Human and AI collaboration in the higher education environment: opportunities and concerns

In service of the goal of examining how cognitive science can facilitate human-computer interactions in complex systems, we explore how cognitive psychology research might help educators better utilize artificial intelligence and AI supported tools as facilitatory to learning, rather than see these emerging technologies as a threat. We also aim to provide historical perspective, both on how automation and technology has generated unnecessary apprehension over time, and how generative AI technologies such as ChatGPT are a product of the discipline of cognitive science. We introduce a model for how higher education instruction can adapt to the age of AI by fully capitalizing on the role that metacognition knowledge and skills play in determining learning effectiveness. Finally, we urge educators to consider how AI can be seen as a critical collaborator to be utilized in our efforts to educate around the critical workforce skills of effective communication and collaboration.

By Design: How People Adapt to Cognitive Limitations in Politics

Elections, jury deliberations, lawmaking, high-stakes negotiations and related activities are human attempts to answer the question "How should we live?" Collectively, we know these activities as politics. Politics are how societies attempt to reconcile diverse individual needs with potential benefits of social coordination. People's beliefs about what others will do ainfluence many political strategies and outcomes. This article reviews how properties of cognition affect these political phenomena. Contrary to the common belief that many citizens are too ignorant to make competent political decisions, we focus on a central finding of social science-how societies can design contexts and environments to overcome individual cognitive limitations. These adaptations expand societal capacities to provide essential goods, services, and protections. In addition to explaining these adaptations, we also show how greater collaborations between cognitive science and the social sciences can help societies do even better.

Radical Collective Intelligence and the Reimagining of Cognitive Science

To introduce our special issue How Minds Work: The Collective in the Individual, we propose "radical CI," a form of collective intelligence, as a new paradigm for cognitive science. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. To explain cognitive performance, it appeals to the distribution of cognitive labor on the assumption that the human project runs on countless interactions between locally acting individuals with specialized skills that each retain a small part of the relevant information. Some of the papers in the special issue appeal to radical CI to account for a variety of cognitive phenomena including memory performance, metacognition, belief updating, reasoning, and problem-solving. Other papers focus on the cultural and institutional practices that make radical CI possible.

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

BACKGROUND

Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake.

OBJECTIVE

This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction.

METHODS

We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R2>0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake. Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction.

RESULTS

Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P<.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P<.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake.

CONCLUSIONS

The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).

Cognitive Science From the Perspective of Linguistic Diversity

This letter addresses two issues in language research that are important to cognitive science: the comparability of word meanings across languages and the neglect of an integrated approach to writing systems. The first issue challenges generativist claims by emphasizing the importance of comparability of data, drawing on typologists' findings about different languages. The second issue addresses the exclusion of diverse writing systems from linguistic investigation and argues for a more extensive study of their effects on language and cognition. We argue for a refocusing of cognitive science research on linguistic diversity in all modalities to develop the most robust understanding of language and its role in human cognition more broadly.

Order and change in art: towards an active inference account of aesthetic experience

How to account for the power that art holds over us? Why do artworks touch us deeply, consoling, transforming or invigorating us in the process? In this paper, we argue that an answer to this question might emerge from a fecund framework in cognitive science known as predictive processing (a.k.a. active inference). We unpack how this approach connects sense-making and aesthetic experiences through the idea of an 'epistemic arc', consisting of three parts (curiosity, epistemic action and aha experiences), which we cast as aspects of active inference. We then show how epistemic arcs are built and sustained by artworks to provide us with those satisfying experiences that we tend to call 'aesthetic'. Next, we defuse two key objections to this approach; namely, that it places undue emphasis on the cognitive component of our aesthetic encounters-at the expense of affective aspects-and on closure and uncertainty minimization (order)-at the expense of openness and lingering uncertainty (change). We show that the approach offers crucial resources to account for the open-ended, free and playful behaviour inherent in aesthetic experiences. The upshot is a promising but deflationary approach, both philosophically informed and psychologically sound, that opens new empirical avenues for understanding our aesthetic encounters. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Aesthetics and predictive processing: grounds and prospects of a fruitful encounter

