United States

In this paper, we argue that the contributions of perceivers to object perception can substantially affect what objects are represented in perceptual experience. To capture the scalar nature of these perceiver-contingent contributions, we discuss two grades of subject-dependency in object perception. The first grade, weak subject-dependency, concerns attentional changes to perceptual content like, for instance, when a perceiver is turning her head, plugging her ears, or her attention is primed for a particular cue.  The second grade, strong subject-dependency, concerns generating perceptual objects whose existence depends upon their perceivers’ sensory contributions. We offer evidence from the future-directed anticipation of perceptual experts and from the feature binding of synesthetes to exemplify this nonstandard, subject-dependent form of object perception. We conclude that strongly subject-dependent perceptual objects are more than mere material objects, but are rather a necessary combination of material objects with the contributions of a perceiving subject.

CogSci 2025

Do perceivers contribute to object perception? 

cognitive neuroscience

sensory processing

psychology

representation

perception

vision

philosophy

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.

Based on Blame Theory and Integrative Moral Judgment and Decision-Making (MJDM), this paper examines the role of responsibility for the situation and control over the consequences of the situation in first-person, realistic and non-sacrificial dilemmas. In a 2x2 within-subjects design, 69 participants had to read and solve four dilemmas. Their choice, affect felt, perceived utility, and moral acceptability values were measured. Analyses showed that increased control led to more pro-social choices, and that responsibility increased pro-sociality only when control was low. Control and responsibility also influenced affect, utility, and moral acceptability judgments, corroborating the integrative MJDM model. These results show that control and responsibility play a key role in MJDM but differ from blame theory, where responsibility is necessary for blame and for control to matter. In contrast, in first-person MJDM, control suffices for pro-social action, and responsibility influences decisions only when control is absent.

Control and Responsibility Role in Moral Judgment and Decision-Making

Humans develop cooperation heuristics in social decision-making, either intuitively or deliberatively. Large language models (LLMs), which exhibit human-like biases across cognitive domains, may acquire prosocial tendencies through instruction tuning, enabling cooperative behavior in strategic reasoning games. However, most studies of this kind either focus on cooperative language generation or explicitly instruct LLMs to cooperate, deviating from the inherent cooperation heuristics of humans. Using negotiation role-play simulations with BATNA (Best Alternative to a Negotiated Agreement), we found that LLMs struggle with cooperation in the absence of explicit instructions, leading to a 50–80% lower success rate than in instructed scenarios and 50–60% lower than human performance reported in past studies. Implicitly inducing cooperation through personality traits had inconsistent effects, with agreeableness showing marginal influence and other traits exhibiting no systematic impact. These findings suggest that personality-based cooperation cues are subtle, and explicit instructions remain essential for multi-agent LLMs to approximate human-like negotiation.

Explicit Cooperation Shapes Human-Like Multi-Agent LLM Negotiation

Past research on eye-movements during reading and
comprehension has primarily focused on alphabetic scripts,
such as the Roman script used to write European languages like
English, Dutch, German, and Spanish, where classical
measures like word length can be easily calculated by counting
characters. However, this approach may not generalize to
alphasyllabic languages like Hindi and other Indian languages
written using the Devanagari script, where many characters
depend on diacritic markers for proper pronunciation. This
poses challenges in studying these languages in eye-tracking
research, discourages eye-tracking studies with these
languages. To address this gap, we asked 61 native Hindi
speakers (L1) read Hindi text, while their eye-movements were
being tracked. Results revealed that a complexity metric for the
script predicts variables such as first fixation duration, gaze
duration, single fixation duration, total reading time, and
number of fixations. These results were also correlated with
variables such as word frequency (van Heuven et al., 2014) for
all eye-tracking measures.

Orthographic Complexity Moderates Eye Movements While Reading in Hindi, Along with Length & Frequency

Evidence is accumulating that patterns of use for singular “they” are changing in English. The pronoun is becoming the preferred generic when the gender of the referent is unknown or backgrounded. This change reflects a shift in patterns of acceptability for uses of singular “they” which is in turn linked to the increased frequency of singular they. We predict that adaptation may be a cognitive mechanism underlying this change, and if so, we may see short-term adaptation within a lab session. In the present study, we use a between-subjects priming paradigm to test whether participants adapt to the frequency with which they encounter singular or plural senses of “they” in the local discourse. We find that selections of singular “they” are significantly more likely after participants have been exposed to unambiguously singular vs. plural uses of “they”. This finding implicates adaptation and suggests that adaptation may link changes in the frequency of linguistic forms to changes in their acceptability.

