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Famous and (Infamous) Workplace and Community Training: A Social History of Training and Development 1st Edition David M. Kopp (Auth.)
Perspectives From Psychology, Neuroscience, and Human Development
Edited by
JARED M. NOVICK
MICHAEL F. BUNTING
MICHAEL R. DOUGHERTY
RANDALL W. ENGLE
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Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America.
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Library of Congress Control Number: 2019949802
ISBN 978–0–19–997446–7
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To our families.
For our students.
I. COGNITIVE PERSPECTIVE
II. NEUROCOGNITIVE PERSPECTIVE
III. DEVELOPMENTAL PERSPECTIVE
of the Present Systematic Review and a Call to Others to Analyze the Extant Literature in
Training People in Strategies to Minimize the Need for
Acknowledgments
We are indebted to the range of scholars who contributed their time and expertise to write the chapters for this collected volume, and who also provided ample work to peer-review each other’s submissions. We thank them for these efforts and for slogging through our rounds of gentle edits and comments. A special thanks to Karly Schwarz and Claire Crossman, graduate assistants in the Department of Hearing and Speech Sciences at the University of Maryland, for their tireless endeavors to pull this entire project together in all the ways that matter. It is not an overstatement to say that, without them, this book would not have been completed.
Contributors
Jacky Au School of Education University of California, Irvine Irvine, CA, USA
Claudia C. von Bastian Department of Psychology University of Sheffield Sheffield, UK
Daphne Bavelier Brain and Learning Lab Psychology and Education Sciences University of Geneva Geneva, Switzerland
Rossana De Beni Department of General Psychology University of Padova Padova, Italy
Erika Borella Department of General Psychology University of Padova Padova, Italy
Michael F. Bunting
Senior Research Scientist and Director of Research Development Applied Research Laboratory for Intelligence and Security (ARLIS)
University of Maryland College Park, MD, USA
Martin Buschkuehl
Director Education Research
MIND Research Institute Irvine, CA, USA
Barbara Carretti Department of General Psychology University of Padova Padova, Italy
Cesare Cornoldi Department of General Psychology University of Padova Padova, Italy
Adele Diamond Department of Psychiatry University of British Columbia Vancouver, BC, Canada
Sandra Dörrenbächer Department of Psychology Saarland University Saarbrücken, Germany
Michael R. Dougherty Professor of Psychology Department of Psychology University of Maryland College Park, MD, USA
Adam Eichenbaum Helen Wills Neuroscience Institute University of California, Berkeley Berkeley, CA, USA
Randall W. Engle Professor of Psychology School of Psychology
Georgia Institute of Technology Atlanta, GA, USA
Anders M. Fjell Department of Psychology University of Oslo Oslo, Norway
C. Shawn Green
Department of Psychology
University of Wisconsin, Madison Madison, WI, USA
Sabrina Guye
University Research Priority Program
“Dynamics of Healthy Aging” University of Zurich Zurich, Switzerland
Henk J. Haarmann
Applied Research Lab for Intelligence and Security University of Maryland College Park, MD, USA
Kenny Hicks
School of Psychology
Georgia Institute of Technology Atlanta, GA
Susanne M. Jaeggi
School of Education University of California, Irvine Irvine, CA
Benjamin Katz
Assistant Professor Human Development and Family Science
Virginia Tech Blacksburg, VA
Jutta Kray
Department of Psychology
Saarland University Saarbrücken, Germany
Stefanie E. Kuchinsky
Applied Research Lab for Intelligence and Security
University of Maryland College Park, MD, USA
Ulman Lindenberger
Max Planck Institute for Human Development Center for Lifespan Psychology Berlin, Germany
Daphne S. Ling Department of Psychiatry University of British Columbia Vancouver, BC, Canada
Martin Lövdén Department of Neurobiology, Care Sciences and Society (NVS), H1
Karolinska Institutet Stockholm, Sweden
Jared M. Novick
Associate Professor Department of Hearing and Speech Sciences Program in Neuroscience and Cognitive Science
University of Maryland College Park, MD, USA
Lars Nyberg
Department of Integrative Medical Biology
Umeå University
Umeå, Sweden
Brooke M. Okada Graduate Researcher Department of Psychology University of Maryland, College Park College Park, MD, USA
Florian Schmiedek Department of Education and Human Development
German Institute for International Educational Research (DIPF) Frankfurt am Main, Germany
Priti Shah
Department of Psychology
University of Michigan
Ann Arbor, MI
Carla De Simoni
