computational algorithms [29] to correct the data, rather than trying to deal with the movement of awake infants in the fMRI scanner. Another challenge of conducting infant fMRI study is that infants’ brains are much more variable across individuals, as compared to adults’ brains. Since each infant shows different rates of development and growth, the relative organization of functions, as well as brain shape and size are highly variable across individual infants. In adults, brain structure and function are much more stable, allowing researchers to stretch and adjust images of individual’s brains to overlay onto a standardized brain data for group comparison. On the other hand, the development of function and anatomy over time is more likely to show different trajectories across infants. This makes comparisons between groups or across individuals at infancy difficult. Modern cognitive neuroscience approaches are currently seeking to find solutions to this challenge by adopting various computational and mathematical algorithms. Although this review paper mostly focused on fMRI, many other neuroimaging techniques are available to examine the development of function and structure in the brain, as well as changes in behavior. Such techniques include Electroencephalography (EEG), which records electrical changes on the scalp [30] and Magnetoencephalography (MEG), which captures subtle changes in the magnetic field generated by electrical neural activity [31]. Both EEG

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and MEG can measure signals from the infant’s brain in a non-invasive, safe manner, while capturing very rapid mental processes. Each of these techniques has its strengths and weaknesses as compared to the other. fMRI has better spatial resolution, providing better maps for localizing small brain parts, including the subcortical areas. However, it is not fast enough to capture brain signals when studying rapid mental processes [32]. Conversely, EEG and MEG have better temporal resolution than fMRI and capture rapid temporal dynamics of brain functions, though the spatial resolution is not as good as fMRI [32]. Thus, researchers need to decide which approach would be appropriate depending on their research goals: whether they are testing temporal changes of brain functions or localizing the small brain parts that are active for a particular mental process.

4. What do infants know about the social world and Many developmental psychologists agree that infants are equipped with the core knowledge and abilities that helps them learn new information and

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interact with the external environment. This section will review the core knowledge that infants seem to have from their early stage of development, in two different domains: social cognition and the basic physics of the natural world.

Social cognition in infancy Infants seem to know that people do something in a specific way to achieve whatever they are trying to get. Researchers explored how infants can understand and interpret what goal people are trying to achieve and what supports this ability [33]. They carried out experiments with infants to test whether infants can appropriately infer the value of the two goals from the efforts others put in to achieve the goals. They showed infants an animated video of a circle agent pursuing one goal than another (see Figure 3 for visual illustrations). In one video clip, the agent tried to jump on a small wall (less effort), but when faced with a medium wall (some effort), refused to

Figure 3: Visual illustration of experimental procedures described in Liu et al. (2017)’s study. Source [33]


jump to reach the triangle (Goal #1). In the other video clip, the agent tried to jump on the medium wall (some ffort), but when faced with a higher wall (more effort), it refused to jump to reach the square (Goal #2). To test whether infants understood which goal (Goal #1 vs. Goal #2) the agent would try harder to reach, researchers then showed the infants a clip of the agent being in the middle, with the triangle (Goal #1) and square (Goal #2) on each end without any walls. They found that the infants looked longer when the agent went to Goal #1 that took less effort, compared to when the agent went to Goal #2 that took relatively more effort. Based on the notion of expectation violation, the researchers suggested that the infants understood the degree to which the agent put its effort into Goal #1 vs. Goal #2. The researchers also indicated that the infants might have formed an understanding about the agent’s value system: Goal #2 is more valuable than Goal #1 to the agent because the agent put more effort to reach Goal #2 compared to Goal #1. Furthermore, the infants might have expected that the agent would proceed toward the high-value target (Goal #2) when they are both equally available. Therefore, this finding suggests that infants can successfully infer the value of goals from others’ actions based on their efforts.

Basic knowledge about physics in the natural world Some researchers also suggest that infants are born with the core knowledge about some basic physical rules about the natural world. They seem to have expectations for some characteristics of objects and have a prior idea of what each object might be like. For example, object permanence is one of the basic concepts about most of the physical objects in the world. It is the understanding that objects continue to exist even when they are not visible or perceptible for the moment. In one classical study on object permanence in infancy, researchers showed two different events to 4.5 month old infants [10]. The

infants were first habituated with the experimental setting by viewing a screen that rotated back and forth through a 180 ̊ arc. The screen first rotated moving away from and then back toward the infants. After the habituation phase, a box was placed behind the screen in front of the infants, followed by one of the two test events. In one event, the screen rotated until it reached the hidden box behind the screen (an expected event in which the hidden box stopped the screen). In the other event, however, the screen rotated through a full 180˚ arc, as if the hidden box no longer existed behind the screen (an unexpected event). The researchers found that the infants looked longer at the unexpected event than the expected event. Relying on the notion of expectation violation again, they suggested that the 4.5-month-old infants have created representations of the hidden object (the box). They also indicated that the infants could correctly establish expectations about the property of an object: an object continues to exist even when it is occluded by another object, and that it cannot physically pass through another object.

Conclusion This review paper discussed how researchers study infants and what they have found about the core knowledge that is available in infancy. Because infants cannot talk to researchers or express what they are thinking, developmental scientists and cognitive neuroscientists have benefitted from neuroimaging techniques and study paradigms in exploring their minds. The literature reviewed here suggests that babies are smarter and more capable than generally believed. Even when it seems like they do not know anything about what is happening around them, they may be actively creating representations about the world, acquiring new information, and making predictions about the events and objects surrounding them. The core knowledge implemented in infants’ minds seems to serve as solid starting points, allowing them to learn from the physical and social world more efficiently and effectively.

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References [1] Spelke, E.S., & Kinzler, D.K. (2007). Core Knowledge. Developmental Science, 10, 89-96. [2] Baillargeon, R., Spelke, E.S., & Wasserman, S. (1985). Object permanence in 5.5-month-old infants. Cognition, 20, 191–208. [3] Johnson, S.P. (2004). Development of perceptual completion in infancy. Psychol Sci, 15, 769– 775. [4] Fantz,.R. (1964). Visual experience in infants: Decreased attention to familiar patterns relative to novel ones. Science, 146, 668–670. [5] Aslin, R.N. (2007). What’s in a look? Dev. Sci, 10, 48–53. [6] Cohen, L.B. (1976). Habituation of infant visual attention. In Habituation: Perspectives From Child Development, Animal Behavior, and Neurophysiology, T. J. Tighe and R. N. Leaton, eds (Hillsdale, NJ, Lawrence Erlbaum), 207–238. [7] Olson, G.M. (1976). An information-processing analysis of visual memory and habituation in infants. In Habituation. Perspectives From Child Development, Animal Behavior, and Neurophysiology, T. J. Tighe and R. N. Leaton, eds (Hillsdale, NJ, Lawrence Erlbaum), pp. 239–278. [8] Kagan, J., & Lewis, M. (1965). Studies of attention in the human infant. Merrill Palmer Q, 11, 95–127. [9] Xu, F., & Spelke, E.S. (2000). Large number discrimination in 6-month-old infants. Cognition, 74, B1-B11. [10] Baillargeon, R. (1987). Object permanence in 3.5- and 4.5-month-old infants. Developmental Psychology, 23, 655–664. [11] Baillargeon, R. (1993). The object concept revisited: New directions in the investigation of infants’ physical knowledge. In C. Granrud (Ed.), Visual perception and cognition in infancy: Carnegie Mellon Symposia on Cognition, pp. 265–315. [12] Stahl, A.E, & Feigenson, L. (2017). Expectancy violations promote learning in young children. Cognition, 163, 1-14. [13] Stahl, A.E, & Feigenson, L. (2015). Observing the unexpected enhances infants’ learning and exploration. Science, 348, 91–94. [14] Weisberg, D.S, & Sobel, D.M. (2012). Young children discriminate improbable from impossible events in fiction. Cognitive Development, 27, 90–98. [15] Moore, M.K, & Meltzoff, A.N. (1999). New findings on object permanence: A developmental difference between two types of occlusion. Br J Dev Psychol, 17, 623–644. [16] Baillargeon, R. (1987), Object permanence in 3½- and 4½-month-old infants. Developmental Psychology, 23, 655–664. [17] Spelke, E.S. (1990). Principles of object perception. Cognitive Science, 14, 29–56.

