United States

Self-regulated learning (SRL) is essential for directing one’s own learning. While its importance is widely recognized, empirical research on how SRL interacts with cognitive, metacognitive, and personality traits remains limited. This study extends our previous work by exploring how these traits influence children’s information-seeking behaviors.

We interviewed 134 children (ages 8-11) about learning a concept independently and assessed metacognitive ability and personality traits using the Big Five Questionnaire – Children Version.

Results revealed that, while Extraversion did not predict the chosen source, other personality traits were significantly linked to SRL components. Children higher in Agreeableness were more likely to choose to learn from human-sources, and Openness to Experience was associated with greater enjoyment of learning (p=0.039).

Higher metacognitive ability was slightly associated with lower achievement expectancies, consistent with literature contrasting metacognition with wishful thinking.

These results underscore the role of individual variability in shaping SRL, informing tailored interventions to support SRL.

CogSci 2025

How Metacognition and Personality Traits Shape Self-Regulated Learning?

comparative analysis

cognitive neuroscience

statistics

learning

education

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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. 
<br><br>
* **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.
<br><br>

#### 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.

Error monitoring is commonly studied using various inhibitory control tasks, involving response withholding, response cancellation, or response selection. However, it remains unclear whether there is a common neural mechanism underlying error monitoring across these tasks or if it is specific to distinct types of cognitive failures. To identify both similarities and differences in the neural processing of errors across go/no-go, stop-signal, and flanker tasks, we employed microstate analysis. This method allows to study the dynamically evolving topographical patterns of neural activity throughout the brain. Our results revealed that the early phase of error monitoring in all tasks predominantly engages the supplementary motor area. In addition, we observed task-specific neural activity encompassing visual and motor areas in the go/no-go task, the dorsal part of the anterior cingulate cortex in the stop-signal task, and its ventral part in the flanker task. These findings suggest that error monitoring involves a collection of interconnected cognitive processes, rather than a uniform mechanism across tasks.

Are there different types of error monitoring? A microstates analysis of error-related brain activity across three tasks

Widely used analogies in science textbooks relate unfamiliar phenomena to more familiar everyday objects. However, literature on the illusion of explanatory depth suggests that people are highly overconfident in their understanding of the causal processes underlying how everyday objects and devices work. Thus, such analogies may inflate judgments of understanding (JOUs) for to-be-learned scientific explanations, even when the analogies are presented too superficially to aid understanding. A first experiment showed that superficial analogies notably increase JOUs for geology phenomena when presented and judged as individual sentences. However, a second experiment showed that analogies did not increase the more global JOUs made about the longer segments of the textbook in which the analogies were embedded. The superficial analogies did not help (and sometimes harmed) actual understanding of the geology concepts, suggesting that analogies could sometimes increase the general overconfidence that readers have about their understanding of what they read.

Do Analogies in Geoscience Textbooks Inflate Judgments of Understanding?

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.

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

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.

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Are there different types of error monitoring? A microstates analysis of error-related brain activity across three tasks

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