Department of Psychology, University of Zurich Zurich, Switzerland
Contributors
L. Robert Slevc
Associate Professor Department of Psychology Program in Neuroscience and Cognitive Science University of Maryland College Park, MD, USA
Kristine B. Walhovd
Department of Psychology University of Oslo Oslo, Norway
Prologue
“Brain training” exploded into the marketplace earlier this century with the publication of a few scientific studies that caught the attention of the media and, inevitably, excited the public at large. The findings offered hope to populations that no pill could provide: Could we offset the effects of dementia through consistent mental challenge? Could we close the achievement gap for at-risk children? Unsurprisingly, the global buzz promoted the advent of websites and video games that issued regimens for training intelligence. The promise of brain training (also known as cognitive or working memory training) was that persistent engagement of core cognitive functions like fluid reasoning, inhibition, or attention control could increase cognitive functioning in general, and that the effect endures over time.
However, a problem with many brain-training applications on the market then, and today, is the lack of scientific consensus in the field about whether they actually work. Certainly, the natural course of things is that new tests of popular ideas give way to data that offer different results and, thus, put a spotlight on different ways of interpreting original findings. This is good for science, as alternative viewpoints (theories, ideas, etc.) push a field forward through a conspiracy of evidence that attempts to converge on the truth. Sometimes it means reconsidering our initial excitement through the lens of nuance and reminding ourselves—scientists and civilians alike—that knowledge can only advance through steady interrogation of traditional hypotheses. While brain training remains popular, some of the initial excitement has faded under the glare of scientific scrutiny about the true efficacy of the programs and the unavoidable press that comes along with it.
Here, we bring a range of viewpoints into a single volume that represents the ideas (and empirical discoveries) of leading researchers in the arena of cognitive training. Critically, perspectives from both proponents and detractors are included in an accessible format so that students of psychology (of both the professional and armchair varieties) can assess the current state of the science. The chapters are highly interdisciplinary and cover a range of related issues, which are organized thematically around three “cluster” topics in working memory training: cognitive perspectives, cognitive neuroscience perspectives, and developmental perspectives across the lifespan. Each cluster is introduced by a “challenge” chapter in which the authors pose certain fundamental questions that are intended to identify key issues, spark debate, and yield an array of viewpoints
within the clusters. The subsequent chapters within a cluster are organized around the authors’ responses to the challenge questions and the defense of their responses. They offer best practices in the field regarding effective training methods, tools, designs, and uses for applied, theoretical, and computational goals, and what has been learned—and what remains to be learned—about the advantages and potential caveats of cognitive training.
The goal of this book is to offer an objective and balanced appraisal of the science of cognitive training by framing the book’s theme within a broader context of cognitive psychology in terms of past, present, and the contours of future theoretical and empirical endeavors in the domain of cognitive and working memory training.
A Brief History of the Scientific Study of Human Abilities
Human intelligence—the mental traits and abilities that are uniquely human and the province of Homo sapiens alone—has permitted people to accomplish feats unmatched by any other creature on Earth. Societies are products of human intelligence, which enables our knowledge and understanding of the world, both the physical and the spiritual. Intelligence has taken humans to the moon, sent robots to Mars, and developed tools for looking across the cosmos and listening for decipherable sounds from space. Through their intelligence, humans possess the cognitive abilities to learn, understand, remember, and teach. Humans can think concretely or abstractly, and they can apply logic and reason, and solve novel problems. They can recognize patterns, plan, make decisions, and draw conclusions. Humans can use language to communicate thoughts, feelings, intentions, and deceptions in infinitely creative and productive ways.