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[18] Spelke, E.S, Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge. Psychological Review, 99, 605–632. [19] Mitra, A., Snyder ,A.Z, Tagliazucchi, E, et al. (2017). Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness. PLoS One, 12, e0188122. [20] Damaraju, E. et al. (2014). Functional connectivity in the developing brain: a longitudinal study from 4 to 9 months of age. Neuroimage, 84, 169–180. [21] Zhong, H., Shen, D., & Lin, W. (2019). Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts. Neuroimage, 185, 664-684. [22] Fransson, P., Skiöld, B., Horsch, S., Nordell, A., Blennow, M., Lagercrantz, H., & Aden, U. (2007). Resting-state networks in the infant brain. Proc Natl Acad Sci U S A, 104, 15531-15536. [23] Biagi, L. et al. (2015). BOLD response selective to flowmotion in very young infants. PLoS Biol, 13, e1002260. [24] Deen, B. et al. (2017). Organization of high-level visual cortex in human infants. Nat. Commun, 8, 13995. [25] Xie, W., & Richards, J.E. (2016). Effects of interstimulus intervals on behavioral, heart rate, and event-related potential indices of infant engagement and sustained attention. Psychophysiology, 53, 1128–1142 [26] Ellis, C.T, & Turk-Browne, N.B. (2018). Infant fMRI: A Model System for Cognitive Neuroscience. Trends Cogn Sci, 22, 375-387. [27] Doria, V. et al. (2010). Emergence of resting state networks in the preterm human brain. Proc. Natl. Acad. Sci. U. S. A., 107, 20015–20020. [28] Merchant, N et al. (2009). A patient care system for early 3. 0 Tesla magnetic resonance imaging of very low birth weight infants. Early Hum. Dev, 85, 779–783. [29] Baxter, L., Fitzgibbon, S., Moultrie, F., Goksan, S., Jenkinson, M., Smith, S., Andersson, J., Duff, E., & Slater, R. (2019). Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants. Neuroimage, 186, 286-300. [30] Kouider, S. et al. (2015). Neural dynamics of prediction and surprise in infants. Nat. Commun, 6, 8537. [31] Doesburg, S.M. et al. (2013). Region-specific slowing of alpha oscillations is associated with visual-perceptual abilities in children born very preterm. Front. Hum. Neurosci, 7, 791. [32] Dale, A.M, Liu, A.K, Fischl, B.R, Buckner, R.L, Belliveau, J.W, Lewine, J.D, & Halgren, E. (2000). Dynamic statistical parametric mapping: combining fMRI and MEG for highresolution imaging of cortical activity. Neuron, 26, 55-67. [33] Liu, S., Tomer, D., Tenenbaum, U.J.B., & Spelke, E.S. (2017). Ten-month-old infants infer the value of goals from the


Finding Waldo: Two Routes for Processing Visual Search in Complex Scenic Images

by Andrew Hwang Abstract:

Visual search is a complex process that involves the separating of distinct features of a complex scene in order to identify a target. Visual search involves both bottom-up and top-down processes that interact to allow the identification of a target. Here, the concept of visual search is reviewed and both the bottom-up and top-down processes are described in detail. In addition, the reasons as to why humans excel at visual search when compared to computer systems is explored.

Main Body:

Imagine that you are facing the arrivals door at the John F. Kennedy International Airport, peacefully waiting for an old school friend who is visiting you for summer vacation. After the plane lands and the arrival door opens, your surroundings suddenly become crowded, filled with people walking in and out, looking for someone, greeting one another, and rushing to baggage claim. As people crowd the greeting area in front of the arrivals gate, your eyes and brain become overwhelmed searching for your friend, attempting to recall the way he looked at the last moment you saw him, and guessing how he may have changed. This activity, scanning the environment to search for something important, is defined as “visual search” in the fields of cognitive psychology and neuroscience. Visual search represents a

Figure 1: Real Life Visual Search Some of the real-world examples of visual search, including looking for a carton of milk in the grocery market, detecting a gun on a security monitor, and finding Waldo in the book.

Art by Lillian Kong

fundamental psychological and perceptual process that occurs in various contexts of everyday life (e.g., when your mom picks out a specific brand of milk on a supermarket shelf, when a TSA officer attempts to detect weapons or dangerous objects in an x-ray image, or when you are reading “Where’s Waldo?”; see Figure 1 for visual illustrations). Anything that is currently important to your goal (e.g., picking up your friend at the airport) is your “search target” (e.g., your friend), which must be identified among all “distractors” (e.g., the other objects and people in the environment). Our ability to detect or to locate a target amongst many distractors during various visual search tasks has been extensively studied over the past 40 years. Figure 2 illustrates a typical trial sequence for a visual search task that has been used in many research studies. First, a search target is defined (e.g., a red letter “T”); then, a human observer scans a visual search array that contains multiple items. The search array sometimes contains the target (i.e., present trials) and sometimes it does not (i.e., absent trials). Typically, the human observer is tasked with reporting whether or not the target item is located among the visual array. While the human observer completes the task, some researchers measure search accuracy [1]; others measure response time (RT; [2]); and the rest monitor and explore the eye movement patterns and gazes of the observers during these tasks [3]. These measurements allow researchers to study and better understand the perceptual and cognitive processes that underlie visual search. By using these behavioral assessments, it is proven that humans are genuine experts at visual search, even when presented with extremely complex natural scenes, and can still outperform state-of-the-art computer detection systems [4]. How is this high performance achieved? How do our brains solve the Where’s Waldo problem so easily and efficiently? A minimum of two different processors in the brain guide and allocate our attention. These two processors not only support different attentional mechanisms, but they also work together to facilitate visual search performance, resulting in a process that is more efficient and flexible than any existing computer vision or artificial intelligence (AI) program. Properly allocating attention towards a target location and suppressing additional distractors is critical to the success of visual search [5]. The combined functions of these two processors determine the outcome of any search. One of these search processors relies on bottom-up processing, which involves the real-time, data-driven, sensory analysis of information that is conveyed through 12 | JOURNYS | SUMMER 2020


Figure 2: Visual Search Array An example of an experimental sequence for testing the ability to identify a visual target from a search array. In the experimental settings, a human participant is asked to find a target (defined by the experimenters, in this example, a red letter “T�). The search array sometimes includes the target and sometimes does not, and the participant is asked to indicate whether the target was present or absent among the search array. Experimenters tend to measure the speed and accuracy of participant responses.

the retina to the visual cortex and other (higher) brain areas and results in a representation of an object being formed in our minds [6]. Bottom-up processing plays roles in capturing and allocating our attention towards a target item that is unique, distinct, or salient. Let’s consider the airport example again. If your old friend is the only one who is wearing a green shirt among others in yellow shirts (Figure 3A), you will be able to spot him easily and quickly (almost automatically and immediately). Your attention will be directed to the unique color by default, allowing you to recognize that he is the person you are looking for. Alternatively, if he is wearing a very bright, neon green shirt, saliency would result in your attention being attracted towards him through bottom-up processing, even if he was not the only person in a bright, neon green shirt (Figure 3B). Figure 4 lists the factors that have been determined to guide the bottom-up processing during various visual search tasks based on previous research studies, along with relevant citations and visual illustrations. Relying on the principles that underlie bottom-up processing, recent mathematical and probabilistic models designed for AI and computer vision have successfully stimulated and even out-performed humans during the detection of a pop-out target in various visual search tasks [7]. The other search processor relies on top-down processing. Top-down processing utilizes what the brain already knows about objects, scenes, concepts, contexts, and the environment to create a more efficient understanding based on this information. For example, your visual search can be guided by what you know about the world described in the search array. 13 | JOURNYS | SUMMER 2020

Figure 3: Visual Search Airport Example Visual illustrations demonstrating how bottom-up and top-down processing would work in the airport example. In panel A, the person in the green shirt will attract your attention first because it represents a unique feature. In panel B, the person in a neon green shirt will pop out because of its saliency. In panel C, your expectations regarding the color of the shirt that your friend is wearing will guide your attention, allowing you to focus on and search only for blue shirts, filtering out other colors.

When you are asked to locate where a person is in Figure 5, you are likely to first look near the door and the bench on the ground and to scan these spots more often and more carefully (with more frequent eye gazes), rather than looking in a tree or at the sky. Here, you are applying your general knowledge that people tend to sit on a bench or walk through doors on the ground and rarely fly or sit in trees. Conversely, when you are asked to locate a bird in the same image, your attention will most likely be drawn to the sky, a tree, or the bush,

Figure 4: Bottom-up Examples and References Visual illustrations of bottom-up factors and relevant papers that reference each example.


rather than the bench or the door, and you will spend more time scanning these based on your reasonable expectation and prior knowledge about birds. Sometimes, you can learn new information about the characteristics of a target item and utilize this information to find the target faster. Returning to the airport example, if your friend informed you that he will be wearing a blue shirt, then you would be able to focus on people who are wearing blue shirts and skip scanning those who are not in blue to facilitate the search and terminate the process faster (Figure 5C). In addition to prior knowledge and expectations, various other factors can effectively guide topdown processing during visual search, which are summarized in Figure 6. Compared with existing AI systems, the human brain appears to be much better at utilizing and combining factors associated with top-down processing, even before most of the objects and details within a scene are recognized and consciously processed [4]. Thus, top-down processing represents a domain in which machines, such as AI and computer vision systems, are currently incapable of simulating human performance. How can a computer utilize its past experience, maximize its motivation, and rely on its long-term memory of prior knowledge and expectations about the world? Addressing this question will help scientists and engineers bridge the gap between the human brain and machines during the performance of visual search in complex, natural scenes. Over the past 40 years, much research on visual search has been conducted, providing us with a good grasp of the mechanisms that underlie bottom-up and top-down processing and that allow human observers to locate a target item in complex scenes (whether it is your old friend in the airport scene, a bird in a forest, or Waldo in your book). The remarkable search abilities of the human brain are the result of attentional guidance mechanisms that rely on combinations of bottom-up and top-down factors [8]. Although computer vision and AI systems have achieved and surpassed the bottomup processing abilities of humans [7], the top-down processing factors, such as semantic knowledge, prior information based

Figure 6: Top-down Examples and References Some examples of top-down factors and relevant papers that reference each example.

on past experiences, and common sense regarding the external environment, in addition to motivational or emotional factors, are what allow humans to outperform machines during this important task. The visual search field is just beginning to understand how bottom-up and top-down processing interact with each other in real time and how this interaction can be effectively implemented to improve search performance in the computer vision and AI systems and other practical applications. Future challenges for the field include understanding how the principles of attentional guidance driven by bottom-up and top-down processing can be expanded from processing two-dimensional images to processing immersive, dynamic, three-dimensional environments and how these processes can be utilized in practical applications that require outstanding visual search abilities, such as surveillance, security cameras, self-driving cars, and robots.

