United States

The present study examined how participants (N = 298) assessed causality, blameworthiness, foreseeability, and counterfactuality of an AI or human therapist, across three levels of empathy, in comparison to their supervisor and a recommending clinician. We found that participants judged the human therapist as more causal and blameworthy than their supervisor when medium or low empathy levels were displayed, whereas no difference emerged between the judgments of the AI therapist and its supervisor across all of the empathy levels. Additionally, participants did not differentiate causality and blameworthiness between the AI and human therapists, regardless of the empathy level. However, they did perceive the human therapist as foreseeing the outcome more than the AI therapist in the medium and low empathy levels. Qualitative analysis revealed that participants considered the directness of the causes to the outcome, counterfactual reasoning, and inherent limitations of AI when making judgments.

CogSci 2025

Cause and Blame Attribution to AI and Human Agents in Mental Health Context

qualitative analysis

human-computer interaction

psychology

causal reasoning

survey

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.

Food choices, as a type of value-based decision, are affected by environmental cues. We conducted three studies to investigate the computational mechanisms through which background music influences the food choice-making process. Hierarchical drift-diffusion modeling revealed that nature-related music led to higher drift rates than urban-related music, indicating faster evidence accumulation toward certain choices. Specifically, participants processed the value of vegetable-forward meals more efficiently when exposed to nature-related music compared to urban-related music. Moreover, the effect of music on the drift rate varied with the vividness of music-induced mental imagery and the perceived identity of performers (human or robot). Collectively, these findings reveal the computational mechanisms underlying the influence of environmental cues on value-based decision-making.

The Computational Mechanism of How Music Influences Food Choices:  A Drift-Diffusion Model Analysis

Whether language is essential, sufficient, or a tool for numerical cognition has been hotly debated. Here, we investigate the influence of language on quantity representations by comparing embeddings from vision-only Transformer models (ViTs) and vision-language models (VLMs) exposed to image pairs depicting either the same or different stimulus quantities. If linguistic exposure stabilises quantity representations, VLMs should produce more distinct representations for image pairs with differing numerosity and more similar representations for those with identical numerosity than ViTs. We operationalized this as the variance in Cosine Similarity in response to either categorical (same/different) or continuous differences in stimulus numerosity. We find that VLMs and ViTs are sensitive to the numerosity of visual stimuli, that this sensitivity increases with layer depth, and that VLMs exhibit slightly more sensitivity to image numerosity than ViTs. This work provides initial support for the claim that linguistic exposure can, in principle, stabilise quantity representations.

Does Language Stabilize Quantity Representations in Vision Transformers?

Although rules are codified in explicit language, the goals and normative force underlying them remain ambiguous. We argue that people interpret rules in the broader social context where the rule is set. We hypothesize that an authority’s perceived legitimacy—impartiality, competence, and benevolence—affects rule endorsement and compliance through influencing interpretation of the rule’s intent. In an online vignette study with 50 realistic rules that range from very positive to very negative, we found that participants were more likely to endorse and comply with the same rule if it is set by a legitimate compared to an illegitimate authority, independent of the a priori rule’s valence (obtained from independent participants in a norming study). In ongoing work, we are testing the robustness of this effect, probing the potentially distinctive representation of legitimacy from other positive leadership dimensions, and investigating the cognitive mechanisms of how legitimacy shapes norm internalization and voluntary compliance.

Perceived legitimacy of authority influences rule endorsement and intent to comply 

Large Language Models (LLMs) display an impressive set of capabilities in linguistic understanding. While advanced models outperform humans on certain tasks, LLM reasoning and linguistic competency differs from that of humans (Felin & Holweg, 2024; Mahowald et al., 2024; Niu et al., 2024). In this study, we evaluate humans and GPT-4o on the Winograd Schema Challenge, a pronoun resolution task. We focus on Japanese, a relatively understudied language in the emergent field of human-LLM evaluation. To assess human vs. LLM performance, we manipulate task demands and content. We report three findings: (i) Humans outperform LLMs in the baseline condition, i.e. the standard pronoun resolution task. (ii) As task demands increase, both human and LLM performance on the task declines (cf. Hu & Frank, 2024). (iii) We find evidence for content effects (cf. Lampinen et al., 2024): LLMs surpass humans as the content of the task is manipulated to favor LLMs.

