United States

We report here two behavioural experiments that investigated the Own Age Bias (OAB), better recognition performance of own-age vs. other-age faces, measured by the Face Inversion Effect (FIE). Both experiments employed an old/new recognition task where upright and inverted own (young adults) and other (older adults) where presented intermixed. Experiment 1 (n=48), used real-life faces, and revealed a robust OAB, where a significantly larger FIE (higher recognition for upright vs. inverted faces) was found for own vs. other age faces due to a reduced performance for upright other age faces. Experiment 2 (n=48) used standardised faces and revealed no effect of OAB, and no difference between upright own vs. other age faces. We interpret our results  in the context of the perceptual learning and faces recognition literature.

CogSci 2025

Exploring the Face Inversion Effect as an Indicator of Age Bias: The Impact of External Facial Features

face processing

cognitive neuropsychology

psychology

perception

memory

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.

Selective social learning is thought to be crucial for cumulative cultural evolution, as it helps preserve valuable but complex knowledge. Traditional models of cultural evolution have emphasized selection for performance. However, recent experiments demonstrate a potentially paradox: learning from high performers can compromise transmission. Should learners select teachers based on their performance or their teaching ability? This study examines whether teaching ability correlates with performance in algorithmic concept learning. Thirty participants learned a sorting algorithm from examples and explained the concept to beginners. A second cohort rated the helpfulness of these explanations after learning the concept themselves. Using an algorithm we developed to assess how well participants had learned the concept, we found no significant relationship between teaching effectiveness and task performance. Our results extend findings from prior research to the setting of algorithmic concept learning, and highlight a fundamental dilemma in cultural transmission.

Learning and teaching are uncorrelated in an algorithm transmission task

While convolutional neural networks (CNNs) have come to match and exceed human performance in many settings, the tasks these models optimize for are largely constrained to the level of individual objects, such as classification and captioning. Humans remain vastly superior to CNNs in visual tasks involving relations, including the ability to identify two objects as `same' or `different'. A number of studies have shown that while CNNs can be coaxed into learning the same-different relation in some settings, they tend to generalize poorly to other instances of this relation. In this work we show that the same CNN architectures that fail to generalize the  same-different relation with conventional training are able to succeed when trained via meta-learning, which explicitly encourages abstraction and generalization across tasks.

Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation

The transition from descriptive regularities to prescriptive expectations is linked to the inherence heuristic (a cognitive shortcut attributing observed associations to inherent properties), reinforcing the perception of internal characteristics as defining features of social categories and contributing to gender stereotype endorsement. We investigated how inherent reasoning and moderating factors (i.e. generics and individual characteristics) influence the endorsement of gendered activities. Using a 3 (framing: generics vs. "most" vs. "some") × 2 (typicality: typical vs. countertypical gender associations) design, 241 French participants provided descriptive and prescriptive judgments about gendered associations, with justifications coded for inherence. Results showed that generic statements increased prescriptive judgments and reliance on inherent reasoning compared to “most” statements. Inherent justifications increased prescriptive judgments for typical and reduced them for countertypical gender associations. Inherent reasoning fully mediated the effect of generics on prescriptive judgments. These findings underscore the role of language and cognition in sustaining normative gender expectations.

The Influence of Generics on Inherent Reasoning and the Endorsement of Gender Stereotypes

Searching through semantic memory involves navigating
across clusters of related items, but the current body of work
has primarily focused on search within hierarchically structured taxonomic categories (e.g., Animals). The present work explored search behavior via the verbal fluency task in thematic categories, where relatedness is construed via complementary roles in a shared environment, and ad hoc categories, where relatedness is construed via shared service to an external goal. We found strong differences across domains within
each type of category, but compared to Animals, responses in
thematic and ad hoc categories had higher average phonological similarity and word frequency. Phonology-inclusive process models provided the best account of search in taxonomic categories, but overall model performance was poor for ad hoc
categories. There were also important differences in the contribution of lexical sources to within and between-cluster transitions across domains. These results underscore the necessity of exploring lexical search and validating computational models
in categorical domains with different compositions and types
of relatedness.

