United States

Baby-schema is a social perception phenomenon describing how infantile facial features and their abstract representation in animals and objects automatically engage human caregiving responses. While often considered universal, its cross-cultural variability remains untested outside large-scale industrialized communities. Does cultural background and visual experience influence perceptual and behavioral biases of baby-schema?

We examined responses to manipulated infant faces in two Indigenous Malaysian (Batek, Temiar) and an urban German community. In a pre-registered eye-tracking task, participants viewed human and cat face pairs differing only in baby-schema-level (high vs. low) and completed a pre-registered binary forced-choice task. Germans selected high baby-schema faces as more likable, cuter, younger, and healthier, while small-scale communities showed attenuated or absent effects and were less sensitive to baby-schema manipulations in a control discrimination-task. Eye-tracking showed cultural differences in gaze patterns that were not meaningfully influenced by species or baby-schema-level. A follow-up study will investigate effects of group membership.

CogSci 2025

Cultural Variability in Baby-Schema Perception: Insights on Face Perception from Two Small-Scale Malaysian and An Urban German Community

field studies

face processing

cross-cultural analysis

social cognition

eye tracking

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

When facing tasks that are difficult to solve optimally, people can construct simplifying strategies that trade off utility with cost (Ho et al., 2022, Callaway et al., 2022). How we do so is an open question, especially in domains with large, structured strategy spaces where strategy evaluation itself is costly. One proposal is that people select strategies without much online computation, by a process of (reinforcement) learning through experience (Lieder & Griffiths, 2017). We present an alternative, resource-rational metareasoning framework that integrates strategy learning with adaptively bounded amounts of online strategy evaluation. We compare these proposals using a new video game task in which players traverse a grid of moving colored tiles while respecting complex rules about valid color sequences. Players quickly discover simplifying strategies, such as “only step on red tiles,” and adapt when the environment changes to favor new strategies, in ways that are most consistent with adaptive metareasoning.

Strategy selection in complex tasks through adaptive integration of learned and online metareasoning

Human behavior is determined by both learned habits and prospective planning. Because planning is computationally expensive, humans face two meta-control challenges: They must determine when to plan and, if so, which potential futures to consider. We propose that habit learning itself could solve these meta-control problems by prioritizing which futures to explore and to what extent. We show how this notion emerges from a normative Bayesian model and test one of the resulting predictions empirically. To do so, we developed a behavioral paradigm that operationalizes model-based planning as spatial navigation through a maze. Our findings suggest that humans indeed incorporate learned habitual information during planning in a manner closely aligned with the Bayesian model. This corroborates existing reinforcement learning accounts and contributes a normative and unifying perspective.

How Habit Learning Guides Planning: A Normative View and Behavioral Evidence

Prior research suggests that learning novel faces with identity-relevant semantic information is beneficial for face encoding, increasing face discriminability on recognition tasks even when tested using a different image (Mattarozzi et al., 2019; Ünal, Akan & Benjamin, 2024). However, no study has yet examined whether the timing of semantic information presentation is important for this effect by comparing conditions where semantic information is presented either before or after seeing the face. Sixty-two young adults learned a series of 36 faces of which 24 were paired with semantic information (12 face-first, 12 semantic-first) and 12 were not (control). We found that participants showed significantly better discriminability (d’) for identities learned in the semantic-first condition compared to the face-first condition. These findings suggest that learning identity-relevant semantic information before seeing a face can optimize face memory, likely by increasing the salience of the face and providing a semantic scaffolding to bolster identity encoding.

Order Matters: Learning semantic information before seeing a face improves face memory

People exhibit systemic biases in their judgment and decision-making, and these biases are often presumed to operate outside of awareness. Nevertheless, there are surprisingly few direct empirical examinations of this question. Here, in two studies (total N = 727), we test participants’ awareness of 11 classic biases. Participants completed a series of tasks, each inducing one bias (e.g., the anchoring effect, decoy effect, halo effect, etc.), and then reported whether and how they believed they were influenced by each bias. We found that, aggregating across tasks, participants’ reports tracked how much they were actually influenced by each bias (with correlations between 0.3 - 0.45), indicating significant awareness. There were also substantial individual differences, with many participants exhibiting near-perfect awareness. This research argues against the notion that people are inherently unaware of their decision-making biases, and instead supports views that place conscious processing closer to the center of human decision-making.

Are people aware of biases in their judgment and decision-making? A rigorous large-scale test

When learning with self-testing alone, will a learner make inferences between the tested items? This study examines whether self-testing's benefits extend beyond isolated facts to support broader connections between the facts. Comparing self-testing to self-explanation (a strategy known to facilitate inferential learning), we find that while self-testing participants show superior recall of individual facts, they perform significantly worse at making connections between those facts.