Human and LLM performance on linguistic test: Content effect and task demands

Across two experiments (N = 256), we test children’s ability to recognize similar causal structures among mechanical systems. In Experiment 1, 4- to 7-year-olds were shown unique sets of three machine types (a causal chain, a common effect, and a common cause) and asked to judge which machines were most similar. We find that 6- to 7-year-olds, but not 4 to 5-year-olds, spontaneously match machines that share the same causal structure. However, all children relied primarily on timing cues when making similarity judgments. In Experiment 2, we control for timing cues, instead asking children to discriminate causal structure by observing an intervention on each machine. We find that, in the absence of perceptual cues, only 8- and 9-year-olds successfully matched machines based on structural similarity. We discuss potential explanations for these findings and consider ways to support recognition of common causal structure in the learning environment.

Reasoning about similar causal structures among mechanical systems

Animacy is a fundamental yet difficult to define notion in cognitive science. For example, people judging whether words refer to entities that are alive are faster and more accurate for animals (e.g., tiger) than plants (e.g., petunia) and slower and less accurate for natural abiotic entities than artifacts (e.g., cave and ocean vs. slipper and bicycle). The current study demonstrates the reliability and validity of individuals’ aliveness judgments. 169 English-speaking Americans completed the aliveness study twice.  Individual d-scores representing the difference in aliveness judgments between animals and plants at Session 1 predicted d-scores at Session 2 (r = .87, p < .001), as did d-scores representing the difference between natural abiotic entities and artifacts (r = .84, p < .001). These measures also predicted attitudes such as humans having the right to extract natural resources. Future research must address how differences in environment/culture contribute to differences in animacy cognition.

Individual differences in animacy cognition are reliable and externally valid

For recent contractualist accounts of moral cognition, moral judgments should coincide with what rational agents would agree to in a negotiation, accounting for their relative bargaining positions. But past research documents widespread egalitarian moral intuitions; impartiality may also require abstracting away from power asymmetries. How can these perspectives be reconciled? We suggest a key difference lies in whether the logic of bargaining drives the interaction, turning existing asymmetries into bargaining power differences. In Study 1, two parties engage in a take-it-or-leave-it negotiation. In Study 2, they can trade with a third party. In both cases, third-party moral judgments about the morally best split of a fixed amount overwhelmingly favor the advantaged party. They can be precisely predicted using classic models from bargaining theory. By contrast, moral intuitions are completely reversed—reflecting redistributive or egalitarian concerns—in a donation setting where the logic of bargaining does not apply.

How the logic of bargaining shapes moral judgments about resource divisions

Both humans and large language models (LLMs) perform better on some reasoning tasks when they are encouraged to think step by step. However, it is unclear whether these performance gains are based on similar principles. In this work, we investigate two hypotheses: (1) that these benefits arise due to the presence of local statistical structure in the training data, where intermediate steps of reasoning may be common but any specific reasoning trajectory is rare, and (2) that sequential processing improves reasoning by mitigating interference. Using LLMs and transformers trained on a synthetic dataset, we show how analogical distance effects previously observed in humans and LLMs may be explained by the presence of local statistical structure. Testing both humans and LLMs on a novel word analogy task, we find that interference caused by semantic similarity can hurt performance and drives humans to engage in a sequential reasoning process. Our findings show that both locality structure and interference may be key principles underlying the benefits of step-by-step thinking.

Step-by-step analogical reasoning in humans and neural networks

When conducting experimental research, the research questions are often inherently linked (and limited) to the paradigm that is used. In this paper, we present a new experimental tool -- GRIS (Generating Representations in Space) -- that builds experiments where participants can manipulate objects on a screen. Through a series of three experiments on sentence acceptability, category typicality, and multi-dimensional similarity, we demonstrate how GRIS-based experiments allow cognitive scientists to approximate representational spaces for a variety of cognitive phenomena, expanding the set of possible research questions that cognitive scientists may ask.

Generating Representations In Space with GRIS

Recent advances in generative language models, such as ChatGPT have demonstrated an uncanny ability to produce texts that appear to be comparable to those produced by humans. Nevertheless, machine generated texts differ from those produced by humans in important aspects, such as routinely including references to nonexistent sources. In this paper, we use both psycholinguistic measurements and participant responses to compare texts generated by machine with equivalent texts generated by humans. Our analysis demonstrates some of the ways in which machine-generated texts differ from human-generated ones in both style (e.g., increased use of positive connectives) and content (e.g., increased confidence). We also note multiple ways in which texts generated by these models are similar to those generated by humans (e.g., their use of emotion words). We believe this research provides insights that can be useful to understanding how language is generated by both humans and machines.

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The Computational Mechanism of How Music Influences Food Choices: A Drift-Diffusion Model Analysis

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