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Correlation judgments are at the core of belief formation. In previous studies of correlation judgment from 2D scatterplots, observers underestimate correlations, and display stronger underestimation biases when the scatterplot is shown in a landscape view than in a portrait view. Yet, it is unclear how these biases arise. Here, we propose that observers are Bayesian learners who perform “mental regression” using the observed data points in graph. Accordingly, judgment errors can arise from biased visual information sampling. We test our model’s predictions with two eye-tracking experiments and find that the Bayesian learning model, applied to information obtained from visual fixation data, replicates classic behavioral findings. The model also predicts trial-level estimation biases at a high accuracy level. Our study shows how computational models trained on process-level data can shed light on the cognitive mechanisms underlying belief formation, and yield theory-driven practical implications for data visualization and statistical communication.

CogSci 2022

Information sampling explains Bayesian learners’ biases in correlation judgment

### 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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CogSci 2025

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

technical paper

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





The traveling salesperson problem (TSP) is an NP-Hard problem that computers find difficult to solve. Humans are surprisingly good at solving the TSP, with solutions within 10% of optimal for problems with up to 100 points, constructed in time linear with the number of points. We propose that humans solve the TSP by initially clustering the points and then connecting them first within and then between clusters. In this study, 67 participants first clustered 40 stimuli and then solved them as TSPs. Strikingly, participants' TSP solutions perfectly followed their clusters for 52% of the stimuli. Further, participants' TSP solutions' were more congruent with their clusters for stimuli with statistically higher levels of clustered structure. This provides strong evidence for the clustering proposal. Random TSP solutions, however, showed no such congruence to cluster structure. These findings suggest that clustering might be a fundamental ability for reasoning about graph-theoretic algorithmic problems.

Use of clustering in human solutions of the traveling salesperson problem

Humans are uniquely adept at extracting structure from the world around them. It is well known that people often form hierarchical task representations during learning, even when a task does not explicitly necessitate a hierarchical representation. Still, how individuals capitalize on this structure to facilitate behavior is an open question. In the present study, we address this question by carefully examining patterns of response time switch costs in a hierarchically structured visuomotor association learning task, to adjudicate between multiple models of behavior. We find evidence that participants do appear to navigate through a hierarchical representation of stimulus-response associations as they prepare responses, rather than maintaining a non-hierarchical, flat model or being primarily affected by changes in stimulus features. These results establish the existence of hierarchical mental representations even for static visuomotor mappings, and imply that such representations are internally navigated in an orderly manner during action selection.

Climbing the Tree: Structured Hierarchical Representations in Visuomotor Maps

Three-dimensional objects pose a challenge for our visual system, since we can only view objects from a single limited perspective at a given moment. Previous work found that given a limited perspective, infants represent 3D objects as complete volumes. Our study replicated this finding in 4- to 7-year-olds and adults, using an explicit prediction measure rather than looking times. We also explored whether humans have a bias to represent visually limited 3D objects as symmetrical rather than asymmetrical across shape, size, texture, and color. Overall, there was an above-chance preference for full volumetric and symmetrical object completion that increased with age. Low-level perceptual similarity of choices did not predict participants’ choices. Moreover, we evaluated ResNet-50 neural networks on the same tasks: they represented objects as complete volumes, but did not show substantial preference for symmetrical 3D representations. This raises the possibility that incorporating human symmetry biases could improve computer vision.

Three-Dimensional Object Completion in Humans and Computational Models

Because there are many situations in our daily life in which the option space is not discrete but continuous, recently developed decision models have been able to examine the cognitive processes underlying choice in laboratory tasks with a continuous outcome space. One of the most important of these continuous models is the circular diffusion model (CDM) by Smith, which has been shown to account for continuous space data from a wide range of paradigms, including color identification, orientation, brightness, pricing. However, in addition to the inherent complexity of this model, it has become more complex in order to predict reliable data patterns, making it a tool only for experts. Here we propose a more easy version of the CDM, the EZ version, to fit the model on continuous scale data. The EZ-CDM for continuous choice space tasks can estimate the parameter values for the cognitive processes underlying without considering the response time distribution but only using traditionally favored summary statistics (i.e. the mean and variance of response time, and angular variance of accuracy.) by simple formulas that can be computed easily and needs neither theoretical knowledge of model fitting nor much programming skills. Here, we formulate the EZ method and show that, despite being easy and fast to calculate, it’s performance in recovering true parameters is acceptable.