For much of the history of the scientific study of intelligence, two related questions have driven the conversation: What are the dominant abilities involved in intelligent behavior, and what is the organization of the mechanisms that support it? Spearman (1904, 1927, 1946; Spearman & Wynn-Jones, 1950) insisted that, instead of an infinite number of unrelated mental abilities, a single common factor runs through all intellectual operations, generating commonly observed correlations among tests. Spearman’s original contention was that performance associations across mental tests can be accounted for by the combination of two factors. One of these factors, g, is common to all the tests and is domain-free. The other factor, designated the s factor, is specific, and it captures what remains in the intercorrelations among the tests (except what is attributable to test errors). Spearman regarded g as a cognitive-general factor and argued that it is present to varying degrees in every intellectual operation.
Spearman’s theory was informed by his observations of performance on mental ability tests. He observed that people who do well in one area also tend to do well in other areas. This lent credence to his notion that general intelligence influences performance on all cognitive tasks. A commonly invoked metaphor for understanding general intelligence is to compare it to athleticism. One probably would not expect a professional basketball player to be equally impressive on the softball or soccer fields; however, because of her all-around athleticism, she is likely to excel at those sports compared to your average couch potato. Yet, many things in the world are related but may reflect lurking influences of confounding variables. Take, for instance, the oft-cited correlation between a rise in ice cream sales and homicide rates. It is considerably unlikely that one has a causal effect on the other, as many factors (e.g., hot weather, more milling about) independently contribute to both. Thus, the same reasoning ought to be applied to cognitive training: rather than focusing on the products of interventions (does it work or not? does performance increase from pretest to posttest?), the effects of brain training (or lack thereof) may be better understood through the mechanisms that drive the process.
Important questions remain about how to measure and define intelligence and understand all the factors that comprise this squishy term; but contemporary study asks how to improve it nonetheless in hopes of preventing cognitive abilities from declining with age. Intelligence may not be static. Cognitive skills, memory, reasoning, motor skills, and the speed of thinking can and do increase or decrease over time and are subject to experience-induced plasticity for a multitude of reasons. Genetics and environmental conditions are contributing factors, both to the growth of intelligence and the delaying of its deterioration. Nutrition, pharmacological and psychological factors, and behavior can have positive or negative impacts. Understanding the relationship among these factors and how they affect cognitive performance is a true scientific challenge, let alone the challenge of isolating what is cause, and what is effect.
The majority of readily noticeable changes in intelligence occur at either the onset of development, during the critical period, or during old age. But is intelligence, or at least complex cognitive behavior, immutable between these two poles on the developmental spectrum? The predominant view for much of the twentieth century was that it was: intellectual capacity was shaped during childhood and fixed during adulthood until old age brought an inevitable decline. Recent research on the malleability of intelligence has begun to challenge this view, indicating that certain types of mental workouts, also known as “brain training,” can actually improve core mental abilities and protect against such declines. As the metaphor goes, as exercising a muscle increases physical strength, exercising the mind can increase mental fitness in terms of how much information can be
temporarily maintained and processed, including the ability to focus attention on a current task at hand.
There is little doubt that “brain training” is a hot and hotly contested topic in the interdisciplinary fields of psychology, cognitive neuroscience, and human development. This edited volume asks questions about the nature of intellect and cognitive abilities and explores evidence that these attributes are amenable to change from training, and what mechanisms that contribute to complex cognition may or may not be malleable via intervention (i.e., is there more promise in a process-specific approach to training, rather than an all-encompassing one subsumed under the broad heading of “intelligence”?). Importantly, one focus of the book is on the notion of transfer—namely, the extent to which cognitive training generalizes to learning and performance measures that were decidedly not part of the training regimen, but still tap into specific process that were part of the regimen despite ostensible differences in task characteristics. This edited volume is inspired by the outcome of a 2011 workshop on this topic and features a series of chapters by 12 leading scholars in the cognitive and neural sciences. Generally, the issues addressed are:
• What is the scientific evidence that cognitive training influences and benefits performance on a range of everyday tasks, including intelligence, memory, attention, vision, learning, creativity, and language processing— in both healthy and special populations (e.g., young children, aging adults, those diagnosed with Attention Deficit Disorder)?
• What best practices exist in the field regarding effective training methods, tools, designs, and uses for applied, theoretical, and computational goals?