References

Figure 5: Visual Search Application in Natural Scene An example of visual search in a naturalistic scene. Our knowledge and expectations regarding the world guide our eye movements (indicated by colored circles on this image), which reflects our optimal strategy for effectively identifying a target.

[1] Wolfe JM. What Can 1 Million Trials Tell Us About Visual Search? Psychological Science. 1998;9(1):33-39. doi:10.1111/1467-9280.00006. [2] Schweizer K. Visual search, reaction time, and cognitive ability. Perceptual and Motor Skills. 1998;86(1):79-84. [3] Rayner K. Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology. 2009; 62: 1457-1506. [4] Wu CC, Wolfe JM. Eye Movements in Medical Image Perception: A Selective Review of Past, Present and Future. Vision. 2019; 3:32. [5] Luck SJ, Hillyard SA. Spatial filtering during visual search: Evidence from human electrophysiology. Journal of Experimental Psychology: Human Perception and Performance. 1994;20(5):1000-1014. doi:10.1037/0096-1523.20.5.1000. [6] Sobel KV, Gerrie MP, Poole BJ, Kane MJ. Individual differences in working memory capacity and visual search: The roles of top-down and bottom-up processing. Psychonomic Bulletin & Review. 2007; 14(5): 840-845. [7] Itti L, Koch C. A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research. 2000; 40: 1489-1506. [8] van Zoest W, Donk M. Bottom-up and top-down control in visual search. Perception. 2004; 33(8). 927-937.

14 | JOURNYS | SUMMER 2020


By: Michele Kim The Fine Line Between A.I.’s Bright Future and Doom Imagine being in the middle of an intense game of poker. After multiple games, a lot is at stake—you’ve put all your chips in and are close to getting a good hand. As you’re playing, one of the players calls your bluff. This opponent has more skill than any other player you’ve seen—because they’re a robot. Artificial intelligence has further advanced and two well-known automated intelligence systems, Libratus and Pluribus, are able to skillfully play one of the most complicated forms of poker: Texas hold’em. How is it possible that these systems are able to master the complex strategies needed to play this intricate card game? The most important component of playing poker is deciding which decisions would provide the best result. These machines are able to simulate billions of possibilities, and possibly even more, against itself and evaluate each move to decide which move would provide the greatest advantage over its opponent. Noam Brown dubs this process as “‘counterfactual regret minimization’” [1] and compares it to the way humans learn the game. “‘One player will ask another, ‘What would you have done if I had raised here instead of called?’”. The intelligence system is able to master techniques faster than most humans take to learn. Pluribus, for example, was able to train over the course of only eight days and beat multiple elite poker players such as Darren Elias, a renowned poker player who held a world poker title four times. The other key components, however, are very difficult for robots or machines to do. Bluffing and random behavior was added to Pluribus’ skill set. The more Pluribus played after that, the more it refined its skills; “Pluribus honed its initial strategy by playing against copies against itself, starting from scratch and gradually learning which actions helped it to win. Then, the AI uses that intuition for when to hold and when to fold during the first betting round of each hand against five human players” [2]. In addition to initial programming, the program had to practice with copies of itself in order to know what moves to make during the real poker matches. If any robot can play card games, what’s the significance of Pluribus and Libratus? For starters, the capabilities of Pluribus and Libratus are good for real-life situations besides poker. The robots are incredibly fast—they practice against themselves over the course of just eight days, play out millions of possibilities in minutes and at the end are able to master a challenging card game. Likewise, a similar methodology could be applied to cybersecurity. Michael Wellman, a professor of Computer Science and Electrical Engineering at the University of Michigan compares the concept of cybersecurity to poker: “‘[both] the attacker and the defender have limited knowledge of what each other is doing… [in essence,] they are playing games with each other’” [1]. Although cybersecurity is much more complex than Texas hold’em due to the lack of rules, researchers are trying to develop solutions that will work. 15 | JOURNYS | SUMMER 2020

II Art By: Alex Han

However, what are the risks of artificial intelligence? Just like a scene from iconic dystopian movies like Terminator or Star Wars, A.I. can be programmed to perform catastrophic actions, or develop a destructive method to reach its set goals. The Future of Life Institute specifies that AI weapons are “programmed to kill,” and “could easily cause mass casualties” in the wrong hands [3]. For example, an analogy that could be applied are the battle droids from Star Wars, which carry out the Separatists’ orders and are programmed to kill. Another instance would be when “...we fail to fully align the AI’s goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for” [3]. Take the advanced control system from Wall-E as an example. It wanted to provide the humans a safe haven to live, and, as a result, would not let the humans return to Earth. However, these dystopian images may not be possible until the distant future, when A.I. become smarter and more capable. Furthermore, these new systems’ expansive capabilities truly open up a new, promising pathway to a future in which artificial intelligence is faster, more developed, and widely used. However, this advantage must be used cautiously and responsibly in the future as A.I. grows more and more advanced. It’s surprising how poker-playing robots will be able to help research grow and give us endless possibilities— from killer androids to extremely advanced cybersecurity.

References [1] Metz, C. (2019). Hold ’Em or Fold ’Em? This A.I. Bluffs With the Best. [online] Nytimes.com. Available at: https://www.nytimes.com/2019/07/11/ science/poker-robot-ai-artificial-intelligence.html [Accessed 12 Dec. 2019]. [2] Temming, M. (2019). Artificial intelligence has now pretty much conquered poker | Science News. [online] Science News. Available at: https:// www.sciencenews.org/article/artificial-intelligence-has-now-pretty-muchconquered-poker [Accessed 12 Dec. 2019]. [3] Tegmark, M. (2019). Benefits & Risks of Artificial Intelligence - Future of Life Institute. [online] Future of Life Institute. Available at: https:// futureoflife.org/background/benefits-risks-of-artificial-intelligence/


by Prerana Pai Bhande

Psychiatric Factors Influencing Cervical Dystonia Abstract Cervical dystonia is a neurological disorder that has been described in medical journals since the 1500s. However, there have still been no definite breakthroughs made regarding its true causes and mechanisms. Clinicians have little information about the detection, treatment, and management of psychopathologic conditions in such neurologic diseases. This paper addresses how psychology is connected to cervical dystonia and how psychological aspects have a strong influence on the appearance of the involuntary tremors that result from the disorder. “The boundary between neurology and psychiatry is becoming increasingly blurred, and it’s only a matter of time before psychiatry becomes just another branch of neurology” -Vilayanur S. Ramachandran

Introduction The human neurological system is one of the most complex and sophisticated structures on Earth, consisting of an intricate network of neurons, forming an exceptional intelligence center in the brain. It mostly functions beyond consciousness and visibility, providing us with the capability to sense and perceive the world around us and process and react to it. Having several levels of processing, this system can be damaged in numerous ways. Neurotransmitter and neurohormone production can be affected, causing misfiring signals, emotional instability, or organ damage. Glial cells can get damaged, disabling the communication systems they support. Physical injuries can cause neural damage, and toxins can alter the delicate chemical balance. In addition, psychological factors can have a large influence on the neurological condition of the body.

Movement disorders are characterized by increased a b n o r m a l movements, which may be voluntary or involuntary. They can also cause reduced or slow movements. It includes conditions such as Huntington’s disease, atrophy, chorea and related disorders. These appear in copious, distinct ways across a wide age range. They can be classified on the basis of location and the number of regions stimulated, the type of movement caused, their neurological relevance, and the method of acquisition. These disorders are caused primarily due to genetic inheritance or severe brain trauma, including strokes. Treatments vary among syndromes, but concise cures for all of them are yet to be found. Medicines can cure some disorders, but, most of the treatments focus mainly on the alleviation of pain.