Lexical Search Dynamics in Taxonomic, Thematic, and Ad hoc Categories

This paper presents a formal account of causal explanation, grounded in a theory of conversational pragmatics, and inspired by the interventionist idea that explanation is about asking and answering what-if-things-had-been-different questions. We illustrate the fruitfulness of the account, relative to previous accounts, by showing that widely recognized "explanatory virtues" emerge naturally, as do subtle empirical patterns concerning the impact of norms on causal  judgments. An extended version of the paper with further details can be found here: https://arxiv.org/pdf/2505.03732

Toward a Formal Pragmatics of Explanation

Existing research has drawn parallels from the comprehension of text to the comprehension of source code. In this study, we attempt to develop this analogy by positing and testing a notion of code cohesion, analogous to text cohesion. We also attempt to extend a known effect in text comprehension research, the reverse cohesion effect, to code contexts. Our findings provide some corroboration for code cohesion, but fail to find robust evidence for a reverse cohesion effect. This reinforces similarities between text and code comprehension but also suggests that everyday comprehension processes of code and text  might differ in meaningful ways.

Can We Extend the Reverse Cohesion Effect to Programming Contexts?

The increasing integration of large language models (LLMs) based conversational agents into everyday life raises critical cognitive and social questions about their potential to influence human opinions. Although previous studies have shown that LLM-based agents can generate persuasive content, these typically involve controlled English-language settings. Addressing this, our preregistered study explored LLMs' persuasive capabilities in more ecological, unconstrained scenarios, examining both static (written paragraphs) and dynamic (conversations via Telegram) interaction types. Conducted entirely in Hebrew with 200 participants, the study assessed the persuasive effects of both LLM and human interlocutors on controversial civil policy topics. Results indicated that participants adopted LLM and human perspectives similarly, with significant opinion changes evident across all conditions, regardless of interlocutor type or interaction mode. Confidence levels increased significantly in most scenarios. These findings demonstrate LLM-based agents' robust persuasive capabilities across diverse sources and settings, highlighting their potential impact on shaping public opinions.

Can (A)I Change Your Mind?

Pragmatic accommodation is a key factor in maintaining
smooth real-life communication, yet it is largely overlooked in
pragmatic reasoning models such as the Rational Speech Act
(RSA) model. The current study explores ways of extending
the basic RSA model: revising the listener’s belief of common
ground, adjusting the listener’s belief of speaker rationality,
and doing both simultaneously. We evaluated model
predictions for utterances varying in utility levels (i.e., how
useful an utterance is for state updating) and sentence
polarities. We find that (i) contra prior findings, low utility does
not always trigger the expected extra inferences, but high utility
does, (ii) higher utility is associated with higher speaker
rationality, and (iii) our combined model predicts that lower
speaker rationality and lower wonkiness co-occur. Theoretical
implications of these findings are discussed.

Irrational Speaker or Wonky World: Modeling Prior Revision and Prior Update

Some researchers have argued that visual representations in the periphery differ qualitatively from those in the fovea (e.g., Balas et al., 2009; Freeman and Simoncelli, 2011; Rosenholtz et al., 2012). Consistent with this proposal, He et al. (1997) showed that crowding in the periphery disrupts the ability to individuate features but doesn’t disrupt feature detection. We hypothesized that He et al.’s demonstration could be accounted for simply in terms of cortical magnification alone. We tested this hypothesis by presenting He et al.’s stimuli to a neurally realistic model of V1 (Heaton & Hummel, 2022) that incorporates cortical magnification but posits no other differences between foveal and peripheral early visual representations. The model’s performance captured He et al.’s findings, suggesting that cortical magnification alone is sufficient to account for the differences between foveal and peripheral visual perception.

Modeling the effect of cortical magnification on feature detection and individuation 

Why do we sometimes fall into repetitive negative thinking (RNT) patterns, such as rumination and worry? To address this question, I trained a meta-control model (Todd et al., 2008) on affective working memory (WM) content by providing a reward signal when items (representing thoughts) were gated into WM. The model still facilitated adaptive motor action, as in the original implementation, yet it also exhibited the defining characteristics of RNT. Specifically, its thought became repetitive (because it learned to selectively gate items into working memory); negative (because it persisted in, and only slowly extinguished, gating into WM negatively valenced  on-theme and distractor items, respectively); and difficult to control (because, after extensive learning, it had a high chance of selecting the most probable WM gating strategy, irrespective of the value of an exploration parameter). Germane to clinically relevant RNT, such as pathological rumination and worry, the model’s thinking was more unproductive when the valence of one thought sequence strongly biased the next one. This work helps to establish meta-control as a formal algorithmic framework for RNT. It may catalyze clinical research on RNT by providing a bridge to computational findings.

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