United States

Mind wandering during film viewing can impede comprehension and learning, and prior studies have reported conflicting results regarding its occurrence at event boundaries in narrative versus educational films. This study examines whether scene discontinuity – changes in time, place, character, and action – affects mind wandering differently across film genres. Replicating previous findings, we found that in the narrative film, less mind wandering occurred at event boundaries compared to other parts of the film. Conversely, in the educational film, more mind wandering was reported at event boundaries. Our analysis revealed that the narrative film exhibited higher scene discontinuity at event boundaries than the educational film. Importantly, across both film types, greater scene discontinuity at event boundaries was associated with decreased mind wandering. The differing levels of scene discontinuity between narrative and educational films may explain the contrasting patterns of mind wandering observed, highlighting the influence of film structure on cognitive engagement.

CogSci 2025

The influence of scene discontinuity on the relation between mind wandering and event boundaries

event cognition

psychology

education

### 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.

poster

Distributional semantic models (DSMs) are computational models that learn semantic relationships through word co-occurrence patterns, broadly aligning with human statistical learning mechanisms. Prior research has shown that DSMs capture not only general semantic structure but also human social biases. For example, Caliskan et al. (2017) demonstrated that pre-trained word embeddings encode associations that mirror implicit stereotypes measured by the Implicit Association Test (IAT). To better understands how DSMs acquire these biases, we examined the roles of two distinct sources of distributional information: first-order (direct co-occurrence) and second-order (indirect co-occurrence)  statistics. Our analysis revealed that nearly all biases tested could be accounted for by first-order statistics alone, while about half were significant in second-order statistics. Every bias was present in at least one of these co-occurrence types, with nuanced variation in how different topics exhibited bias across first- and second-order associations. These findings suggest that implicit biases in DSMs can be attributed to simple co-occurrence patterns, predominantly direct associations. Moreover, they support theories positing that implicit biases reflect statistical regularities in the environment rather than personal attitudes. This work highlights how these biases are embedded in natural language and how a cognitive system capable of statistical learning could acquire implicit biases through the same mechanisms that shape human semantic memory.

Decomposing Implicit Bias in Distributional Semantic Models: The Roles of First- and Second-Order Co-Occurrence

Heritage bilinguals—who learned their L1 in their childhood home and L2 at school—vary greatly in their language and literacy skills. Although much is known about heritage bilingual children’s language and literacy development, less understood are psycholinguistic processes underlying literacy skills in heritage bilingual adults, which we examined here. Spanish(L1)-English(L2) heritage bilingual adults completed psycholinguistic and reading tasks in English and Spanish. Although participants’ English and Spanish verbal fluency did not significantly differ (t=1.750, p=.118), they read sight words more efficiently in English than Spanish (t=5.371, p<.001), suggesting asymmetries in language versus literacy skills. However, participants had better recollection for and familiarity with passages read in Spanish than English (ts>2.014, ps<.04), despite lower d’ scores in Spanish than English reflecting greater uncertainty (t=1.681, p=.066). These findings suggest that memory is richer for passages read in Spanish than English, despite reading being more efficient in English than Spanish.

Heritage Language vs. Dominant Language: When Bilinguals Excel in Unexpected Ways

The speech young children hear is highly variable. For example, reduced pronunciations, where some sounds in the canonical pronunciation are naturally dropped or altered, are common even in speech to children. The present study employed a new story-guided looking method (a variation on language-guided looking) to create felicitous conditions for testing young children’s recognition of reduced pronunciations of familiar words. Experiment 1 (18-24 months, n=32) found that toddlers succeeded at recognizing clear pronunciations, but failed to recognize reduced pronunciations, even in repetition trials when target words were preceded by a clear mention of the same word in the previous sentence. In Experiment 2, 3-year-olds (35-39 months, n=17 out of 44 pre-registered, ongoing) succeeded at recognizing reduced pronunciations, and benefited from preceding repetition. Overall, these results demonstrate a powerful new method for studying children’s language comprehension under more naturalistic conditions, and highlight an important psycholinguistic development over the 2–3 year span.

Development in the comprehension of phonetically reduced spoken words

This paper explores how personalized recommendation algorithms may inadvertently create echo-chambers, leading to the emergence of political issue polarization in online networks. Using an Agent-Based Modelling (ABM) approach, we conducted two studies, simulating users in three distinct algorithm designs. In Study 1, we found that personalized 
recommendation algorithms had a small but significant effect on increasing polarization, even among rational Bayesians. In Study 2, we introduced users with novelty-seeking preferences. Contrary to previous literature, our findings suggested interventions targeting personalization bubbles are ineffective, as introducing novelty-seeking preferences had no significant effect on reducing polarization levels. Together, our findings highlight the importance of algorithmic influence in creating online polarization, offer implications for social media network design, and urge caution regarding existing interventions aimed at minimizing polarization.