Cervical Dystonia Dystonia is “a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive, movements, postures, or both. Dystonic movements are typically patterned, twisting, and may be tremulous” [1]. It is a rare neurological disorder that originates in the brain. The underlying problems associated with dystonia include 16 | JOURNYS | SUMMER 2020


the over-activation of certain muscles unnecessary for movement and simultaneous activation of muscles that work against each other as a result of abnormal brain activity, not the muscles themselves. The course of dystonia depends on the severity and distribution of muscles involved. Dystonic movements may appear in the form of transient exaggerations of normal movements and can be slow, stiff, cramped, twisting or jerky. In extreme cases, dystonia manifests as odd postures that may become perpetual. Other than the common characteristic of overactive regions of movement, dystonia is quite clinically diverse in terms of manifestation and personal experience of the disorder. Cervical dystonia, also known as spasmodic torticollis, can be classified as focal dystonia, which means that the movement and its effect surround a localized region -- specifically, the neck and shoulder. The involuntary movements caused by cervical dystonia are turning (torticollis), tilting (laterocollis), flexion (anterocollis) or extension (retrocollis) of the neck. Similar conditions can also appear in an arm, in which case it is known as segmental dystonia, with two continuous regions. Most cervical dystonia is idiopathic, having no specific underlying cause, except for a potential genetic susceptibility or environmental factors, as found in 10-25% cases [2]. It is also undetectable by Magnetic Resonance Imaging (MRI) aside from cases with nerve irritation or compression. It is believed to be related to damage to the basal ganglia in the brain, an integral part of motor functioning and the motor cortex. However, no concrete evidence regarding the cause of dystonia or the functioning of the basal ganglia has been discovered as of yet. The system of movement includes the cerebral cortex, the striatum, substantia nigra, the brain stem, and the thalamus. The current hypothesis states that the circuits in the basal ganglia promote or inhibit activity in the motor cortex by way of two pathways -- direct and indirect -- whose coordinated functioning leads to ordinary motor operation. The common cures presently employed by doctors are symptomatic and are intended to relieve spasms, pain and disturbed postures or functions. The usual option for treating cervical dystonia is the injection of botulinum toxin, which prevents the muscle from releasing the neurotransmitter Acetylcholine, preventing the over-activation of neurons and involuntary movement. Dopaminergic agents, anticholinergic agents, baclofen and clonazepam are the most frequently used oral medications. In severe cases, even surgical procedures are prescribed. However, no cure that targets a root cause or has a certain success rate has been found.

Neuropsychiatry Neuropsychiatry is the branch of juncture between neurology and psychiatry. It deals with the interface of behavioral phenomena driven by brain dysfunction. 17 | JOURNYS | SUMMER 2020

Studies have made it clear that almost all neurological diseases have a high frequency of psychological indicators. Psychiatric disturbances resulting from illness may range from affective conditions (depression, mania) to cognitive impairments (dementia, milder cognitive syndromes) to perceptual disorders (hallucinations, delusions). These disturbances typically run parallel to classical neurologic symptoms and may cause disability and impair the quality of life as much as or even more than the neurological ones. Many major neurological syndromes, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and epilepsy, all have large correlations with psychological illnesses. Depression, anxiety disorders (Generalized Anxiety Disorder, ObsessiveCompulsive Disorder, etc.), apathy, dementia, Involuntary Emotion Expression Disorder (IEDD), substance abuse and addiction are all associated with neurological disorders. These may interfere with rehabilitation and cause further issues with recovery. A common theme discovered is that there appear to be consistent links between specific types of psychopathology and specific brain areas no matter the pathology of the disease, and that regardless of the cause, these disorders lie in definable groups. [3] Historically, dystonia has been perceived periodically both as a neurological and psychological disturbance. To support the psychiatric view of dystonia, it was argued that involuntary movements might be a result of a situation putting weight on the psyche of an individual -- for example, a psychoanalytic interpretation for a woman with cervical dystonia whose symptoms involved turning her head to the right might have been that she was conflicted about looking at the woman working to her left because that woman’s husband was having an extramarital relationship with the patient. A change in the paradigm occurred in the International Symposium for Dystonia in 1975. There, it was confirmed that dystonia was a result of a movement disorder, not a functional or psychiatric condition, and was not caused by psychogenic factors. Subsequently, new data proved that comorbid psychiatric disturbances can cause significant morbidity and reduce the quality of life independent of the motor aspects of dystonia. The severity of psychological conditions can be established by the usage of the Structured Clinical Interview for Diagnosis and Statistical Manual (SCID), or by patient or clinicians rated scales to gauge the prevalence of the psychiatric disturbance. Further studies regarding cervical dystonia proved that the probability of a patient suffering from dystonia being diagnosed with a psychological illness was much larger than that faced by the rest of the population. [4] Looking at particular diagnoses associated with cervical dystonia, the pervasiveness of major depression is increased, as is the risk for meeting diagnostic criteria for anxiety disorders, especially social phobia and panic disorder. [5] This indicates a correlation between mental health and cervical dystonia.


Patients with dystonia generally have more personality traits of amicability and conscientiousness than the average person but fewer traits of openness, which contributes to social phobia. These attributes are seen as long-term predispositions that do not change significantly after late adolescence or early adulthood and are therefore likely to be present prior to the onset of dystonia. This suggests that the link between psychiatry and dystonia is not just a result of physiological factors, but also with other experiences and hardships associated with it. and hints at a greater explanation for this phenomenon. In 1998, Wenzel et al. proved that 53% of the time, mood disorders preceded the onset of the movement disorder, exhibiting that the correlation was not just a consequence of hardships of living with the disease. Findings suggest that subjectively experienced stress and self-consciousness, as well as physical factors, all aggravate the motor symptoms of cervical dystonia, proving a relationship between dystonia and physical factors as well. However, overestimating the effect that psychological disturbances have on dystonia have led to wrong diagnosis and exacerbation of pain and symptoms in the past. Thus, cervical dystonia and related psychological morbidities have a profound impact on the quality of life. It is necessary to advance research in this direction, to lead to a greater understanding of the functioning and better treatments.

Personal Observations Case Observations The subject of the case study is a 45-year-old male, H.P, diagnosed with cervical dystonia 10 years ago. Coupled with social anxiety and phobia, typical of such cases has caused significant disruption to quality of life. Over the past couple of years, having observed the factors affecting, aggravating and alleviating his condition, it can be

noted that specific factors influence his state of mind and physical involuntary movements in different ways. Introspection and discussion revealed that travelling to foreign countries has a startlingly positive result on his psyche, leading to an increase in confidence and a liberating effect on his personality and behavior. He transforms from an introverted camera- shy individual to an exuberant, active one. Not knowing the people around him, gives him anonymity regarding his dystonia, and relief from social consciousness. Getting a break from the stresses and responsibilities of everyday life makes a huge difference, leaving him calmer and happier. This state of mind can also be correlated with physical well-being. He reports suffering from noticeably fewer tremors and less pain in the neck region. Being around people he is comfortable with, serves to reduce the involuntary movement, and generally brighten his mood. In contrast, there is also a marked increase in the amount and severity of tremors and involuntary movement, with great pain during the following days in the presence of large crowds. H.P has been prescribed various medications, only some of which have proved to assist his recovery which have both assisted and hindered his recovery. The most effective short term treatment was the injection of botulinum toxin in the cervical region. An initial misdiagnosis and recommendation of psychotic drugs caused what had begun as mild writer’s cramp to worsen into cervical dystonia. One of the medications that was inadequate was levodopa, a drug that increases levels of dopamine in the brain to potentially help the improvement of dystonia. Therefore, the fact that travelling and being around familiar figures close friends helps his condition may not be due to to the fact that such calming activity causes the consequent automatic release of dopamine by the hypothalamus in the parts of the brain like the substantia nigra, which is associated with basal ganglia, suspected to have an impact on cervical dystonia. It is thus suggested that the influence of such positive and calming factors can control the condition beyond just the release of neurotransmitters such as dopamine. Upon further research, such effects can give deeper insight into the mechanism of spasmodic torticollis and help introduce advancements in the search for better treatments.

General Data Collection To analyze the effects of surroundings on the mood of a person not suffering from dystonia, a survey was conducted for people between the ages of twenty to eighty, with most 18 | JOURNYS | SUMMER 2020


participants being around the age of forty-five, the age range of the case mentioned above. The sample space of the survey was hundred participants. All participants were undiagnosed with any psychological disorders and were neurologically healthy. The questions included in the survey were: 1. How old are you? 2. How would you describe your average mood? 3. In comparison to those around, how stable is your state of mind/ mood? 4. How often do the people around you affect your mood? 5. How often does your location affect your mood? 6. On an average, how do these factors influence you? 7. What factors around you best impact you positively? 8. What factors around you impact you most negatively? The results of the survey are as follows. The answers were quite varied, showing only the expected trend of answers. There was neither any evident reticence towards social behavior nor a significant influence of surroundings on the mind. When asked the same questions, H.P depicted a definite increased tendency to associate social behavior and gatherings and being around people whose presence causes awkwardness on his part, with negativity. His mood is reported to be widely influenced by the people around him. This corroborates with the experiences described by various other dystonia patients, and similar studies conducted by neurologists,. This alluding to a greater meaning behind this relationship. and is worth considering during research. He also chose travelling as an activity that he associated with positivity. Travelling gives him an opportunity to leave all familiarity behind, boosting the condition of his dystonia, and , which is said to have has an overall therapeutic effect on him.