In the last few years, a remarkable convergence of interests and results has emerged between scholars interested in the arts and aesthetics from a variety of perspectives and cognitive scientists studying the mind and brain within the predictive processing (PP) framework. This convergence has so far proven fruitful for both sides: while PP is increasingly adopted as a framework for understanding aesthetic phenomena, the arts and aesthetics, examined under the lens of PP, are starting to be seen as important windows into our mental functioning. The result is a vast and fast-growing research programme that promises to deliver important insights into our aesthetic encounters as well as a wide range of psychological phenomena of general interest. Here, we present this developing research programme, describing its grounds and highlighting its prospects. We start by clarifying how the study of the arts and aesthetics encounters the PP picture of mental functioning (§1). We then go on to outline the prospects of this encounter for the fields involved: philosophy and history of art (§2), psychology of aesthetics and neuroaesthetics (§3) and psychology and neuroscience more generally (§4). The upshot is an ambitious but well-defined framework within which aesthetics and cognitive science can partner up to illuminate crucial aspects of the human mind. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.

Evolution, the Cognitive Sciences, and the Science of Victimization

Despite clear aversion to such labels, one of the most impactful criminological theories is rooted in cognitive science. Gottfredson and Hirschi's general theory has been repeatedly tested, replicated relatively well, and has since reached beyond its original scope to explain other important outcomes like victimization. However, the work never viewed itself as part of a larger scientific landscape and resisted the incursion of neuroscience, cognitive science, and evolutionary theory from the start. This missed opportunity contributes to some of the theory's shortcomings. We begin by considering relevant literatures that were originally excluded and then conduct a new analysis examining the cognitive underpinnings of victimization in a high-risk sample of adolescents. We used the Future of Families and Child Wellbeing Study (n = 3,444; 48% female; 49% Black, 25% Hispanic) which contained sound measures of self-control and intelligence, as well as four types of adolescent victimization. Self-control was robustly associated with all forms of victimization, whereas intelligence had generally no detectable effect. We discuss how these findings fit into a broader understanding about self-control and victimization.

A rationale for promoting cognitive science in teacher education: Deconstructing prevailing learning myths and advancing research-based practices

PURPOSE

Cognitive science is essential to designing, implementing, and evaluating instruction for enhancing student learning. However, there may not be sufficient focus on the principles of cognitive science, as some educators hold learning beliefs that may be considered cognitive myths.

PROCEDURES

This review article analyzes examples of five learning myths (learning styles, pure discovery learning, digital natives, extrinsic motivation, multitasking) and five research-based learning strategies (dual coding, direct instruction, summarization, retrieval practice, spacing). It details the research evidence for each to explain those misconceptions of learning and also those underutilized or misunderstood but effective strategies shown to benefit student learning.

CONCLUSION

Educational practices related to learning myths are widespread in education with potentially detrimental effects on student learning. We recommend that colleges of education be restructured to ensure greater emphasis on cognitive science in educator preparation programs to better promote research-based instructional strategies to meet students' learning needs.

An information-theoretic approach to the typology of spatial demonstratives

We explore systems of spatial deictic words (such as 'here' and 'there') from the perspective of communicative efficiency using typological data from over 200 languages Nintemann et al. (2020). We argue from an information-theoretic perspective that spatial deictic systems balance informativity and complexity in the sense of the Information Bottleneck (Zaslavsky et al., (2018). We find that under an appropriate choice of cost function and need probability over meanings, among all the 21,146 theoretically possible spatial deictic systems, those adopted by real languages lie near an efficient frontier of informativity and complexity. Moreover, we find that the conditions that the need probability and the cost function need to satisfy for this result are consistent with the cognitive science literature on spatial cognition, especially regarding the source-goal asymmetry. We further show that the typological data are better explained by introducing a notion of consistency into the Information Bottleneck framework, which is jointly optimized along with informativity and complexity.