• What has been learned—and what remains to be learned—about the advantages and caveats of cognitive training? What should, and should we not, buy into?
We have assembled an interdisciplinary group of distinguished authors—all experts in the field—who have been testing the efficacy of cognitive and workingmemory training using a combination of behavioral, neuroimaging, metaanalytic, and computational modeling methods. As will become clear, there is a range of views on the extent to which cognitive training remains promising. As such, this edited volume will be a defining resource on the practicality, utility, and validity of the field of cognitive training research in general and working memory training in particular.
This book is the first of its kind and is therefore expected to appeal broadly to academics in the cognitive and neural sciences, to students of psychology, to clinical practitioners interested in cognitive remediation, and to government stakeholders whose principal concern is to increase the learning and
performance capabilities of their workforce. We therefore anticipate that this book will play a key role in the field by integrating a host of research efforts on cognitive training and cognitive plasticity into a single, comprehensive volume accessible to a wide audience. In the end, we also hope that it will spring new research that addresses still-open questions through collaborators on both sides— namely, the enthusiasts and critics of current data—who set aside dogma, their scientific differences, and agree upon a study design in pursuit of scientific rigor and knowledge, which can only advance through constant questioning of conventional models, ideas, and approaches.
Michael F. Bunting
and
Jared M. Novick
References
Spearman, C. (1904). “General intelligence,” objectively determined and measured. American Journal of Psychology, 15, 201–293.
Spearman, C. (1927). The abilities of man. New York, NY: Macmillan.
Spearman, C. (1946). Theory of general factor. British Journal of Psychology, 36, 117–131.
Spearman, C., & Wynn-Jones, L. L. (1950). Human ability: A continuation of “the abilities of man.” London, England: Macmillan.
SECTION I
COGNITIVE PERSPECTIVE
1 Cognitive Perspectives of Working Memory Training
Current Challenges in Working Memory Training
Kenny Hicks and Randall W. Engle
Introduction
Working memory training is an emergent field aimed at improving general cognitive abilities through targeted brain exercises. The prospect of improving cognitive abilities like attention control, comprehension, and reasoning has piqued the interest of the scientific community and the general public alike. If cognitive abilities like working memory capacity can be improved, it is assumed that this improvement will result in benefits to a broad range of real-world abilities associated with working memory capacity, including reading comprehension, math performance, and attention control (Holmes et al., 2010; Jaeggi, Buschkeuhl, Jonides, & Perrig, 2008; Klingberg, 2005; Klingberg, Forssberg, & Westerberg, 2002). However, to date, there is no clear answer to the question of whether cognition will improve through interventions designed to enhance working memory capacity. One reason is an absence of discussion among researchers of various training paradigms, which has resulted in a lack of consensus on the basic underlying principles of the research, including differences in the operational definition of working memory training, inconsistent ways to measure increases in working memory, and little integration of findings into a larger literature on cognitive training or, more broadly, working memory capacity.
This line of research is theoretically important, but it is also unique because of its potential for real-world impact. Working memory training has far-reaching implications for many diverse stakeholders, including not only academics but also any group interested in cognitive improvements. Those concerned with such diverse topics as improving selection, job training, and cognitive remediation are interested in the efficacy of working memory training and its potential for future applications. Products that extoll the benefits of “brain training” and other targeted exercises aimed at increasing cognitive abilities have permeated the public sphere. Such widespread attention has led to an influx of working
memory training literature in psychology and other disciplines, but our enthusiasm must be tempered by the evidence.
Preliminary studies on working memory training are not often referred to as pilot studies in media headlines or by the commercial programs that use them for advertisement (Holmes, Gathercole, & Dunning, 2009; Holmes et al., 2010). Simply put, although coverage of preliminary studies may be well intentioned, the results can be easily oversold. It is our hope that the reader will gain perspective on the outcomes of training, evaluate the strength of the current evidence, develop an understanding of the current debate on the most controversial findings, and understand how these findings might transfer to the real world.