Conclusion Among patients with movement disorders, the probability of having dystonia is over 25%. Living in a part of the world with limited facilities to reach other patients suffering from dystonia, the sample space used was not very large. However, it is prudent to study more about the psychological aspects of dystonia, a syndrome still shrouded in mystery, despite the large number of people suffering from it. There is evidence suggesting a strong interplay between the neurological and biological expression of the syndrome and the psychological status of the individual. The social and physical consequences of having dystonia are debilitating and any possible treatments that can be formulated will cause great improvements in the lives of many.

References [1] Albanese A, Bhatia K, Bressman SB, et al. Mov Disord. 2013 [2] DeLong MR. Cervical Dystonia (Spasmodic Torticollis). NORD Guide to Rare Disorders [3] Constantine Lyketsos, Nicholas Kozauer, Peter Rabins Dialogues Clin Neurosci. 2007 Jun; 9(2): 111–124, www.ncbi.nlm.nih.gov/pmc/articles/PMC2687521/ [4] Social phobia in spasmodic torticollis. Gßndel H, Wolf A, Xidara V, Busch R, Ceballos - Baumann AO J Neurol Neurosurg Psychiatry. 2001 Oct [5] Ozel-Kizil ET, Akbostanci MC, Ozguven HD, Atbasoglu EC. Secondary social anxiety in hyperkinesias. Mov Disord. 2008 19 | JOURNYS | SUMMER 2020


The Anti-Vaccination Movement:

Paranoia or Necessity? By: Bill Yildez

II

Art By: Seyoung Lee

Introduction

Vaccines have been one of humanity’s most essential tools for countering various dangerous diseases. Although their safety has been questioned throughout history, vaccines have proven themselves to be useful time and time again. Vaccines have eradicated extremely deadly diseases and suppressed many others. Most recently, another movement denying the effectiveness and safety of vaccines has emerged. This current anti-vaccination movement has swept the world through the media and has significantly prohibited the efficacy of vaccines.

History of Vaccines

While vaccines are beneficial now, early inoculations (or controlled administration of a disease) worked properly to build immunity for a patient most of the time but were dangerous for those administering the inoculations. This led to an uproar about the safety of vaccinations until Edward Jenner discovered the smallpox vaccine and ushered in the modern era of immunity building [4]. Vaccines have ceased many epidemics. In the past, the majority of the population would perish from diseases such as smallpox, measles, and pertussis. Although some diseases have been completely exterminated, such as smallpox, others, such as measles, still pose a threat to the United States and other developed countries [2]. These nations must maintain a healthy percentage of vaccinations to achieve herd immunity to prevent the resurgence of these diseases.

Vaccine Effectiveness and Risks

Vaccines have been exceedingly effective at preventing diseases. However, herd immunity is necessary for vaccinations to be efficacious. Herd immunity is the threshold of population percentage that vaccines require to be effective. For most diseases that are not extremely contagious, approximately eighty to ninety percent of the population must be vaccinated for herd immunity to be in play, while measles requires ninety-five percent immunization to be completely effective [10]. Furthermore, studies have shown that current vaccines are also very safe. The risks of the Measles, Mumps, Rubella vaccine (MMR) causing adverse side effects is astronomically lower than the risks associated with measles, mumps, and rubella. Without the vaccine, fifteen percent of kids who contract one or multiple of the three diseases will suffer severe symptoms such as pneumonia, measles croup, and fever-induced convulsion. In contrast, these side effects are nonexistent for people who are vaccinated [3]. Vaccines have been proven to be effective at preventing diseases

while also being one of the safest and cheapest methods of doing so. Perhaps Paul Offit says it best, “When you choose for your child not to get a vaccine, it’s not a choice that you’re making for yourself alone... You’re making that choice for other people who are near you who may be too young to be vaccinated, or who are getting chemotherapy for their cancer or are getting immune-suppressive therapy for their transplants.” [8]. The people who are protected by vaccines depend on all others to get vaccinated as well; without herd immunity, even the vaccinated would be susceptible to diseases, and nearly everyone could possess a risk to people with compromised or weak immune systems.

Contemporary Movements Against Vaccines and Causes

Recently, the anti-vaccination movement has become widespread through exploiting misinformation and personal beliefs. These trends in disbelief have been continuously gaining ground by television shows and social media. One commonly held belief by people who oppose vaccines is that the United States Government forces children to be vaccinated. This is false, vaccinations are only required for public schools, and there are religious or personal exemptions in most states [1]. False ideas like these continue spreading due to the lack of communication between people with different outlooks regarding vaccines. For example, people who question or oppose vaccines tend to discuss their stance solely with other people who share their beliefs. These misconceptions result in fear of outside ideas and skewed viewpoints and beliefs. Not only this, but opponents of vaccines can also select specific information from journal articles and other sources. Anyone in these “echo chambers” can spread the information about vaccines that they wish to, even people who have malicious intentions [7]. Endorsements by famous figures such as Jenny McCarthy and Oprah are another reason behind the rapid spread of these ideas. Both Jenny McCarthy and Oprah have openly questioned vaccines while also spreading misinformation and fear into parents’ minds. 20 18|| JOURNYS JOURNYS || SUMMER SUMMER 2020 2020


Andrew Wakef ield

The most notorious proponent of the anti-vaccine trend in the past decades has been Andrew Wakefield. In an article from the Lancet, he stated that there was a link between the MMR vaccine and autism. Not only was the autism link nonexistent, but Wakefield also profited from his research [8]. Later, he was denounced and stripped of his medical license, but the damage of Andrew Wakefield’s false information was already in effect; vaccination rates decreased across many western countries. For example, the vaccination rate used to be ninety-two percent in the United Kingdom, but within the year 2003 alone, it dipped down to as low as sixty-one percent. Again, the herd immunity threshold for measles is a distant ninety-five percent as a reference [6]. After all of this, Andrew Wakefield has become a celebrity in the anti-vaccination community, gaining traction among politicians and other celebrities [4]. He is a perfect example of an anti-vaccination movement or idea that was proven to be wrong multiple times, yet still is very popular among the community.

The Effects on the Population

The reluctance to vaccinate diminishes the entire population’s immunity to a disease. Many examples of once-forgotten diseases have begun a resurgence due to the anti-vaccination movement. There are many examples of outbreaks caused by a decrease in vaccination rates. Although Measles was once thought to be eliminated in the United States, there have been multiple outbreaks in recent years [9]. States such as Washington have declared states of emergency amid a measles outbreak. According to Bridget Farham, “Between 1979 to 1996 Sweden declared a moratorium against whooping cough vaccination, during which time 60% of all children contracted the disease before the age of 10.” [4]. The mortality of Measles builds on the concern regarding these statistics. According to the NCIRS, many complications of measles can lead to hospitalization or even death in rare cases [3]. The refusal to vaccinate each individual may not have an immediate effect on the general health of the population, but it will gradually build up to undermine the herd immunity countries have spent generations building.

Battle Against Anti-Vax

Now the question arises of how to stop a movement in which severe misunderstanding from one side has left the other feeling confused and therefore opposing vaccines. First of all, when having a conversation with someone against vaccines, one should not bash them. Everyone has their personal biases, and it is in everyone’s interests to understand where they are coming from. It is essential to listen to different perspectives since everyone has a different background. One must try to be respectful and kind to have any chance of changing another’s thoughts [5]. 21 | JOURNYS | SUMMER 2020

Conclusion

The modern anti-vaccination movement has been a perfect storm of false journal articles, along with cherry-picking of misinformation and hidden agendas. This has negatively impacted the world as a whole, bringing back diseases that doctors may not be fully trained for. To change the mind of people against vaccines, one must be respectful and thoughtful of all arguments and remember to use reliable sources. If the current COVID-19 pandemic is putting the whole world at risk because of the lack of one vaccine, Could you imagine what would be the scenario of a world without any? As said by Roy Benaroch, “Vaccines aren’t about protecting just you, or your children, or just the person who gets the vaccine. Vaccines are about protecting all of us, even the babies, and the ill, and the unlucky few in whom vaccines don’t work. We’re all in this together.” [1].