"World-Informed" Neuroscience for Diversity and Inclusion: An Organizational Change in Cognitive Sciences

By nature, humans are "tojisha (participating subjects/player-witnesses)" who encounter an unpredictable real world. An important characteristic of the relationship between the individual brain and the world is that it creates a loop of interaction and mutual formation. However, cognitive sciences have traditionally been based on a model that treats the world as a given constant. We propose incorporating the interaction loop into this model to create "world-informed neuroscience (WIN)". Based on co-productive research with people with minority characteristics that do not match the world, we hypothesize that the tojisha and the world interact in a two-dimensional way of rule-based and story-based. By defining the cognitive process of becoming tojisha in this way, it is possible to contribute to the various issues of the real world and diversity and inclusion through the integration of the humanities and sciences. The critical role of the brain dopamine system as a basis for brain-world interaction and the importance of research on urbanicity and adolescent development as examples of the application of WIN were discussed. The promotion of these studies will require bidirectional translation between human population science and animal cognitive neuroscience. We propose that the social model of disability should be incorporated into cognitive sciences, and that disability-informed innovation is needed to identify how social factors are involved in mismatches that are difficult to visualize. To promote WIN to ultimately contribute to a diverse and inclusive society, co-production of research from the initial stage of research design should be a baseline requirement.

Making sense of domain specificity

The notion of domain specificity plays a central role in some of the most important debates in cognitive science. Yet, despite the widespread reliance on domain specificity in recent theorizing in cognitive science, this notion remains elusive. Critics have claimed that the notion of domain specificity can't bear the theoretical weight that has been put on it and that it should be abandoned. Even its most steadfast proponents have highlighted puzzles and tensions that arise once one tries to go beyond an initial intuitive sketch of what domain specificity involves. In this paper, we address these concerns head on by developing an account of what it means for a cognitive mechanism to be domain specific that overcomes the obstacles that have made domain specificity seem so problematic. We then apply this understanding of domain specificity to one of the key debates that it has figured prominently in-the rationalism-empiricism debate concerning the origins of cognitive traits-and introduce several related theoretical notions that work alongside domain specificity in helping to clarify what makes a view more (or less) rationalist. This example illustrates how the notion of domain specificity can, and should, continue to play a central role in ongoing debates in cognitive science.

The curious origins of ownership

What are the origins of ownership as a conceptual domain? By combining experimental evidence from cognitive science, a theoretical proposal from developmental psychology, and the computational framework of reinforcement learning, I argue that ownership concepts can develop as a by-product of our curiosity-based exploration and become grounded via our experience of control in physical and social environments.

The feasibility of ideography as an empirical question for a science representational systems design

The possibility of ideography is an empirical question. Prior examples of graphic codes do not provide compelling evidence for the infeasibility of ideography, because they fail to satisfy essential cognitive requirements that have only recently been revealed by studies of representational systems in cognitive science. Design criteria derived from cognitive principles suggest how effective graphic codes may be engineered.

Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .

Natural logic and baby LoTH

Language-of-thought hypothesis (LoTH) is having a profound impact on cognition studies. However, much remains unknown about its basic primitives and generative operations. Infant studies are fundamental, but methodologically very challenging. By distilling potential primitives from work in natural-language semantics, an approach beyond the corset of standard formal logic may be undertaken. Still, the road ahead is challenging and long.

Is core knowledge in the format of LOT?

Object individuation provides a test case for the claim that infants already have a prelinguistic language-of-thought (LOT). By 12 months, infants represent several sortal-kinds: Object, agent, animate, and perhaps artifact. Infants have also encountered many words for object kinds, animals, people, and artifacts, therefore it remains a viable hypothesis that language learning may play a causal role in the acquisition of sortal-kinds, contra Quilty-Dunn et al.

Representational structures only make their mark over time: A case from memory

Memory structures range across the dimensions that distinguish language-like thought. Recent work suggests agent- or situation-specific information is embedded in these structures. Understanding why this is, and pulling these structures apart, requires observing what happens under major changes. The evidence presented for the language-of-thought (LoT) does not look broadly enough across time to capture the function of representational structure.