Researchers of working memory training claim that cognitive training interventions result in transfer to a domain-general ability (Klingberg, 2005) above and beyond task-specific abilities, such as strategy use (e.g., chunking items into groups). Although perspectives on what constitutes successful transfer are varied, it is generally conceptualized as near and far transfer.
Near transfer refers to gains in tasks that tap the same construct that the intervention seeks to improve. In terms of working memory training, near transfer would be achieved by demonstrating improved performance on novel working memory tasks. Evidence of far transfer occurs when subjects demonstrate superior performance on tasks that require working memory but reflect a fundamentally different construct (e.g., fluid intelligence). The logic here is that working memory is a key component of other higher-order cognitive abilities, such as fluid intelligence, and that improving working memory should lead to an increase in any ability dependent on the working memory construct. Ideally, after working memory training, improvements would be observed in tasks designed to assess both near and far transfer. However, to claim that working memory training increases fluid intelligence, the results should also provide evidence that working memory was improved. That is to say, at a minimum, researchers should demonstrate near transfer before claiming to show evidence of far transfer.
To better understand the complexity of transfer, we can look to the 1992 case study of Rajan Mahadevan. Mahadevan had an exceptional memory for numbers: he could recall a record of more than 30,000 digits of pi. Investigators found that, rather than possessing an innate ability for memorization, he used a mnemonic strategy, namely grouping numbers into blocks of ten. Further evidence that Mahadevan used a memory strategy for numbers came when scientists found that his memory for spatial objects was merely average (Biederman, Cooper, Fox, & Mahadevan, 1992). What is important is that Mahadevan’s superior memory performance for digits did not transfer to other domains of memory (Ericsson, Delaney, Weaver, & Mahadevan, 2004).
The goal of working memory training is to demonstrate broad transfer to tasks that involve the same components of working memory that were targeted during
training. Therefore, improvements should be observed on a broad range of tasks that tap the ability being trained. This is measured by observing the difference between pre- and posttest performance on cognitive tasks that subjects have not practiced.
Investigating Transfer
Given our lab’s substantial contributions to the theoretical aspects of working memory capacity and its relationship to fluid intelligence (Engle & Kane, 2004; Engle, Tuholski, Laughlin, & Conway, 1999; Kane et al., 2004; Shipstead, Redick, & Engle, 2012b), we were intrigued by the prospect of improving cognition and the potential theoretical and real-world implications. In particular, we were motivated to further our understanding of the mechanisms that drive the cognitive improvements.
The task of selecting which training regimen to investigate was difficult because no unified approach to the study of working memory training exists. Due to the highly influential claim that training on the dual N-back task led to improvements on matrix reasoning, one of the best indicators of fluid intelligence (Jaeggi et al., 2008), our first study implemented the dual N-back paradigm. While offering an exciting prospect for the field, Jaeggi and colleagues’ article included methodological shortcomings, such as measuring fluid intelligence with a single test instead of multiple indicators. Further, the 2008 article actually represents the combination of four studies that differed significantly, including different measures of fluid intelligence across studies, differing deadlines for completing the test of fluid intelligence, and the use of no-contact control groups (see Redick et al., 2012, for a more in-depth discussion of the studies). In an attempt to replicate Jaeggi and colleagues’ 2008 findings, we conducted a follow-up study that addressed the previous study’s shortcomings by including a dual N-back training group, an active control group that performed an adaptive visual search task, as well as a no-contact control group. Practice on a visual search task was chosen as the active control condition because previous research found no relationship between working memory capacity and visual search performance across a number of studies that varied the difficulty of the search task (Kane, Poole, Tuholski, & Engle, 2006). The training study also included measures designed to simulate real-world performance (e.g., the ability to manage air traffic and perform complex multitasks). Despite adequate statistical power and the inclusion of multiple indicators of both fluid intelligence and multitasking, the results of the training study failed to demonstrate any behavioral improvements after dual N-back training (Redick et al., 2012).
