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This study examined how verbal probability expressions (VPEs) used in group decision-making discussions influence individuals’ decision-making processes. An online experimental task was developed to investigate how biased decisions emerge depending on the type of expression used during discussions. Scripted conversational agents were employed to experimentally manipulate the VPEs used during group discussions. 440 participants took part in the online experiment, which controlled two factors: (1) the type of VPEs used by group members during discussions and (2) the type of anchoring group decision. The results revealed an interaction between these two factors when participants perceived the agents as human-like. Specifically, confirmation bias occurred more quickly when positive VPEs were used by the agents and the anchoring probability of the group decision was low (20\%). These findings provide valuable insights into the influence of VPEs on probability decision-making during group discussions, highlighting the advantages of utilizing multiple conversational agents for investigation.

CogSci 2025

Influences of Language Expressions in Group Decision Making: Exploring Verbal Probability Expressions in Group Discussions with Conversational Agents

group behaviour

interactive behavior

quantitative behavior

statistics

decision making

human-computer interaction

psychology

### 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.

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Children express some concepts in their gestures before expressing them in speech (Goldin-Meadow, 2015). This phenomenon has been shown in several domains that are rich in visuospatial information. One other domain that can benefit from gestures is emotion expression (Kelly & Tran, 2023). In this study, we explored monolingual Turkish speaking children (N=23, Mage=8.6) and adults (N=19, Mage=35.6) in emotion recall after watching a silent video demonstrating a range of emotions. We coded participants’ emotion expression either in speech-alone or multimodally (speech plus head/body/hand gestures and/or facial expressions). Overall, children (M=13.8, SD=6.4) recalled significantly more emotions than adults (M=9.5, SD=3.8) (p=.014). They also recalled emotions significantly more multimodally (M=8, SD=7) compared to adults (M=4.9, SD=4) (p=.03). These results corroborate previous research on children’s reliance on gestures, now also extending it to the domain of emotions and by incorporating facial expressions as an alternative expression channel.

Children express emotions multimodally before expressing them in speech

Gestures and silent pauses are integral to the pragmatic, semantic, and temporal organization of speech. However, how gesture inhibition affects silent pauses in emotional context remains unexplored. This study examined how narrative type and gesture conditions affect (a) the distribution of silent pauses [short (250–500 ms), medium (500–1000 ms), and long (>1000 ms)], measured by frequency, average duration, and time ratio, while controlling for speech rate, and (b) self-reported emotional intensity. Thirty participants (Mage=20.61) narrated negative (sadness, fear, anger) and neutral (daily routine) experiences in Hindi-English under gesture-restricted (N=15) and gesture-free (N=15) conditions. A significant main effect of narrative type was found: short pauses increased in neutral narratives under the gesture-free condition, and long pauses increased in negative narratives under the gesture-restricted condition. No significant effect of gesture condition or interaction effect was observed. Gesture restriction also appeared to increase self-reported emotional intensity during negative narratives.

Gesture Restrictions effect on Silent Pauses in Emotional Narratives: An Embodied Emotion Perspective

The Speech-to-Song Illusion (STS) phenomenon, where repeated short speech utterances transform into perceived song, has drawn attention to its underlying mechanisms and cross-linguistic differences. This study examines the STS effects among Korean speakers, comparing standard Korean (non-tonal) and dialects such as Gyeongsang (pitch-accent, tonal) and Jeju (non-tonal but intonation-rich), which exhibit varying levels of linguistic tonal features. Participants (N = 60), evenly divided between standard and dialect users, evaluated 180 auditory stimuli comprising standard Korean, Gyeongsang, and Jeju utterances under controlled repetition conditions.
Results revealed significant STS effects across all groups and stimuli, with stronger effects observed for dialectal stimuli, particularly Jeju, compared to standard Korean. Interestingly, differences between standard and dialect speaker groups in STS perception were not statistically significant, suggesting that exposure to diverse linguistic environments, facilitated by modern Korean media, may homogenize perceptual responses to tonal variations. The study highlights the influence of tonal and rhythmic elements in STS perception and underscores the cultural and linguistic uniqueness of Korean as a fertile ground for exploring auditory illusions.
This research contributes to understanding the interplay of linguistic and perceptual factors in STS and opens avenues for cross-cultural comparisons and neuroscientific investigations of auditory illusions.

Exploring the Speech-to-Song Illusion: A Comparative Study of Standard Korean and Dialects

Previous studies have found that character type, voicing, or vowel type affects the sensorimotor and affective information imagined from individual characters or pseudowords in Japanese. The purpose of this study was to examine whether character type, voicing, or vowel type influence the taste information imagined from individual characters. Fifty and seven participants rated five taste information (sweet, salt, sour, bitter, and spicy) imagined from five vowels (a, i, u, e, or o) written in hiragana or katakana, respectively, in Study 1. Additionally, 40 participants rated six taste information (sweet, salt, sour, umami, bitter, and spicy) imagined from pairs of three consonant types (voiced: b, semi-voiced: p, or voiceless: h or f) and five vowels written in hiragana in Study 2. The results of these studies suggest that character types, voicing, or vowel types affect the taste information associated with individual Japanese characters.

Can you imagine the spiciness of a /hi/ sound?