Do nonlinguistic creatures deploy mental symbols for logical connectives in reasoning?

Some nonlinguistic systems of representation display some of the six features of a language-of-thought (LoT) delineated by Quilty-Dunn et al. But they conjecture something stronger: That all six features cooccur homeostatically in nonlinguistic thought. Here I argue that there is no good evidence for nonlinguistic deductive reasoning involving the disjunctive syllogism. Animals and prelinguistic children probably do not make logical inferences.

The language of tactile thought

The target article argues that language-of-thought hypothesis (LoTH) is applicable to various domains, including perception. However, it focusses exclusively on the visual case, which is limited in this regard. I argue for two ideas in this commentary: first, their case can be extended to other modalities such as touch; and second, the status of those six criteria needs to be further clarified.

Is evidence of language-like properties evidence of a language-of-thought architecture?

We argue that Quilty-Dunn et al.'s commitment to representational pluralism undermines their case for the language-of-thought hypothesis as the evidence they present is consistent with the operation of the other representational formats that they are willing to accept.

Toward biologically plausible artificial vision

Quilty-Dunn et al. argue that deep convolutional neural networks (DCNNs) optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision - found in reinforcement-learning agents navigating artificial three-dimensional environments - can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents' performance, lending some indirect support to the language-of-thought hypothesis (LoTH).

Never not the best: LoT and the explanation of person-level psychology

As Quilty-Dunn et al. observe, the language-of-thought hypothesis (LoTH) has fallen out of favor in philosophy. I will support the arguments made for its rehabilitation by Quilty-Dunn et al. by reviewing old, but still potent arguments for LoTH, and briefly criticizing recent proposed alternatives to LoT, such as Frances Egan's deflationism and Eric Schwitzgebel's dispositionalism, revealing inadequacies in such antirepresentational, antisyntactic theories.

The reemergence of the language-of-thought hypothesis: Consequences for the development of the logic of thought

Quilty-Dunn et al. defended the reemergence of language-of-thought hypothesis (LoTH). My commentary builds up implications for the study of the development of our logical capacities. Empirical support for logically augmented LoT systems calls for the investigation of their logical primitives and developmental origin. Furthermore, Quilty-Dunn et al.'s characterization of LoT helps the quest for the foundation of logic by dissociating logical cognition from natural language.

Stop me if you've heard this one before: The Chomskyan hammer and the Skinnerian nail

The target article signal boosts important ongoing work across the cognitive sciences. However, its theoretical claims, generative value, and purported contributions are - where not simply restatements of arguments extensively explored elsewhere - imprecise, noncommittal, and underdeveloped to a degree that makes them difficult to evaluate. The article's apparent force results from engaging with straw rather than steel opponents.

The language-of-thought hypothesis as a working hypothesis in cognitive science

The target article attempted to draw connections between broad swaths of evidence by noticing a common thread: Abstract, symbolic, compositional codes, that is, language-of-thoughts (LoTs). Commentators raised concerns about the evidence and offered fascinating extensions to areas we overlooked. Here we respond and highlight the many specific empirical questions to be answered in the next decade and beyond.

Neither neural networks nor the language-of-thought alone make a complete game

Cognitive science has evolved since early disputes between radical empiricism and radical nativism. The authors are reacting to the revival of radical empiricism spurred by recent successes in deep neural network (NN) models. We agree that language-like mental representations (language-of-thoughts [LoTs]) are part of the best game in town, but they cannot be understood independent of the other players.

The computational and the representational language-of-thought hypotheses

There are two versions of the language-of-thought hypothesis (LOT): Representational LOT (roughly, structured representation), introduced by Ockham, and computational LOT (roughly, symbolic computation) introduced by Fodor. Like many others, I oppose the latter but not the former. Quilty-Dunn et al. defend representational LOT, but they do not defend the strong computational LOT thesis central to the classical-connectionist debate.