After our failure to replicate far transfer to fluid intelligence after dual N-back training, our research became focused on investigating near transfer. Therefore, our next study explored the effects of training on measures of short-term memory and the complex span (Chein & Morrison, 2010; Unsworth, Heitz, Schrock, & Engle, 2005; Unsworth, Redick, Heitz, Broadway, & Engle, 2009). Subjects were randomly assigned to a short-term memory training condition, a complex span training condition, or an active control group that practiced a visual search task. All three were adaptive tasks (i.e., increased in difficulty with success). To investigate far transfer, we administered multiple measures of fluid intelligence at pre- and posttest, including Raven’s Progressive Matrices, the Letter Sets task, and the Number Series task. We also included other measures related to working memory capacity, such as free recall. None of our training groups showed improvements in fluid reasoning (e.g., far transfer). We found partial evidence for moderate transfer (tasks representing moderate transfer that were different from the training paradigm but still relied heavily on many of the same memory processes involved in the training tasks). For instance, improvements in the secondary memory portion of immediate free recall were observed, while evidence for the Keep Track Task was less interpretable (Harrison et al., 2013). Overall, this study represented our second failure to demonstrate domain-general improvements to cognitive abilities after extensive working memory training.
Criticisms of Working Memory Training
Shipstead and colleagues (2010) published the first systematic review analyzing the methods and results used in the working memory training literature. Prior to 2010, the claims that working memory training led to vast improvements in intelligence and attention control had gone largely unchallenged. The Shipstead article was fittingly titled “Does Working Memory Training Generalize?” If working memory capacity can predict performance across a range of important cognitive outcomes, does working memory training lead to improvements across a wide range of cognitive tasks? Although the question posed is simple, it has received surprisingly little attention. The conclusion of Shipstead and colleagues was that most working memory training studies failed to control for threats to internal validity, failed to include adequate control groups for comparison, and consistently relied on a single test to represent constructs of interest—practices that continue in the working memory training literature today.
In the publication, “Cogmed Training: Let’s Be Realistic About Intervention Research” by Gathercole and colleagues (2012), the researchers argued that many of the criticisms raised about working memory training are impractical. They justified their perspective with two arguments. First, the authors maintained that
training studies are costly and time consuming. Second, they argued that too much rigor in the early stages of investigation could be wasteful because the outcome of a training study is uncertain. They advised initial studies on working memory training to focus on ways to minimize costs in order to maximize the chance for a successful outcome (e.g., no-contact and active control groups are expensive, so they shouldn’t be required during early stages of research). The authors made their case by describing the results of two pilot studies they were able to complete successfully while reducing the cost of training. The first study (Holmes et al., 2009) did not include a control group, while the second study (Holmes et al., 2010) included a no-contact control group. For Gathercole and colleagues’ logic to hold true, they must assume that the results of working memory training studies are not dependent on whether the study includes a passive or active control group. However, meta-analytic work investigating this issue has found the opposite. Researchers have found substantial differences in training outcomes that depend on the type of control group included in the study. Specifically, training studies including no-contact control groups have much higher rates of finding positive training effects, whereas studies including active control groups have found little to no reliable effects of training (for a more in-depth review, see Melby-Lervåg & Hulme, 2013; Melby-Lervåg, Redick, & Hulme, 2016). This result is further supported by Dougherty and colleagues (2016), who conducted a re-analysis of N-back training studies. In line with the broader literature on working memory training, the authors found significant evidence in support of working memory training when studies included a no-contact control group, but no evidence for training-related benefits for studies that included an active control group. In light of these findings, we agree with Shipstead and colleagues (2012b) that researchers should pursue more robust experimental designs. In addition to the inclusion of active control groups, researchers should also be cautious when interpreting their findings. An in-depth analysis of five studies by Redick (2016) showed that several studies supporting working memory training are the result of a general decline in the performance of the control group from pre- to posttest, which render the results uninterpretable from any theoretical perspective.
In consideration of this work, the aim of the current chapter is to pose a series of questions to researchers investigating the efficacy of working memory training. By adopting a common-question approach, we are following the example set by Variation in Working Memory (Conway, Jarrold, Kane, Miyake, & Towse, 2007), where each contributing research group is asked to address questions that motivate discussion about the commonalities and distinctions in the field of working memory training. Through answering these directed questions, each research group will discuss what motivates their work, cover the research results of their particular paradigm, and express their views on the current state of working
