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.

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.

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.

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).

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.

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'.

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'.

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.

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.

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.

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).

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.

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.

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.

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.

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 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.

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.