References [1] Benaroch, R. (2014, March/April). Vaccine myths. Pediatrics for Parents, 10+. Retrieved from Health & Wellness Resource Center database. [2] Centers for Disease Control and Prevention. (2018, June 29). Vaccines: Vac-Gen/What Would Happen If We Stopped Vaccinations. Retrieved from https://www.cdc.gov/vaccines/vac-gen/whatifstop.htm [3] Comparing risks - Measles | NCIRS. (n.d.). Retrieved from http://www. ncirs.org.au/mmr-vaccine-decision-aid/comparing-risks-measles [4] Farham, B. (2019). Anti-vaxx--wilful ignorance or misunderstanding? SAMJ South African Medical Journal, 109(4), 197. http://dx.doi.org/10.7196/ SAMJ.2019.v109i4.14034 [5] Fischer, K. (2019, February 11). What You Believe about “Science Denial” May Be All Wrong. Retrieved from https://www.the-scientist.com/news-opinion/ opinion--what-you-believe-about-science-denial-may-be-all-wrong-65448 [6] Hussain, A., Ali, S., Ahmed, M., & Hussain, S. (2018). The anti-vaccination movement: A regression in modern medicine. Cureus. https://doi.org/10.7759/ cureus.2919 [7] Joubert, M., & Schalkwyk, F. V. (2017, February 13). Why anti-vaccine beliefs and ideas spread so fast on the internet. The Conversation. Retrieved from

http://theconversation.com/why-anti-vaccine-beliefs-and-ideas-

spread-so-fast-on-the-inte rnet-111431 [8] McEnery, R. (2011). Research that sparked anti-vaccination campaign called an "elaborate fraud". Vax Report, 09(No. 01). Retrieved from https:// www.vaxreport.org/vax-january-2011/561-research-that-sparked-anti-vaccination- campaign-called-an-elaborate-fraud [9] Phadke, V. K., Bednarczyk, R. A., Salmon, D. A., & Omer, S. B. (2016). Association between vaccine refusal and vaccine-preventable diseases in the united states. JAMA. https://doi.org/10.1001/jama.2016.1353 [10] Vanderslott, S., & Roser, M. (n.d.). Vaccination. In Our world in data. Retrieved from https://ourworldindata.org/vaccination#how-vaccines-work-herd-immunity-and-reasons-for-caring-about-broad-vaccination-coverage


QUANTUM COMPUTING TODAY by katherine izhikevich ABSTRACT

I.INTRODUCTION

Although quantum computing may seem like a distant concept only present in sci-fi thrillers, technological breakthroughs have spurred quantum computing to popularity in the world of modern science. Although it is not nearly at its full potential, the computer has started to appear at some of the largest tech conventions in the world, such as the Consumer Electronics Show (CES). In this article, we will cover the mathematics, physics, and chemistry behind quantum computing, compare quantum computers to regular computers, and discuss the abilities of recent state-of-the-art quantum computer models.

In essence, a problem presented to a regular computer is in the form of an algorithm. The computer executes the exact steps provided by the algorithm and produces a unique solution. A quantum computer, however, follows a different approach. Instead, they are able to properly understand a problem, calculate every outcome at once, and declare the correct answer at an incredibly fast rate. For example, cryptography is a field where quantum computers may prove to be very useful. If a regular computer were to crack a passcode with parameters such as length and character restrictions, it would process every single passcode from a list to find the correct one. A quantum computer would be able to try every possible outcome within the parameters simultaneously and finish computing almost instantly.

II. QUANTUM MECHANICS The existence of quantum computers is made possible by a complex topic of physics known as quantum mechanics. Quantum mechanics refers to the study of quantum electrons that display unusual and counter intuitive qualities. Electrons do not follow the consistent pattern of a simple, circular orbit basic molecular chemistry teaches. Instead, only their general positions can be calculated. Figure 1 provides a visual, where the darker blue areas represent a higher probability of an electron’s presence [3].

Fig. 1. Electron Probability Distribution (Reproduced from [9])

Why can’t we map an electron’s exact position like science courses in elementary school taught us? The answer lies in the concept of superposition and entanglement in quantum mechanics. Superposition is the theory that an electron can spin up on its axis (i.e. counterclockwise), spin down (i.e. clockwise), or both. The “both” state, or the superposition of an electron, is what makes it difficult to define the way an electron is moving because this property is unobservable. When the electron is not examined, it is neither spun up nor down, but both (see Schrodinger’s Cat) [3]. While a regular computer uses only ones and zeros called bits, a quantum computer includes the superposition of both called qubits that can be utilized to solve harder problems [5]. Figure 2 shows a theoretical representation of a qubit. 22 | JOURNYS | SUMMER 2020


III. QUANTUM GATES Regular computers use logic gates to perform operations and compute the outcome of algorithms. Just as they exist in the real computers, logic gates exist in quantum computers as well, called quantum gates. In regular computers, logic gates take inputs in a binary form, and form a single output [5]. These gates, all combined, form circuits which eventually form entire computers. Quantum gates are similar but contain some key differences including: superposition, entanglement, and operating on qubits instead of classical bits. Quantum gates are represented through matrices of dimensions 2^n x 2^n where n is the number of qubits [5]. Rather than computing logic on binary numbers, quantum gates tell the electrons which way to spin. Figure 3 shows a list of the most common quantum gates and their matrix representation. Fig. 2. Spherical Representation of a Qubit Fig. 3. Sample Quantum Gates

The Pauli-X gate is a pi-rotation around the X-axis, meaning it generally turns the spin up state |0 > of an electron into a spin down state |1 > and vice versa. The Pauli-Y gate is a pi-rotation around the yaxis. The Pauli-Z gate is a pi-rotation around the Z-axis. The Hadamard gate maps X to Z and Z to X. [6] The Hadamard gate is the quantum computing embodiment of superposition: it places an electron in a superposed state— both spin up and spin down.

IV. QUANTUM CODING Recently, IBM launched a service that allows anyone to run their code on a quantum computer from their personal computers using a cloud platform. The code can be written in Python, or the representation can be changed so that anyone can code through “dragging and dropping” logic gates. These logic gate represent different operations one can compute on a qubit, or a quantum-bit. In figure 4, a sample of the alternative ”drag and drop” coding interface is shown. The blue H gate stands for a Hadamard Gate, which places an electron in a superposed state— both spin up and spin down. The pink gate with the timer is a measurement gate which is used to measure whether the output of the code is a 0 or 1. 23 | JOURNYS | SUMMER 2020

Fig. 4. Sample IBM Composer


The results (Figure 5) of running code on a quantum computer looks very different compared to a regular computer. A regular computer provides one unique solution, but a quantum computer provides a distribution of answers. This is due to quantum computing’s non-determinate nature, as discussed in Section II. The results of running the ”code” in Figure 4 are shown in Figure 5. The histogram shows 4 different outcomes: 00000, 00001, 00010, and 00011. Each result shows 5 digits because the code was run on a 5 qubit computer. The percentages above each graph in the histogram represent the probability of each outcome at the moment the measurement gate measures the output of the code. The number of shots for this experiment, or the number of times the computer measures the results, is 1024. This means, for example, the quantum computer measured 00000 50.879% of its 1024 shots. Fig. 5. Sample Results

V. QUANTUM NEWS In October of 2019, Google’s Quantum experts hastily asserted that their quantum computer (Figure 6) with their chip, Sycamore, had achieved Quantum Supremacy by solving a problem — related to generating random numbers— in 200 seconds [1]. They claimed that IBM’s own Summit machine, supposedly the world’s best supercomputer, could only solve this problem in 10,000 years. IBM quickly refuted their claim and stated that it would only take 2.5 days for their computer (Figure 7) to solve the problem [1,2]. This was, and still is, all part of the contentious race to create a quantum computer that could eventually replace certain traditional computers that require high power computation.

Fig. 6. Google Quantum Computer with Sycamore chip (Reproduced from [7])

REFERENCES [1] Gibney, Elizabeth. “Hello Quantum World! Google Publishes Landmark Quantum Supremacy Claim.” Nature News, Nature Publishing Group, 23 Oct. 2019, www.nature.com/articles/d41586-019-03213-zref-CR1. [2] “Quantum Supremacy Using a Programmable Superconducting Processor.” Nature Magazine, 23 Oct. 2019. [3] Tro, Nivaldo J., et al. Chemistry: a Molecular Approach. Pearson Canada, 2020. [4] Nielsen, Michael A., and Isaac L. Chuang. Quantum Computation and Quantum Information. Cambridge University Press, 2010. [5] “Quantum Computing 101.” Institute for Quantum Computing, 14 June 2019, uwaterloo.ca/institute-for-quantum-computing/quantumcomputing101Superposition-and-entanglement. [6] Roell, Jason. “Demystifying Quantum Gates - One Qubit At A Time.” Medium, Towards Data Science, 28 Feb. 2018, towardsdatascience.com/demystifying-quantum-gates-one-qubit-atatime-54404ed80640. [7] https://cdn.technologyreview.com/i/images/38239092692922db65763o. jpg?sw=5000cx=0cy=0cw=2861ch=3813. [8] https://cdn.neow.in/news/images/uploaded/2019/10/1571721088ibm53-qubit-quantum-computer:jpg: [9] http://www.angelfire.com/falcon2/dirgni/00.jpg

Fig. 7. A classical IBM Quantum Computer (Reproduced from [8])

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BUILDING CONSCIOUSNESS IN ARTIFICIAL INTELLIGENCE:

I Think, therefore I am.