Some Assembly Required: Learning Facts in Isolation Limits Inferences

Do people’s stereotype judgements align with what Bayes’ Theorem dictates their judgements should be? Although prior work suggests they do, such research has generally been carried out with White undergraduates and minority social group stimuli (e.g., McCauley & Stitt, 1978; Solanki & Cesario, 2024). To determine whether these findings hold across diverse populations and stimuli, 870 Black American adults participated in a conceptual replication study that examined the congruence between stereotype judgements and a Bayesian criterion. Using trait and social group stimuli from prior research along with novel stimuli reflecting stereotypes about social groups such as White people and police officers, correlations between stereotype judgements and the Bayesian criterion were nearly half the size of those found in prior work (r = .34, vs. r = .64, Solanki & Cesario, 2024). An ongoing follow-up experiment designed to probe potential explanations is also discussed.

Stereotyping as Bayesian Inference among Black Adults in the U.S.

Algebra is powerful but difficult. It requires reasoning about abstract relations among symbolic variables. How do we do it? On one account, algebraic expertise is a kind of visual expertise: Experts learn to deploy their attention in ways that reflect the equation’s hierarchical structure. Here, we tested this account by tracking participants’ eye movements while they viewed algebraic expressions. On Algebra trials, participants judged the algebraic equivalence of two expressions. On Search trials, participants viewed the same expressions but had to verify the location of letters, a non-algebraic task. Despite viewing identical visual displays on both tasks, participants shifted their gaze in systematically different ways. When interacting with the expressions algebraically, participants’ eye movements reflected the expression’s algebraic structure. Despite algebra’s abstractness, its practice may depend on the embodied skill of shifting one’s gaze in strategic ways. This perspective can inform mathematics education and theories of abstract reasoning.

Mathematics as visual skill: Evidence from eye movements during algebraic reasoning

Category representations are often assumed to reflect the statistical distribution individual category members. However, recent work shows that people’s category representations tend to be biased toward high-value category members. We propose that this bias stems from prioritized memory: when learning about a category, people devote more cognitive resources to remembering important or desirable items, leading to their overrepresentation in category-level representations. We test key predictions of this account behaviorally and computationally. Behaviorally, we find a strong correlation between the features people prioritize in memory and the features that dominate their spontaneous recall of category members. Computationally, we use variational autoencoders to show that when statistical learning prioritizes accuracy for certain items, these items are overrepresented when sampling from the learned category distribution. Together, these findings suggest that prioritized memory plays a key role in shaping category representations.

Prioritized memory can explain the effect of value on category representation

Detecting depression from user-generated posts on social media platforms offers significant potential for early intervention on at-risk individuals. Existing works mainly concentrate on text processing, and only a limited number incorporate images posted by users. These image-integrated methods face challenges in modeling the intricate relationships between textual and visual features. Besides, the absence of approaches that explore psychological trajectory of users by analyzing their posts over time leaves a critical gap in capturing the progression of depressive symptoms. In this paper, we propose A Time-Aware Mental State Space (T-M2S) for detecting depression from social media posts. We introduce a Cross-Modal Learning that effectively integrates text and image embeddings into sentiment-oriented unified representations. Additionally, we design a Mental State Space to analyze users’ posts over time, offering a nuanced understanding of emotional dynamics. Extensive experiments on Twitter and Reddit datasets demonstrate that T-M2S significantly outperforms state-of-the-art methods. Code and models are available at GitHub.

A Time-Aware Mental State Space for Multimodal Depression Detection on Social Media

When movement of hands is restricted, memory for words referring to hand-manipulable objects (e.g., cup or pencil) declines (Dutriaux & Gyselinck, 2016), and activity in the intraparietal sulcus decreases and reaction times to judge the size of the objects represented by those words increase (Onishi, Tobita, & Makioka, 2022). These findings suggest that body immobility influences higher-order cognitive processes. However, whether hand immobility also affects the perception of lower-level features, such as texture, remains unclear.
The aim of our study was to investigate whether changes in somatosensation caused by hand constraint affect visual judgments of roughness. As a result, hand constraint did not significantly influence either the accuracy or reaction times of visual roughness judgments. This suggests that somatosensory information is not recruited automatically during visual roughness judgments. However, we cannot exclude the possibility that participants made judgments based solely on visual information, potentially because the task was too easy.

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Strategy selection in complex tasks through adaptive integration of learned and online metareasoning