Scalable cognitive modelling: Putting Simon's (1969) ant back on the beach

A classic goal in cognitive modelling is the integration of process and representation to form complete theories of human cognition (Estes, 1955). This goal is best encapsulated by the seminal work of Simon (1969) who proposed the parable of the ant to describe the importance of understanding the environment that a person is embedded within when constructing theories of cognition. However, typical assumptions in accounting for the role of representation in computational cognitive models do not accurately represent the contents of memory (Johns & Jones, 2010). Recent developments in machine learning and big data approaches to cognition, referred to as scaled cognitive modelling here, offer a potential solution to the integration of process and representation. This article will review standard practices and assumptions that take place in cognitive modelling, how new big data and machine learning approaches modify these practices, and the directions that future research should take. The goal of the article is to ground big data and machine learning approaches that are emerging in the cognitive sciences within classic cognitive theoretical principles to provide a constructive pathway towards the integration of cognitive theory with advanced computational methodology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Cognitive perspectives on maintaining physicians' medical expertise: III. Strengths and weaknesses of self-assessment

Is self-assessment enough to keep physicians' cognitive skills-such as diagnosis, treatment, basic biological knowledge, and communicative skills-current? We review the cognitive strengths and weaknesses of self-assessment in the context of maintaining medical expertise. Cognitive science supports the importance of accurately self-assessing one's own skills and abilities, and we review several ways such accuracy can be quantified. However, our review also indicates a broad challenge in self-assessment is that individuals do not have direct access to the strength or quality of their knowledge and instead must infer this from heuristic strategies. These heuristics are reasonably accurate in many circumstances, but they also suffer from systematic biases. For example, information that feels easy to process in the moment can lead individuals to overconfidence in their ability to remember it in the future. Another notable phenomenon is the Dunning-Kruger effect: the poorest performers in a domain are also the least accurate in self-assessment. Further, explicit instruction is not always sufficient to remove these biases. We discuss what these findings imply about when physicians' self-assessment can be useful and when it may be valuable to supplement with outside sources.

Could Bayesian cognitive science undermine dual-process theories of reasoning?

Computational-level models proposed in recent Bayesian cognitive science predict both the "biased" and correct responses on many tasks. So, rather than possessing two reasoning systems, people can generate both possible responses within a single system. Consequently, although an account of why people make one response rather than another is required, dual processes of reasoning may not be.

Why is system 1/system 2 switching affectively loaded?

Why are only some occasions of system 1 to system 2 switching affectively loaded? This commentary not only draws attention to this neglected phenomenon, but also shows how research in philosophy and the social and cognitive sciences sheds light on it, doing so in ways that may help answer some of the open questions that De Neys's paper highlights.

Cognitive Science Progresses Toward Interactive Frameworks

Despite its many twists and turns, the arc of cognitive science generally bends toward progress, thanks to its interdisciplinary nature. By glancing at the last few decades of experimental and computational advances, it can be argued that-far from failing to converge on a shared set of conceptual assumptions-the field is indeed making steady consensual progress toward what can broadly be referred to as interactive frameworks. This inclination is apparent in the subfields of psycholinguistics, visual perception, embodied cognition, extended cognition, neural networks, dynamical systems theory, and more. This pictorial essay briefly documents this steady progress both from a bird's eye view and from the trenches. The conclusion is one of optimism that cognitive science is getting there, albeit slowly and arduously, like any good science should.

Linguistic concepts are self-generating choice architectures

While the idea of a 'concept' has been defined in diverse ways, researchers in the cognitive science of language have largely agreed that linguistic concepts are objects, whether mental or physical, that bits of language stand for. This O-axis view (where O = object), focusing on sign-object relations, sees linguistic concepts as ideas that stand in a static relation to signs, with the function of mediating relations between agents and their environments. But this is only half the story. Because every linguistic concept is moored to a bit of language, and bits of language are mostly learned and encountered in sequences of social interaction, then we must look not only at what signs stand for (their objects), but at the interpretants, or rational responses, that they elicit. By focusing on sign-interpretant relations, and thus taking an I-axis view (where I = interpretant), we not only acknowledge the direct link between concepts and social interaction, we also discover causal mechanisms that explain how linguistic concepts are distributed in relatively stable form in populations. We find that while concepts are indeed mental objects, they function as choice architectures in the dynamic flow of situated language usage. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Toward an Atlas of Canonical Cognitive Mechanisms

