United States

This paper presents a framework for understanding metacognition as a distinct domain of skill, drawing on established research in motor and cognitive domains. It proposes that metacognitive expertise shares key characteristics with other skill domains, including goal-directed action, hierarchical organization, declarative and procedural knowledge, and automatization. By integrating theoretical and empirical insights, this paper aims to establish a comprehensive model of metacognitive skill development, with implications for research and practical applications in education, therapy, and beyond.

CogSci 2025

Metacognition as a domain of skill

skill acquisition and learning

psychology

knowledge representation

philosophy

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
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#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

poster

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
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* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
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* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
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#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Humans are enmeshed in many hierarchical relationships, such as that between a parent and child. Leaders in dominance hierarchies are typically strong and intimidating, while leaders in prestige hierarchies are respected for their expertise. In an ongoing study (N=13), we ask whether preschoolers ages 4–5 have different expectations about how dominant and prestigious leaders divide resources. Children learned about two social groups, the dominant Glerks and prestigious Zonks. Children watched a leader and subordinate from each group pick five apples and divide them into two baskets in a 5-0 or 3-2 split. We then pointed to one basket and asked children whether the leader or subordinate took that share of apples. In preliminary results, we find that children expect the dominant (76.9%) but not prestigious leader (46.2%) to take all of the apples, which suggests that children expect dominant leaders to claim a larger share of resources.

Children’s expectations of dominant and prestigious leaders 

Number words are often used in noun phrases referring to objects (e.g., “two balls”). This usage may suggest to children that numbers quantify same-kind sets. We investigated this hypothesis using a novel task that assessed English-speaking children’s (aged 3-11 years) preference for same-kind sets when asked for two or three objects. Across four experiments (N=167) children’s preference for same-kind sets was evaluated at the basic level (cows, horses, etc.), subordinate level (grey horses, brown horses), and superordinate level (animals, food). Children did not show any bias at the subordinate level, but they tended to produce same-kind sets when presented with items that differed at the basic or superordinate level. At the basic level, counting fluency was associated with children’s production of same-kind sets. Our results suggest that children interpret number words as referring to same-kind sets and this bias is particularly strong in children with limited number knowledge.

Homogeneity Bias in Small Number Word Comprehension

Large-scale annotated data is essential for age prediction in social media, yet obtaining such data is costly. Age is a key psychological and cognitive marker influencing communication and social behavior. Understanding age-related patterns in online interactions can provide insights into cognitive development and identity formation. To address data limitations, we propose a semi-supervised multimodal regression model leveraging Transformer-based variational autoencoders to infer age from textual and social features. This approach aligns with cognitive and social science theories on age-related behavioral patterns. Our framework effectively utilizes unlabeled data, reducing annotation dependency while enhancing predictive accuracy. Empirical results demonstrate superior performance over traditional classification and supervised baselines, advancing interdisciplinary research in age inference and online behavior modeling.

Age Inference on both Textual and Social Perspectives with Semi-supervised Learning

A child invents a game, describes its rules, and in an instant, we can play it, judge progress, and even suggest new variations. What mental representations enable such flexible reasoning? We build on recent work formalizing naturally expressed goals as a type of program, grounding linguistic descriptions into precise scoring systems. To support this notion, we study human-created objectives in a physics game environment. We leverage the formal representations to quantitatively analyze relationships between reward geometry, goal complexity, and perceived difficulty. We then propose a proof-of-concept of a computational goal inference method using these program representations and behavioral demonstrations, offering a concrete proposal of how humans reason about others' goals.

Goal Inference using Reward-Producing Programs in a Novel Physics Environment

Life often presents choices that are not mutually exclusive, yet there has been insufficient research on human learning and directed exploration involved in combinatorial settings. We investigated human behavior in a four-armed combinatorial bandit (CB) task (N=107) where participants combined "nutrients" affecting required nurture time of virtual plants. Participants demonstrated effective learning but converged to suboptimal strategies, preferring combinations of one or two options. To model learning, two computational models were proposed and compared: a naïve extension of upper confidence bound (NaiveUCB), and a linear UCB model (LinUCB), both incorporating heuristic components. The NaiveUCB model with penalty for multiple selections, value decay, stickiness, and recency-based credit assignment best explained behavior, outperforming both LinUCB and simplified variants, suggesting that humans may navigate uncertainty through simple heuristics rather than sophisticated estimation. These findings extend our understanding of exploration and credit assignment in CB, and provide insight into daily decision making.