An EZ-circular diffusion model of continuous decision processes

Affective factors such as anxiety, confidence, and motivation can impair and enhance task performance. Here, we used drift diffusion modeling (DDM) to examine how these variables affect visualization, manipulation, and decision making on a mental rotation task (MRT). The effects of affective factors on visuospatial reasoning are largely unknown, perhaps in part because analyses are generally concerned with overall accuracy and reaction time (RT), without decomposing the stages of processing. With DDM, we decompose performance on a MRT into separate processing components, particularly the speed of information update (drift rate) and the amount of evidence accumulation (decision threshold). 106 adult participants performed a two-alternative forced-choice (2-AFC) MRT, and throughout, they rated their levels of anxiety, confidence, and motivation. We found that although anxiety, confidence, and motivation all impacted drift rate, only confidence affected the decision threshold. Moreover, we observed a unique role for confidence in mediating the links between gender and model parameters, as well as a unique moderating role of motivation in this mediation. Altogether, these findings shed light on the interrelations between affective factors in accounting for mental rotation performance in men and women, including the unique combination of confidence and motivation in explaining the gender difference in mental rotation performance.

Affective Factors Affect Visuospatial Decision-making: A Dri Diffusion Modeling Approach

To achieve goals, people leverage cognitive control to adjust how they process information. Here we show that frequent adjustments in information processing strategies (e.g., response threshold) within a single task give rise to reconfiguration costs. In two experiments we induced different performance goals in a Stroop task via explicit instruction or incentives, and these goals either varied or were fixed across different blocks. Across both experiments, we find smaller adjustments in control intensity when people frequently adjust the amount of control they exert, relative to blocks in which they don’t. We show that these results can be accounted for with a model that maximizes reward rate while minimizing reconfiguration costs (proportional to the Euclidean distances between the previous and current control signals). These findings suggest that cognitive control adjustments are regularized to constrain larger adjustments in control, which has important implications for computational modeling and measurement of motivated cognitive control.

Empirical and Computational Evidence for Reconfiguration Costs During Within-Task Adjustments in Cognitive Control

People are often reluctant to reconsider their choices, sticking with their goals even when it is clear that they would be better off abandoning them. Explanations for this abound, including loss aversion, sunk costs, social and reputational pressures, and resource rational consideration of the costs of replanning. Here we propose another hypothesis: In adopting a goal, you immediately reap the rewards of gaining information about what to do and how to act. Insofar as goals are rewarding in themselves, we predict that unless a goal is specifically devalued or the costs associated with it are very high, the default is not to engage in any reconsideration at all. We test this hypothesis by creating a stripped down scenario involving choices between two goals with transparently obvious cost differentials. The task is designed to minimize other factors that might contribute to goal persistence and indeed, we test both adults and very young children on virtually the same task to ensure that the cognitive load for adults is negligible. Both adults (Experiments 1-2) and 4-6-year-old children (Experiments 3-4) choose the less costly of two goals when shown the costs and goals together. However, when participants are shown the goals first, and only then shown that their chosen goal is more costly than the alternative, participants stick with higher cost goals, unless the goals are explicitly devalued.

"Because I want to": Valuing goals for their own sake

When deciding whether and how to punish, people consider not only the potential direct consequences, but also, how their choice will affect observers’ judgements about the values and motives underlying the choice. We formalize the decision to punish as a rational communicative social action (RCSA). The model generates rational decisions to punish, incorporating anticipated observers’ judgements obtained from a recursive model of inference using an intuitive theory of mind. Using this model, we synthesize patterns of human punishment from recently published papers. RCSA thus offers a formal model of the cognitive process that humans use to balance preferences for how they are perceived, with other goals for punishing.

Modeling punishment as a rational communicative social action

We explore whether people can recognise the epistemic goal of active learners. In a novel online experiment, 110 adults watched screen recordings of other adults (``players'') manipulating objects in a 2D simulated physical microworld. Players had the goal of either identifying the magnet-like force connecting two of the objects, or their relative masses. Observers were asked to identify the learning goal of the player. By drawing from a previously collected dataset of active physical learning interactions and an ideal observer analysis, we manipulated how informative the players' actions are about the target property, and observers' level of access to the players' micro-control actions. We found observers were better at identifying the goals of successful players and of players trying to identify force, while the micro-dynamic evidence improved accuracy on identifying the mass goal. We use mixed methods to explore what cues observers used to make these judgments.

Inferring epistemic intention in simulated physical microworlds

It is commonly believed that expressions visually signal rich diagnostic information to human observers. We studied how observers interpret the dynamic expressions that people spontaneously produced during a real-life high-stakes televised game. We find that human observers are remarkably poor at recovering what events elicited others' facial and bodily expressions. Beyond simple inaccuracy, people's causal reasoning exhibits systematic model-based patterns of errors. We show that latent emotion representations can explain people's reasoning about the unseen causes of expressions. A hierarchical Bayesian model simulates which events people infer to be the cause of others' expressions by comparing the emotions inferred from the expressions against the emotions people were predicted to experience in various situations. This causal model provides a close, parameter-free fit to human causal judgments, suggesting that humans interpret expressions in the context of emotion predictions generated by a causally-structured mental model of other minds.

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Use of clustering in human solutions of the traveling salesperson problem

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