BY: CHARVIE YADAV Humans have been evolving for centuries, and selective evolution is key to human survival. As a species, humans learn from their bodies, senses, and consequences of their actions. The most beautiful thing that makes people who they are is the ability to have compassion. But what makes a being sentient? Is it the ability to feel pain, love, or is to reason and think? Is it an inherent quality or can it be acquired knowledge? Artificial intelligence and neuroscience experts believe that the complexity of our hormones, signals connecting our neurons, along with interactions in the real world, builds consciousness. “Empathy is the ability to recognize thoughts and feelings within another conscious being. It is a mental capacity in which we can understand the inner world of others — their beliefs, emotions, intentions, values, goals, and personal experience.” [1] The world today is shaped by the age of artificial intelligence. From chatbots to social media, the internet creates ideal human happiness, wants, and desires that do not exist in the real world. To understand AI, the understanding of its history is critical. From the first Dartmouth conference that theorized the idea of AI, the field has expanded a lot. IBM 7090, the most powerful computer (200,000 operations) was enough to help with the US Air Force ballistic system. Now, an iPhone alone can do 600 billion operations per second. A modern supercomputer? 30 quadrillion operations per second. A program is that which responds automatically to an input. Even with this limited definition, it plays more vital roles. Everytime a link is clicked or a recommendation is requested, it provides data for a bigger system. The AI revolution is now, because the data and the computing power to make actual sense of it is here. Each one of us is writing the future every single day. Now onto the interesting part: what if machines could be developed that had consciousness built inside them? It is known that the fundamental human qualities such as empathy, altruism and 25 | JOURNYS | SUMMER 2020

||

ART: AMY GE

compassion can be mirrored and acquired. This is because billions of these data acquiring events happen simultaneously as through speaking, walking, eating and thinking occur. There has always been an argument that AI cannot adapt to human compassion. This begs the question, “What if human compassion was acquired knowledge?” One such psychological study conducted by Harry Harlow in the United States reveals the importance of maternal contact. He took two cases, one where monkeys were separated from their mothers and another where they were raised by two inanimate surrogate mothers. The first surrogate was a simple construction of metal wires while the other surrogate also had a soft cloth. Harlow observed how infants spent more time with the soft inanimate object. This interaction with warmth and heat also revealed behaviour of the baby monkeys when faced with new and scary situations. His groundbreaking study revealed the importance of human touch. This raises two important questions: If babies choose comfort over nourishment, are babies equally comforted by a robot that provides warmth and comfort to the baby? And if they are, can there be robot caretakers? “Humans suffer from social isolation but react positively to physical contact. This has to do with the… Oxytocin hormone released in our body, which effectively reduces our stress levels” [2] Building upon the connection of human touch and sensations of warmth and love, possibly these same sensations can be produced by AI by activating the same parts of the brain that activates when with a loved one. If simulation bands that make the other person feel the individual’s heartbeat or sensation were available, AI could emulate the feeling one gets when having a loved one next to them. Perhaps, holding a robot’s hand may trigger similar neural simulation. Google is already ahead of the game. A recent article (NAME) discusses how Google is teaching an AI system to create more AI: AutoML. AutoML is the process by which neural networks can be built

without human intervention. This is possibly due to the breaking point of consciousness. Imagine by now if complex hormones can be generated by simulation bands, an automated machine learning system has been created and if a human could possibly build a human mind, then why can’t AI build another AI mind, assuming its accumulated the criteria to become conscious like humans. However, it could also play as a malfunction. Through epigenetics its been discovered that certain genes can be turned off and on. It is thus possible that overtime, our genes will adapt to this new wave of technology to completely dismiss reality. People always fear the thought of AI losing control and having what could be called “an intelligence explosion”. Society hates the idea of something more powerful treating us with disregard. Yes, AI could exceed intelligence through the virtue of speed alone (electronic circuits function hundred times faster than biochemical ones), but intelligence is neither the source of everything of value, nor does it safeguard everything valued. It is thought that intelligence is the grandest way to improve life. Why not make it something beyond intellect?

References [1] “Empathy and Perspective-Taking.” The Emotion Machine, www. theemotionmachine. com/empathy-andperspective-taking. [2] “The Human Touch: a Neglected Feeling.” NeuroNation, www. neuronation.com/ science/humantouch-neglectedfeeling.


TP MATH CLUB

Wednesday

Solve cool problems and study interesting topics with students of all ages and levels who enjoy math!

15 4to5

Members Per Meeting

Competitions Per Year

Odd

Rm

41

Even

Rm

47

13to18

Competitors Per Competition CREATED BY: AMY GE

26 | JOURNYS | SUMMER 2020


AN ANALYSIS OF GLOBAL ECONOMIC INDICATORS by Pratyush Seshadri and Abraham Goldstein art by Kevin Song There are many different metrics by which to measure the strength of an economy, but GDP is the strongest indicator if you are only looking at one metric. However, when you look deeper into GDP as an independent metric, there are many factors that affect the GDP of a nation itself. This study was conducted to identify what differentiates economies, based on various economic indicators, and also beneficial strategies for growth and stability for each type of economy. 27 | JOURNYS | SUMMER 2020


Through the various methods of statistical testing used above, it is clear that the independent variables chosen (population, corporate tax, inflation rates, and unemployment rates) affect the economic success of different international groupings. However, the effects of these factors are not standard across the globe, so each grouping of nations must be closely examined to determine which factors most affect its economic success and the reason behind their effects. It is worth noting that these factors are not completely unrelated to the GDP, but there is sufficient variation in these factors within groupings of nations with similar GDPs. Europe is largely regarded as economically successful, boasting an average GDP per capita of 42,054 USD. Among the European nations used in the study, population has a strong, positive correlation with GDP, inflation has a strong, negative correlation with GDP per capita, and unemployment has a strong, negative correlation with GDP change. GDP represents the total output of an economy. A larger population size would then increase the GDP, because it would both increase the size of the labor force, and create a larger demand for goods and services, leading to an overall higher volume of transactions. In Classical Economic theory, one of the main tenets is that the GDP of an economy is self-regulating when all of its goods and services are fully utilized. This complete utilization is beneficial for a country’s economy, but can only occur if there is enough demand to meet the supply of goods and services. By Say’s Law, there will always be enough total income to purchase all goods and services in an economy, which therefore means that the potential for maximum demand is always present in an economy. Population increases the amount of demand in the economy, as more people want to exchange their income for goods and services, thereby boosting the overall economy and resulting in a higher GDP for the nation. Additionally, it makes sense that higher unemployment results in less GDP growth for a nation, because it means there would be a reduced labor force to create the goods and services

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demanded by the economy. This would mean that the supply may not necessarily meet demand, thereby decreasing the amount the economy would be able to grow by. Keynesian theory suggests this result, as it states that the economy cannot balance itself if there is a large portion of people unemployed. The converse is also true, as low GDP growth would increase unemployment, with a lack of economic opportunity resulting in less people being employed. Inflation harms GDP per capita, as it causes a decrease in the value of the currency, which means that a standard amount of money decreases in purchasing power over time. If the amount of goods/ services capable of being purchased with the same amount of money decreases, then spending will decrease in the economy.. This once again relates to Classical economic theory, because if the supply is greater than demand the full potential of the economy is not realized, and the GDP, and therefore GDP per capita, would decrease. Overall, Europe is economically safe, and for the most part, in an ideal situation. Based on the data, the main focus points of European governments should be to reduce unemployment and slow down inflation. Inflation is unavoidable in a purely capitalistic economy, so unemployment is the largest detrimental factor for economic success, and therefore more effort should be put into connecting unemployed workers with jobs to fully utilize their role in GDP maximization. Asia represents a different stage of economic growth than Europe, as it contains economies that are rapidly growing and are not yet as large as their European counterparts. This means that Asian nations have a high ceiling in terms of economic growth, but it also means that they are much more unstable, and therefore more susceptible to economic crises. In Asia, population and GDP have a strong, positive correlation, population change and GDP change have a strong, positive correlation, corporate tax rates and GDP per capita have a strong, negative correlation, and inflation rates and GDP change have a strong, negative correlation. These trends are more indicative of the rapid growth that developing

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nations tend to undergo. Population change and GDP change is the correlation that the rapid growth of Asian nations is most easily shown by. Population increases GDP, as seen in European nations, as it creates a greater volume of supply-and-demand based transactions. Therefore an increase in population would increase GDP, albeit the increase may be marginal. The fact that population change would result in such a large correlation with GDP change means that population growth is a much larger factor in Asian economies than anywhere else in the world. This goes back to the idea that Asian economies have not yet reached their ceiling for rapid growth, which means that demand is much higher than a supply of goods and services, so any new additions to the workforce are given a job out of necessity, to fully monetize the demand in the economy. This rapid growth of the workforce further boosts the ceiling of maximum output for the nation, meaning that its GDP would increase. Another interesting factor for Asian economies is corporate tax. By Keynesian economic principles, when the government spends money on public-work projects to reduce unemployment, the economy receives a stimulus and grows. By that same logic, if the government takes money from the economy in the form of taxes, the growth of the economy is reduced. If all of the tax money is then reinvested into the economy through public-infrastructure projects or simply by investing in the economy, then the economy will still grow. However, taxes are also used for non-economic purposes, meaning that the economy would be losing money from the system entirely, which not only reduces the value of the economy but also removes the buying potential of that money, so the demand decreases at the same time as the GDP. Therefore, it is clear that higher corporate tax rates result in lower GDP per capita for countries in rapid development. Lastly, inflation harms the GDP change in Asia for the same reason as Europe. Inflation decreases the buying power of money, reducing overall spending, which means that the total output of the economy reduces drastically, causing a much smaller GDP change.


