United States

CogSci 2025

Cognitive Priming Prompting Facilitates Knowledge Elicitation in Multilingual Large Language Models

analogy

computational modeling

artificial intelligence

linguistics

natural language processing

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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.

A quintessential feature of human intelligence is the ability to create ad-hoc conventions over time to accomplish shared goals efficiently. Prior research has primarily focused on unimodal communication or communication mediated by a 2D screen. We study how multimodal communication using gesture and language changes during physical collaboration. We paired human participants and used augmented reality to isolate voice and hand gestures. One participant saw a 3D virtual tower and provided instructions to the other participant, who constructed the physical tower. Participants became faster and more accurate by forming conventions that made use of both gestural and linguistic abstractions. Redundancy was used to emphasize a change in the established convention. Based on these findings, we extend unimodal probabilistic models of convention formation to multimodal settings while capturing various modality preferences. Our work serves as a building block for convention-aware and physically situated intelligent agents.

Using Gesture and Language to Establish Multimodal Conventions in Collaborative Physical Tasks

Interaction theories argue that mutual understanding between
speakers in natural conversations arises from building shared
knowledge (common ground), but no model specifies what
information is retained or under what conditions. Previous
studies have used Information Theory metrics to quantify the
dynamics of information exchanged between participants but
lack an efficient way to identify which information becomes
common ground. These attempts furthermore limited them-
selves to the study of conversation transcripts, overlooking
nonverbal cues like visuals and intonation. To address this,
we propose a method for annotating new corpora using models
trained on a subset of annotated utterances. Results show a fair
applicability (κ ≃0.3) across corpora, though this is strongly
modulated by the conversational task being investigated.

Locating strongly informative utterances in conversation using multimodal cues

Risk attitudes determine how decision-makers resolve tradeoffs when decisions involve uncertainty. The framing of a decision problem can affect these attitudes. Regulatory focus theory holds that (promotion-focused) frames that emphasize acquiring gains induce higher risk-taking than (prevention-focused) frames that emphasize avoiding losses. Here, we examine how this framing effect is moderated by individual differences in the internality of encoding style—the readiness to construe stimuli in terms of expectancies and pre-existing categories. In two experiments, participants could obtain costly information to aid their focal decision; thus, a risky choice corresponded to obtaining little or no information. Payoffs were framed to emphasize either gaining a bonus or retaining an endowed budget. The results of both experiments suggested that individuals with a more internal encoding style were more likely to be affected by the payoff framing. These results suggest that framings’ effectiveness on risk-taking depends on individual differences in cognitive processing style.

Individual differences in encoding style moderate framing effects on risk-taking

This study examines perceptual differences in speech quality discrimination using a novel ternary AX task, testing how blind and sighted participants perceive distortions in a synthesized speech signal. Two groups were tested across two difficulty conditions, involving differing levels of distortions. While sighted participants showed decreased performance as task difficulty increased, blind participants demonstrated greater stability and perceptual capacity. Additionally, groups struggled with different stimuli, suggesting that item-specific perceptual differences drive difficulty, rather than a uniform increase in task complexity. Acoustic analyses were conducted to explore potential factors influencing perception. These findings indicate that blind and sighted participants may rely on different auditory processing strategies, leading to group-specific difficulty patterns. Our results highlight the importance of individual perceptual mechanisms in speech discrimination, with implications for models of auditory perception and accessibility in speech technology.

Perceptual Strategies in Speech Quality Discrimination: A Comparison of Blind and Sighted Listeners

Emotion recognition using electroencephalography (EEG) represents a significant area of study in brain-machine interfaces. To address this multifaceted challenge, it is crucial to improve the ability of EEG features to represent emotional states. A hypergraph-based methodology allows for the depiction of higher-order spatial correlations to develop distinguishing emotional features. However, the original hypergraph may lack robustness due to potential interference among local channels. In addition, excessively coarse hypergraph granularity can result in the loss of critical information. To mitigate these issues, we propose hypergraph group learning, which aims to balance robustness with the retention of detailed information. In this study, we model temporal and spatial dependencies across varying granularities using Hypergraph Group Learning to achieve a discriminative representation of emotional features. We used multiple CNN convolutions to map EEG signals from different brain regions and time segments into a unified distribution. The multi-granularity hypergraph convolutional network (MGHGCN) is specifically designed to capture long-term temporal correlations among channels effectively. By integrating multiview fusion, we significantly improved the accuracy and robustness of EEG-based emotion recognition. Experimental results from publicly available datasets, including SEED, SEED-IV, and EMOT, validate the effectiveness of our approach, achieving precisions of 98.51 (2.46) %, 89.20 (6.13) % and 97.79 (1.31) %, respectively. These results demonstrate that our hypergraph effectively maintains both robustness and detailed information.