A central goal in Cognitive Science is understanding the mechanisms that underlie cognition. Here, we contend that Cognitive Science, despite intense multidisciplinary efforts, has furnished surprisingly few mechanistic insights. We attribute this slow mechanistic progress to the fact that cognitive scientists insist on performing underdetermined exercises, deriving overparametrized mechanistic theories of complex behaviors and seeking validation of these theories to the elusive notions of optimality and biological plausibility. We propose that mechanistic progress in Cognitive Science will accelerate once cognitive scientists start focusing on simpler explananda that will enable them to chart an atlas of elementary cognitive operations. Looking forward, the next challenge for Cognitive Science will be to understand how these elementary cognitive processes are pieced together to explain complex behavior.

Bringing cumulative technological culture beyond copying versus reasoning

The dominant view of cumulative technological culture suggests that high-fidelity transmission rests upon a high-fidelity copying ability, which allows individuals to reproduce the tool-use actions performed by others without needing to understand them (i.e., without causal reasoning). The opposition between copying versus reasoning is well accepted but with little supporting evidence. In this article, we investigate this distinction by examining the cognitive science literature on tool use. Evidence indicates that the ability to reproduce others' tool-use actions requires causal understanding, which questions the copying versus reasoning distinction and the cognitive reality of the so-called copying ability. We conclude that new insights might be gained by considering causal understanding as a key driver of cumulative technological culture.

Context, intersectionality, and resilience: Moving toward a more holistic study of bilingualism in cognitive science

OBJECTIVE

Bilingual experiences are diverse, vibrant, and multidimensional. Yet, prior research has often homogenized bilingualism and based outcomes upon monolingual norms. Framing monolinguals as the norm distorts the reality of bilingual experiences. To promote a more diverse and inclusive study of bilingualism, we propose a theoretical and methodological paradigm shift. Bilinguals exist in different networks, cultural contexts, and individual and societal settings, all of which may lead to differential cognitive and linguistic outcomes that will be lost if left unexamined. Bilingual interactional contexts occur within extensive environmental and ecological systems, and may lead to different outcomes based on experiences within these systems. We seek to recognize these interactional contexts and how, as researchers, we can strive to better understand the complexities of bilingual populations.

METHOD

We propose incorporating more diverse theoretical frameworks-including raciolinguistics, an intersectional resiliency perspective, and an ecological approach-so that researchers can begin to think about how bilingual experiences are shaped before study participants enter the lab. Included also are methodological considerations that will improve our understanding of bilinguals' intersectional experiences. We offer suggestions for becoming more diverse and inclusive in our research.

CONCLUSION

We encourage scientists to take a more holistic and nuanced approach to understanding how individual and contextual factors affect our study populations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Beyond Single-Mindedness: A Figure-Ground Reversal for the Cognitive Sciences

A fundamental fact about human minds is that they are never truly alone: all minds are steeped in situated interaction. That social interaction matters is recognized by any experimentalist who seeks to exclude its influence by studying individuals in isolation. On this view, interaction complicates cognition. Here, we explore the more radical stance that interaction co-constitutes cognition: that we benefit from looking beyond single minds toward cognition as a process involving interacting minds. All around the cognitive sciences, there are approaches that put interaction center stage. Their diverse and pluralistic origins may obscure the fact that collectively, they harbor insights and methods that can respecify foundational assumptions and fuel novel interdisciplinary work. What might the cognitive sciences gain from stronger interactional foundations? This represents, we believe, one of the key questions for the future. Writing as a transdisciplinary collective assembled from across the classic cognitive science hexagon and beyond, we highlight the opportunity for a figure-ground reversal that puts interaction at the heart of cognition. The interactive stance is a way of seeing that deserves to be a key part of the conceptual toolkit of cognitive scientists.