Recommender Systems Issue Polarization in Social Networks

How do vision-language (VL) transformer models ground verb phrases and do they integrate contextual and world knowledge in this process? We introduce the CV-Probes dataset, containing image-caption pairs involving verb phrases that require both social knowledge and visual context to interpret (e.g., `beg'), as well as pairs involving verb phrases that can be grounded based on information directly available in the image (e.g., ``sit"). We show that VL models struggle to ground VPs that are strongly context-dependent. Further analysis using explainable AI techniques shows that such models may not pay sufficient attention to the verb token in the captions. Our results suggest a need for improved methodologies in VL model training and evaluation. The code and dataset will be available https://github.com/ivana-13/CV-Probes.

CV-Probes: Studying the interplay of lexical and world knowledge in visually grounded verb understanding

AI models are often developed to solve reasoning problems optimally.
In contrast, cognitive models focus on explaining and
predicting replicative cognitive patterns of human information
processing. And while many of the theories aim to explain
an assumed ‘general’ human reasoner, only few are aimed at
the individual. This paper addresses the challenge of the latter
by investigating the automatic generation of individualised
predictive algorithms using transformer-based models. These
models which have been trained on huge amounts of human
data, potentially exhibit built-in cognitive patterns. Leveraging
such characteristics and architecture of transformer-based
models, we outline a generalized methodology for establishing
a human-AI collaborative framework, to generate explainable
and reproducible algorithms with cross-domain applicability.
While predictive accuracy and generalizability pose less of a
problem, the bigger challenges in using machine learning approaches
or transformer-based models may be explainability
and replicability. Hence, instead of ‘just’ using such a model
for directly fitting the data, we use it to extract features and
to propose cognitive algorithms that are executable in systems
outside of the model. Using two datasets pertaining to syllogistic
and spatial reasoning, the predictive algorithms thus
generated applying the presented framework, achieve mean accuracies
of 68% and 81%, respectively. Both algorithms outperform
other established, state-of-the-art cognitive models by
far, surpassing the (previously) best state-of-the art models in
syllogistic and spatial human reasoning by 19% and 13%, respectively.

Boosting Cognitive Modelling for Human Reasoning

Event segmentation agreement refers to the degree to which individuals divide the continuous flow of information into meaningful units of experience in the same way as others. In TBI patients, low levels of agreement have been reported and associated with poorer memory performance. However, this effect has been observed with laboratory tasks, and its impact on everyday memories remains unexplored. This study therefore investigated whether the ability to segment a short video predicted personal event memory in fifteen TBI patients and their matched controls. Memory was assessed using a recall of daily events collected through the experience sampling method. With this ecological task, TBI patients exhibited deficits in both the richness and accuracy of their personal memories, the latter being significantly predicted by their level of segmentation agreement. These preliminary findings highlight the role of event segmentation in everyday memory functioning, which could offer new avenues for rehabilitation memory programs.

Event Segmentation and Memories of Daily Life After a Traumatic Brain Injury (TBI)

When viewing a painting, we can discern not only its *content* — what is being depicted, e.g., a mountain — but also its *form* — the manner in which it is depicted, e.g., an impressionist sketch. What are the origins and developmental trajectory of our capacity to distinguish content and form? In 3 experiments, we introduced participants to artists who produced scenes with distinct contents and styles. Then, participants saw a critical third scene whose content matched one artist’s drawing but whose style matched the other. Participants were asked which artist produced this critical scene. Whereas adults attributed the critical scene to an artist based on style (E1), children aged 4-7 years attributed based on content (E2; replicated on Children Helping Science in Experiment 3). This work supports two conclusions: (1) The capacity to distinguish content from form arises early; but (2) the way this capacity is applied shifts throughout development.

Who drew this? Children appreciate visual style differently than adults

The concept of age is difficult for children to understand as it requires coordinating knowledge across several domains of abstract concepts (e.g., time, number, biology). We tested 122 three- to five-year-old children on their identification of which of two figures is older, as well as on their knowledge of which of two numbers is greater and their ability to temporally order past memories. Consistent with prior research, we found that young children are influenced by size in making age judgments, demonstrating a bias to respond that someone who is bigger is older. However, we show that by age 4, children can incorporate numerical age cues to make accurate age judgments. Among other possible interpretations, these findings suggest that children may initially conflate age with size before identifying chronological time as the relevant domain for age, exhibiting a conceptual change for which acquiring numerical knowledge may play a key role.

“He’s bigger so he has to be older”: Children’s development of age concepts from 3 to 5 years old

Conditional reasoning, or reasoning with if-then statements, depends in part on knowledge. However, the mechanisms underlying this dependence are not fully understood. We propose that example knowledge—the ability to generate and categorize examples of logical possibilities—plays a central role, and therefore hypothesize that individual differences in example knowledge contribute to differences in conditional reasoning. Two studies tested this hypothesis in the domain of algebra. In Study 1, individual differences in example knowledge predicted differences in conditional reasoning about algebra when controlling for everyday conditional reasoning and general algebra knowledge. In Study 2, training designed to improve example knowledge improved conditional reasoning about algebra. We discuss implications of the findings regarding the mechanisms underlying the knowledge-dependence of conditional reasoning and the nature of individual differences in conditional reasoning.

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Decomposing Implicit Bias in Distributional Semantic Models: The Roles of First- and Second-Order Co-Occurrence

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Testing the Zeigarnik effect in spontaneous memory recall during mind-wandering

Stay up to date with the latest Underline news!

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