MGHGCN: Boosting EEG-based Emotion Recognition Through Multi-granular Hypergraph Convolutional Networks

Language models are known to absorb biases from their training data, leading to predictions driven by statistical regularities rather than semantic relevance. We investigate the impact of these biases on answer choice preferences in the Massive Multi-Task Language Understanding (MMLU) task. Our findings show that these biases are predictive of model preference and mirror human test-taking strategies even when chain of thought (CoT) reasoning is used. To address this issue, we introduce Counterfactual Prompting with Agnostically Primed CoT (APriCoT). We demonstrate that while Counterfactual Prompting with CoT alone is insufficient to mitigate bias, APriCoT effectively reduces the influence of base-rate probabilities while improving overall accuracy. Our results suggest that mitigating bias requires a slow thinking process which CoT alone may not provide as it tends to reinforce fast thinking model bias under some prompting methodologies. APriCoT is a step toward developing more robust and fair language models that can think slow.

Chain of Thought Still Thinks Fast: APriCoT Helps with Thinking Slow

Event descriptions require mapping event roles from an underlying conceptual representation to surface speech and gesture. Encodings in co-speech gesture tend to align with language-specific options that govern encodings in speech, but are relatively understudied for event roles that can be omitted in speech (e.g., argument-dropping languages like Turkish allow omission of core event roles, including agents and recipients). We examine the content of multimodal possession-transfer event descriptions across two typologically distinct languages (English, Turkish), differing in the grammaticality of argument-dropping. We find that language-specific encoding patterns heavily affect recipient and agent mentions in free event descriptions across modalities. Overall, Turkish speakers mentioned recipients and agents less frequently than English speakers. Although recipient and agent co-speech gestures were used more frequently in Turkish, they rarely contributed information beyond what was encoded in speech. This suggests that argument-dropping in Turkish occurs at a level of representation that is shared across modalities.

Language-specific event role mappings in multimodal possession-transfer event descriptions

If Peter prevents Jack from catching a falling bottle that Mike knocked over, most people would think that Mike caused the spill to a greater degree than Peter. Cases of double prevention like these are famously inconsistent with the idea that causal judgments rely on counterfactual dependence; the spill wouldn't have happened if Mike hadn't knocked the bottle over or if Peter hadn't prevented Jack from catching the bottle. But newer counterfactual models are more flexible, and they assume that people imagine different counterfactuals in proportion with their perceived normality. Following recent work showing that these newer models can account for causal judgments in cases of double prevention, here we find that normality affects such judgments. Specifically, when the productive factor is normal and the double preventer is abnormal, we find that participants preferentially rate either the productive factor or the double preventer as more causal depending on the normality of the possible preventer. Contrary to standard interpretations, then, our results suggest that cases of double prevention are actually more problematic for competing theories of causal judgment than they are for counterfactual theories.

Norms moderate causal judgments in cases of double prevention

Fantasy/symbolic play is central to theories of child cognitive development (Piaget 1962; Pellegrini 1985; Leslie 1987; Francis & Gibson 2022). Most studies suggest that children distinguish pretense from reality by their second year, though the cognitive mechanisms involved remain debated (Leslie 1987). Fantasy play is also linked to language development, including early literacy and metalinguistic awareness (Pellegrini & Galda 1982, 1991; Pellegrini 1984; Orr & Geva 2015). Garvey & Kramer (1989) identify two communicative levels in symbolic play: (i) enactment and (ii) emplotment. This study examines the grammar used by L1 Greek children while setting up scenes and giving instructions. Based on novel naturalistic data from 55 recorded sessions with 14 children (aged 2;7–6;4), we show that by 2;7, children produce counterfactual scenarios with a light verb meaning ‘pretend’. By 5;0, they employ counterfactual morphological marking in symbolic play before using it in other contexts (Amsel & Smalley 2000).

Fantasy Play and the Language of Emplotment in Greek L1 Children

The growing prevalence of artificial intelligence (AI) prompts reflections on the nature of human identity, particularly regarding perceptions of human uniqueness. Adolescents today interact with AI more frequently than any previous generation, yet little is known about the psychological implications of AI on their development. This study explores the associations among AI usage, anthropomorphism, and perceived human uniqueness in adolescents. Through a survey with 487 adolescents aged 13 to 19, we found 1) older adolescents perceived less agency and experience in humans compared to younger ones, whereas no age-related differences were observed in AI usage, anthropomorphic tendency, and perceptions of AI; 2) higher AI usage and anthropomorphic tendency were associated with reduced perceptions of human uniqueness in both agency and experience; and 3) anthropomorphism could serve as a psychological mechanism linking AI usage and perceived human uniqueness. This study contributes to broader philosophical and societal discussions about AI and human uniqueness.

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Using Gesture and Language to Establish Multimodal Conventions in Collaborative Physical Tasks

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