Singular “they” exposure increases singular interpretations in ambiguous pronouns

Despite extensive research on mentalization, few studies target the representations and the cognitive systems that underlie different mental state attributions. In two eye-tracking experiments with adults (n=32) and 19-month-old infants (n=24), we examined whether factive (knowledge, ignorance) and non-factive (false belief, true belief) mental state attributions belong to seperate representational systems, relying on the assumption that transfer within-system should occur faster than between-systems. Participants watched animated videos of an agent tracking a hidden ball that could hide in two locations, requiring mental state attribution updates from non-factive to either another non-factive or to a factive mental state. Saccadic reaction times (SRTs) to the ball’s reappearance were measured. Results showed that both adults and infants had faster SRTs when updates occurred between two non-factive mental states compared to updates between a non-factive and a factive mental state. This supports the existence of distinct systems for factive and non-factive mental state attribution.

Evidence for Distinct Factive and Non-Factive Mentalization Systems in Adults and Infants

A well-known empirical result in human linguistic processing finds that humans are quicker to correctly recognize a string of letters as word when they are first shown a word that is semantically related to the word they must recognize. This is known as the “semantic priming effect.” Since Collins and Loftus (1975), it has been widely theorized that this effect is due to graphical storage of words in memory and a “spreading activation” model of priming. On this theory, words are related to one another in human semantic memory via a graphical structure encoding semantic relationships between words, with participants more likely to quickly recognize a word when they are primed with one that is graphically nearby; the prime word “activates” the node of a participant’s semantic memory network representing the prime word and this activation “spreads” to words at nearby nodes. Today, large language models increasingly excel at generating structured data representations, like graphs, when prompted to do so (Ghanem & Cruz, 2024; Dagdelen et al., 2024). In the current paper we investigate whether a language model can be prompted to represent a set of words as a semantic graph, and whether human reaction times in a word recognition task are predicted by the minimum path length between words in such an LLM-generated semantic graph. Using two versions of the Gemini language model, we use a prompting strategy to generate semantic graphs relating all words used in a large semantic priming experiment conducted by Hutchinson et al. (2013), under a variety of different temperatures and settings for the number of maximum output tokens. While we find that all LLM-generated semantic graphs produced during our experiments are such that the minimum path length between two words predicts the reaction time in which a person primed by one word recognizes the other, this effect is most pronounced for graphs generated via a smaller version of the model. It is under these conditions, we find, that LLMs produce the dense graphs that are more predictive of human semantic priming effects in lexical decision tasks.

LLM-Generated Semantic Networks Predict Semantic Priming Effects on Human Reaction Times in a Word-Recognition Task

Sense-Making, Cultural Scripts, and the Inferential Basis of Meaningful Experience

Can LLMs model meaning in restorying interventions?

We asked whether the concreteness and specificity of the language used by conversation participants change depending upon the familiarity and the presence/absence of an object discussed. Additionally, we explored whether interlocutors engaged in distinct abstraction processes (analogical comparison; superordinate categorization) and whether they focused more on object’s features or on their own experience.  

We used the ECOLANG corpus (Gu et al., 2025), a semi-naturalistic dataset of interactions in which 31 knowledgeable “speakers” describe novel/known objects to an “addressee” when the object is physically present or absent. We analyzed 22,581 sentences produced by the “speaker” and measured the concreteness and specificity of 1,612 nouns used.   

Results showed that more concrete and specific nouns were used for novel objects suggesting a need for precise information.  Additionally, abstraction processes were more likely when the object was present and novel. Finally, when the object was present and known, interlocutors focused more on personal experience.

Be concrete and specific: how speakers introduce novel topics in naturalistic language

Creativity assessment in science and engineering is increasingly based on both human and AI judgment, but the cognitive processes and biases behind these evaluations remain poorly understood. We conducted two experiments examining how including example solutions with ratings impact creativity evaluation, using a finegrained annotation protocol where raters were tasked with explaining their originality scores and rating for the facets of remoteness (whether the response is ``far'' from everyday ideas), uncommonness (whether the response is rare), and cleverness. In Study 1, we analyzed creativity ratings from 72 experts with formal science or engineering training, comparing those who received example solutions with ratings (example) to those who did not (no example). Computational text analysis revealed that, compared to experts with examples, no-example experts used more comparative language (e.g., ``better/worse'') and emphasized solution uncommonness, suggesting they may have relied more on memory retrieval for comparisons. In Study 2, parallel analyses with state-of-the-art LLMs revealed that models prioritized uncommonness and remoteness of ideas when rating originality, suggesting an evaluative process rooted around the semantic similarity of ideas. In the example condition, while LLM accuracy in predicting the true originality scores improved, the correlations of remoteness, uncommonness, and cleverness with originality also increased substantially --- to upwards of $0.99$ --- suggesting a homogenization in the LLMs evaluation of the individual facets. These findings highlight important implications for how humans and AI reason about creativity and suggest diverging preferences for what different populations prioritize when rating.

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Control and Responsibility Role in Moral Judgment and Decision-Making

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