Asia represents a data set of rapid, economic growth, as well as overall growth as well, due to an increasing population. Therefore, the difference between developing economies and stable economies is that population change and corporate tax rates have a greater effect on developing economies, while population and unemployment have a greater effect on developed economies, and inflation rates have an effect on both types of economies. On an interesting note, unemployment has a strong, positive correlation with GDP among African nations. This is reasonable to expect among countries with smaller economies, especially once it is considered that the effect of unemployment on GDP is indirect, and more of a chain reaction. Unemployment causes a reduction in inflation, as companies can reduce the wages they pay to workers, as there are fewer jobs than unemployed people. A reduction in inflation, in turn, boosts the economy, as is seen by its strong, negative correlation with GDP. Therefore, to a certain extent, unemployment can boost the economy, and may even increase the efficiency of the workforce. The G16 group we analyzed was chosen based on GDP, and therefore represents the 16 largest

economies in the world today. The group follows the trends that stable economies follow, with population and GDP having a strong, positive correlation, inflation and GDP per capita having strong, negative correlation, and unemployment and corporate tax not having significant correlation with any of the GDP metrics. The logical reasoning for this follows the same economic theory that best fits Europe, with large economies relying on a large workforce to produce a supply of goods and services capable of meeting the large demand of the economy. The main outliers for the G16 and the world at large are the U.S. and China. Both are proven outliers in terms of GDP, and China is an outlier in terms of population. The U.S. is an outlier in terms of the population and GDP trend among G16 nations because it has an average population size and yet also has the highest GDP by far out of the entire group. China seems to be slightly below the same trend, meaning its GDP per capita is lower than it should be. Additionally, China is rapidly growing as well, with 6.9% GDP growth last year. It is not a matter of coincidence that the U.S. and China have such large economic success, or that they have been successful together.

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The United States has been a nexus of trade and capitalism for a very long time, hosting the biggest stock exchange in the world, as well as being the largest consumer of goods and services in the world. China has focused on becoming the cheapest place to manufacture for large companies, making it an attractive place for companies to outsource their cheap labor, and create a rapidly growing income stream for the nation. China exports far more than they import, keeping the money supply higher inside their economy. However, with such a large population, the GDP per capita is still relatively low, which is ideal for China’s economic style. If China’s standard of living becomes too large, it will drive manufacturing costs up too high and will make China less attractive for businesses to base their manufacturing in. Since China is essentially running a large surplus of money at all times, the government has to figure out how to spend that money to ensure that inflation does occur, to make the currency weaker, so that the rapid growth of the economy is balanced. This leads to why the U.S. and China grow together: China is deeply invested in the U.S., being the largest foreign holder of treasury bonds. Another way of putting this is that China is buying U.S. debt to ensure that the value of the dollar stays strong in comparison to the Chinese Yuan. This has serious repercussions for the rest of the world, as China controls manufacturing for the most part, and by holding American debt, has leverage on the U.S. economy, allowing it to enforce favorable economic policies. There is a constant tug of war between China and the US over allowing Chinese companies entry to the US economy. The reason this strategy is working for the U.S. currently is because if China chose to act on its leverage, and choose to cash in its debt right away, it would cause a financial depression in the U.S. economy, meaning that the world economy would slowly collapse as well, since the American dollar is the standard currency across the world, eventually have dire consequences for China itself. China’s economy is not yet strong enough to withstand an economic blow like that,

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because there would not only be some backlash for its trade partnerships with other nations but also because it would cause them to lose their biggest consumer, meaning that the output of the nation would greatly decrease. Similarly, if the U.S. were to assume a stance of defaulting, or refusing to pay its debt, a comparable situation would commence on the global scale, with the value of the dollar and economies tied to it becoming worthless. As this study proves, the economies of the world at large follow a set of trends based on the historical periods of their respective economies, as in whether they are developed, in the process of developing or struggling. However, these trends are not fully applicable to outlier nations that are far more successful than the rest of the world, namely the U.S. and China. The U.S. and China have a far more intricate relationship than trade agreements in the rest of the world, as both nations have a very high stake in the success of the other, and yet are competing for economic supremacy over the entire world with each other, as is seen in the graphs below. This suggests the possibility of a study to identify which factors truly have the greatest effect in the U.S. and China trade relationship, as it would be significantly different than the rest of the world. Ultimately, the basic points of economic focus are the same for all nations no matter what their economic status is: reduce inflation and unemployment, and ensure that there is always enough supply to meet demand and to ensure that demand does not increase at such a rate that is not manageable. Essentially, stable economic growth is a possibility for any nation capable of following a disciplined economic strategy, responsible monetary discretion, and a large workforce, while rapid economic growth is possible for countries with a growing population, low corporate taxes, and a strong trade partnership, both types of growth being exemplified by China and the U.S. References


REFERENCES [1] Amadeo, Kimberly. “The Surprising Ways China Affects the U.S. Economy.” The Balance, The Balance, 15 July 2019, www.thebalance.com/chinaeconomy-facts-effect-on-us-economy-3306345. [2]: “Bureau of Labor Statistics Data.” U.S. Bureau of Labor Statistics, U.S. Bureau of Labor Statistics, data.bls.gov/timeseries/LNU04000000?periods=AnnualData&perio ds_option=specific_periods&years_option=all_years. [3] “China Nominal GDP [1992 - 2019] [Data & Charts].” [1992 2019] [Data & Charts], www.ceicdata.com/en/indicator/china/nominal-gdp. [4] “GDP Growth Rate.” Google, Google, www.google.com/ publicdata/explore?ds=d5bncppjof8f9_&met_y=ny_gdp_mktp_kd_ zg&idim=country:CHN:IND:USA&hl=en&dl=en. [5] Jahan, Stewart, et al. “What Is Keynesian Economics?” What Is Keynesian Economics? Back to Basics - Finance & Development, September 2014, www. imf.org/external/pubs/ft/fandd/2014/09/basics.htm. [6] MGM Research. “China vs United States - A GDP Comparison.” MGM Research, 14 May 2019, mgmresearch.com/china-vs-united-states-a-gdpcomparison/. [7] “Unemployment - Unemployment Rate - OECD Data.” TheOECD, data.oecd.org/unemp/unemployment-rate.htm. [8] “What Is Economic Growth?” Intelligent Economist, 9 June 2019, www.intelligenteconomist.com/economic-growth/.

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ACS San Diego Local Section The San Diego Local Section of the American Chemical Society is proud to support JOURNYS. Any student in San Diego is welcome to get involved with the ACS San Diego Local Section. Find us at www.sandiegoacs.org! Here are just a few of our activities and services:

Chemistry Olympiad

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Co-Presidents Johnny Lu and Claire Wang

Editor-in-Chief Katherine Izhikevich

Vice President William Zhang

Assistant Editor-in-Chiefs Jessie Gan and Jesse Zhang

Coordinators Riya Irigireddy, Allison Jung, Sua Kim, Erica Wang

Section Editor Jade Nam

Contributing Writers Shivek Narang, Seongkyung Bae, Andrew Hwang, Michele Kim, Pratyush Seshadri and Abraham N. Goldstein, Prerana Pai Bhande, Bill Yildiz, Charvie Yadav

Contributing Editors Rhea Gandhi, Jenny Han, Jerry Huang, Ore James, Jae Kim, Sumith Nalabolu, Rinna Yu, Aidan Zhang, Grace Zhou

Design Managers Anna Jeong and Daniel Kim Designers Anna Jeong, Daniel Kim, Claire Lane, Gwennie Liu, Irene Pi, Isleen Pi, Kevin Song Publicity Manager Gwennie Liu Scientist Review Board Members John Allen, Caroline Kumsta, Heather Broccard-Bell, Ojash Neopane, Titan Alon, Tapas Nag, Ricardo Borges, Arye Nehora, Ji Dai Scientist Review Board Coordinators Claire Wang and Riya Irigireddy

Graphics Manager Seyoung Lee Assistant Graphics Manager Amy Ge Graphic Artists Amy Ge, Alex Han, Lillian Kong, Seyoung Lee, Lesley Moon, Aaron Shi, Kevin Song Web Designer Logan Levy Media Managers Anna Jeong and Katherine Izhikevich Blog Writers Ben Hong, Lynne Xu, Diane Zhou

Staff Advisor Mrs. Mary Ann Rall

Dear Reader, We’re so excited to present to you Issue 11.1! We’d like to start off by thanking our staff for their commitment and diligence to producing great work despite complications due to the COVID-19 pandemic. This year has been tough, but we’re really grateful for the dedication of our team and collaborators. Additionally, In light of recent events, JOURNYS has been looking to expand beyond our normal print issues to develop a more robust digital presence. Thus, we’ve been making great strides toward providing more current and regular content through our new blog articles and weekly newsletter. We are also happy to announce our new partnership with the Newsbank Corporation, helping us reach a wider audience of curious and ambitious youth. JOURNYS is also very grateful for Brain Corp’s generosity in providing us with the Adobe InDesign accounts that we use to put together our issues. And as always, we’d like to thank the American Chemical Society for providing the funding that makes our print issues possible, As for the months ahead of us, we’ll be focusing on collaborations with other student organizations, and we hope that you are as excited as we are to see what the future has in store for JOURNYS. Best, Johnny, Claire, and Katherine 34 | JOURNYS | SUMMER 2020


Journal of Youths of Science

2019-2020

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JOURNYS Issue 11.1  

JOURNYS Issue 11.1  

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