This research introduces an intelligent mentoring system that utilizes behavioral logs and cognitive modeling to provide personalized learning interventions. The system analyzes semantic patterns during web browsing to estimate cognitive parameters and predict task completion times. By employing CLIP, the system evaluates semantic alignment between learning objectives and accessed resources. Our experimental study with eight postgraduate students serves as a starting point for ACT-R cognitive parameter estimation. The system tracks both content relevance and user interest as internal human dynamics to maintain engagement with study materials. By integrating ACT-R architecture to estimate working memory capacity, attention span, and anxiety levels, the system maps these parameters to observed browsing behaviors. This dual-stage approach enables real-time cognitive estimation and personalized schedule generation, establishing a foundation for adaptive learning technologies that provide tailored interventions based on semantic dynamics analysis and cognitive parameter estimation.

Developing a Mentoring System Based on Behavior Logging and Personalized Cognitive Modeling

Letter-position encoding is one of the constituent processes in
visual word recognition. While the existing models attempt to
explain letter-position encoding in English & other European
languages written using the Roman Script, letter-position
encoding in Hindi written using the Devanagari Script has not
been studied in detail. Given that Hindi is spoken/read by over
520 million people in India and the unique properties of the
Devnagari Script (Vaid & Gupta, 2002; Kandhadai & Sproat,
2010; Share et al., 2015), the current study sought to investigate
letter-position encoding in Hindi. 66 participants performed a
lexical decision task that employed six-prime conditions to
compare hypotheses from a) the position-specific coding
scheme, b) local context sensitive coding scheme and the c)
position overlap coding scheme. Interestingly, the results
showed that none of the aforementioned coding schemes could
satisfactorily explain the obtained data. These findings may be
used to question the generalizability of the extant letter-
position encoding schemes to the relatively understudied
languages such as Hindi, which use scripts different from the
Roman script.

Existing Models May Not be Able to Explain Letter-Position Encoding in Hindi: Evidence from a Priming Study

Real-time human-artificial intelligence (AI) collaboration is crucial yet challenging, especially when AI agents must adapt to diverse and unseen human behaviors in dynamic scenarios. Existing large language model (LLM) agents often fail to accurately model the complex human mental characteristics such as domain intentions, especially in the absence of direct communication. To address this limitation, we propose a novel dual process multi-scale theory of mind (DPMT) framework, drawing inspiration from cognitive science's dual process theory. Our DPMT framework incorporates a multi-scale theory of mind (ToM) module to facilitate robust human partner modeling through mental characteristic reasoning. Experimental results demonstrate that DPMT significantly enhances human-AI collaboration, and ablation studies further validate the contributions of our multi-scale ToM in the slow system.

DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration

This study investigates the structural alignment of conceptual representations accessed from visual and linguistic modalities in 8-9-year-old children and adults. Using a Spatial Arrangement (SpAM) task, participants organized familiar items from two categories – household items (HHI) and vegetables & fruits (VF) – presented separately as images and written words on a two-dimensional grid. Representational dissimilarity matrices were computed based on item distances within each modality and analyzed for structural alignment across modalities. Results showed significantly lower cross-modal alignment in children compared to adults, suggesting ongoing developmental changes in the structure of conceptual representations. Additionally, cross-modal alignment was higher for VF than for HHI categories in both age groups, indicating category-specific variations in conceptual organization and its refinement. These findings provide insights into the gradual refinement of the structure of the lexical-conceptual system, extending beyond item-level lexical learning.

Structural Alignment Across Visual and Linguistic Modalities:  A Developmental Refinement Perspective

Generative artificial intelligence (GenAI) is rapidly advancing, providing innovative tools and methods for a wide range of applications. Among these, Generative Agents, a key domain in GenAI, are valued for their fine-grained definitions and simulations of human-like behaviors. These agents provide new avenues for studying and modeling various domains, including social interactions, education, and cognitive sciences. However, existing works suffer from cognitive dynamics disconnect and affective absence, which hinder researchers from exploring human-related cognitive processes in depth. To address these limitations, we propose EvoAgents, a novel cognitive-driven framework that pioneers the exploration of dynamic personality evolution in Generative Agents. By defining the emotional content of agents and integrating a cyclical personality evolution cycle, EvoAgents represent a significant step toward creating more adaptive and authentic agent behaviors. Comprehensive simulations and evaluations show that EvoAgents achieve superior performance in key automated metrics compared to prior work, while uniquely enabling reasonable and robust personality evolution processes that align with cognitive and psychological expectations. By constructing a new simulation environment, SmallClassroom, based on the EvoAgents framework, we validate the framework's ability to provide deeper cognitive insights into social dynamics, aligning closely with established psychological theories.

EvoAgents: A Cognitive-Driven Framework for Personality Evolution in Generative Agent Society

Automated vehicle (AV) validation faces the "billions of miles" challenge, requiring high-fidelity simulations to replicate diverse interactive driving behaviors for safety. Traditional methods oversimplify by using uniform behavioral models, ignoring the diversity of human driving styles, which are deeply influenced by individual psychological traits. This research introduces a condensed framework for representing interactive driving styles, by incorporating these psychological dimensions, balancing completeness and complexity. Key features include: i) individual style recognition via attention mechanisms and hierarchical contrastive learning, capturing subtle cognitive-based interaction patterns that reflect underlying differences in driver psychology (e.g., risk tolerance, decision-making heuristics); ii) scenario-independent style compression, filtering external factors to extract intrinsic driver intentions; iii) dimensionality-aware refinement, mapping complex behaviors to low-dimensional psychological axes for efficient computation. Tests on the NGSIM dataset reduced testing complexity by decoupling styles from scenarios. Compared to traditional methods, style distinctiveness improves by 28% (entropy-based), with 85% edge-case behavior coverage. This framework supports scalable AV testing by integrating diverse, psychologically-informed driving styles without combinatorial complexity.

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