The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures - that is, LoTs - only prove more flexible and well-supported over time.

Cognitive science theory-driven pharmacology elucidates the neurobiological basis of perception-motor integration

An efficient integration of sensory and motor processes is crucial to goal-directed behavior. Despite this high relevance, and although cognitive theories provide clear conceptual frameworks, the neurobiological basis of these processes remains insufficiently understood. In a double-blind, randomized placebo-controlled pharmacological study, we examine the relevance of catecholamines for perception-motor integration processes. Using EEG data, we perform an in-depth analysis of the underlying neurophysiological mechanisms, focusing on sensorimotor integration processes during response inhibition. We show that the catecholaminergic system affects sensorimotor integration during response inhibition by modulating the stability of the representational content. Importantly, catecholamine levels do not affect the stability of all aspects of information processing during sensorimotor integration, but rather-as suggested by cognitive theory-of specific codes in the neurophysiological signal. Particularly fronto-parietal cortical regions are associated with the identified mechanisms. The study shows how cognitive science theory-driven pharmacology can shed light on the neurobiological basis of perception-motor integration and how catecholamines affect specific information codes relevant to cognitive control.

Evaluating the impact of a flipped classroom model based on cognitive science of learning strategies in a pharmacotherapy course

INTRODUCTION

The purpose of this study was to determine the impact of a flipped classroom method based on cognitive science of learning strategies on student performance and experience in a third-year pharmacotherapy course.

METHODS

The cognitive science of learning flipped classroom (CSL-FC) strategies in this study included pre-class learning (Preview), in-class application to cases (Retrieval), after-class learning (Spaced Retrieval), and post-module reflection (Deliberate Reflection) in a required pharmacotherapy course. During fall 2017, one instructor piloted the CSL-FC method. During fall 2018, this method expanded to four instructors. All other instructors used traditional lecture-based methods. The same multiple-choice exam questions were used both years. The average exam question scores between teaching methods were compared by independent t-test. Student focus groups were conducted after the 2017 semester. In 2018, students were surveyed using a 5-point Likert rating (1 = strongly agree, 5 = strongly disagree) to evaluate their experience.

RESULTS

The 2017 and 2018 classes included 132 and 137 students, respectively. During the two years, exam question scores were significantly better with CSL-FC (n = 136 questions) compared to traditional (n = 110 questions) (88.8% vs 84.9%, respectively; P = .02). The focus group analysis revealed three main themes including a "love-hate relationship," "time," and "it works." Student agreement to the survey question "the cognitive science of learning flipped classroom helped me learn" was 2.18 (SD 1.12).

CONCLUSIONS

Implementing a flipped classroom approach based on cognitive science of learning strategies positively impacted student performance and experience in a pharmacotherapy course.

The Value of Statistical Learning to Cognitive Network Science

To study the human mind is to consider the nature of associations-how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar of statistical learning (SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

A Markov Mixed-Effect Multinomial Logistic Regression Model for Nominal Repeated Measures with an Application to Syntactic Self-Priming Effects

Syntactic priming effects have been investigated for several decades in psycholinguistics and the cognitive sciences to understand the cognitive mechanisms that support language production and comprehension. The question of whether speakers prime themselves is central to adjudicating between two theories of syntactic priming, activation-based theories and expectation-based theories. However, there is a lack of a statistical model to investigate the two different theories when nominal repeated measures are obtained from multiple participants and items. This paper presents a Markov mixed-effect multinomial logistic regression model in which there are fixed and random effects for own-category lags and cross-category lags in a multivariate structure and there are category-specific crossed random effects (random person and item effects). The model is illustrated with experimental data that investigates the average and participant-specific deviations in syntactic self-priming effects. Results of the model suggest that evidence of self-priming is consistent with the predictions of activation-based theories. Accuracy of parameter estimates and precision is evaluated via a simulation study using Bayesian analysis.