Modeling human learning and exploration in a temporal combinatorial bandit task

Convolutional neural networks (CNNs) not only recognize objects with high accuracy, but also acquire from images abstract statistical concepts such as numerosity and correlations. However, it remains unclear whether the CNN architectures implement inductive biases that mimic human biases in statistical judgments. In this paper, we examined whether VGG-19 models, a popular CNN architecture, that are trained to make correlation judgments from scatterplots display human-like biases. In comparisons between model predictions and human data, we found that there was a high correspondence between human biases and machine biases in VGG-19 models. Using explainable AI visualization with saliency maps to unpack the regions on which VGG-19 rely to make correlation judgments, we found that the late layers of the model tend to focus on regions similar to human participants’ fixation distributions as captured by eye tracking. We further demonstrate that such models were nearly sufficient to predict human data at an accuracy level rivaling the state-of-the-art model trained on human data in three large-scale correlation discrimination datasets. Our results suggest that VGG-19 models may employ strategies that are similar to those used by human participants for statistical judgments from visual graphs and, therefore, pave the way to address human cognitive biases in visualization-based statistical judgments through the lens of deep neural networks.

VGG-19 Displays Human-like Biases in Statistical Judgment from Visual Graphs 

Human adults think of the natural world as orchestrated by rules, yet many of them are neither equally rigid nor clearly evident. Some are beset by exceptions, and others are not intuitive. The problem of rule learning is especially salient in development, as children are continuously learning how the world works. What cognitive mechanisms underpin this rule learning? 
We propose a computational model for formulating and testing hypotheses in naturalistic contexts, that combines Bayesian inference under uncertainty over self-generated and social evidence with formal rules and optimistic information-seeking heuristics. We validate our model experimentally, showing that it explains 7- to 10-year-olds' behavior in a rule-based, physical task, including the distribution and the types of evidence children sampled. The proposed model outperforms both a purely rule-based Bayesian hypothesis search and a resource-rational random sampling approach. Our results suggest that children implement an internal mechanism for generating and testing a limited number of hypotheses, including formal programmatic rules and heuristics generated from salient problem features to seek more evidence when formal rule generation fails.

Think outside the box: Making up casual hypotheses from unreliable evidence

Human decision-making involves navigating unbounded spaces of possible goals, subgoals, and action sequences. Yet, computational models typically assume pre-defined option sets. This creates a critical gap between the algorithms developed in cognitive science research on decision-making and the open nature of real-world decisions. We propose that option generation in open-ended settings operates as a search through structured decision space. Drawing on foraging theory, we hypothesized that option generation follows Lévy flight distributions, a pattern observed in both spatial foraging and memory retrieval. We found that the inter-generation time between consecutive responses in open-ended option generation problems approximated a Lévy distribution, while semantic distances demonstrated properties of heavy-tailed distributions. These findings reveal connections between action planning, information search, and memory retrieval, suggesting shared computational principles in how humans explore unbounded decision spaces.

Naturalistic action sampling as foraging in the option space

What is conveyed by sensory metaphor? Zhu et al. (2024) found that concepts metaphorized by the same sensory words were more closely aligned in dual categorization (IAT) tasks than was predicted by their alignments in word embedding models. The present study utilized 132 distinct IATs to measure conceptual alignment of words metaphorized by 99 sensory metaphors to test generalizability. This time, affective alignments were additionally measured: A semantic differential method and principal components analysis yielded a three-dimensional affective vector space, which was used to compute a measure of affective alignment. Linear models showed that affective alignments and conceptual alignments (from word embedding models) each predicted independent portions of the variance in the IAT data. Even with both of these sources of variation taken into account, concepts sharing a common sensory metaphor were more aligned than those paired at random. Sensory metaphors may simultaneously convey affective, semantic, and perceptual meaning.

Conceptual and affective alignment in sensory metaphor

Working memory (WM) is a core driver of cognition, supporting executive control, decision-making, and learning. In reward learning, WM works alongside slower reinforcement learning (RL) to establish associations between states, actions, and rewards. WM's capacity is highly limited, necessitating careful allocation of WM resources to optimize performance. How humans manage this WM constraint during reward learning, storing valuable information while discarding superseded data, remains an open question. In this study, we utilize a dynamic reward learning task to isolate rapid WM processes from slower RL mechanisms during reward learning. Through computational modeling we explore the operations humans use to allocate their limited WM resources efficiently. Our findings show strategies including (1) reward-dependent memory operations (write, forgetting, and over-write probabilities) and (2) strategic clearance policies (removing redundant and task-inconsistent data). This research clarifies WM's role in reward learning, highlighting the importance of WM operations in supporting complex behavior.

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Children’s expectations of dominant and prestigious leaders

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