United States

Recent legal rulings have denied copyright protection to artworks derived from AI-generated sources, because AI is assumed to be incompatible with qualities that define human authorship. We empirically test lay intuitions related to these assumptions in two studies (N = 235, N = 119) by investigating how creator attribution of initial source material (AI- vs. human-generated), effort investment in generating source material, and modification level of a derivative work influence perceptions of transformativeness, essence change, and creativity in derivative artworks. Modification level exerted the strongest influence across all measures, with dramatic modifications rated significantly higher than slight or no modifications. Effort investment in generating source material only influenced creativity ratings, with less effort sometimes perceived as more creative. Most notably, creator attribution for source material had minimal impact. These results challenge current copyright doctrine by demonstrating that lay human observers prioritize degree of transformation over both effort and creator attribution for source material. Our findings suggest that legal frameworks should recognize that  AI assistance in generating artworks does not preclude a genuine human contribution that merits copyright protection.

CogSci 2025

Is AI-assisted Creativity an “Original Sin”?: Lay Judgments of Qualities Justifying Copyright Protection for Artworks Derived from AI- vs. Human-generated Sources

art and cognition

creativity

human-computer interaction

psychology

artificial intelligence

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.
<br><br>

#### 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)**.
<br><br>


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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.

Automatic depression detection is gaining traction at the intersection of psychology and machine learning, but concerns over data privacy and scarcity persist. We propose a pipeline using Large Language Models (LLMs) to generate synthetic data that enhances depression prediction while addressing ethical concerns. Starting from recorded clinical transcripts, our chain-of-thought prompting involves two steps: the generation of a synopsis and sentiment analysis from the original transcript, and the generation of synthetic data based on these summaries and a new depression score. The resulting synthetic data not only performs well in terms of utility and fidelity, but also balances the severity distribution in training datasets, improving prediction of depression intensity. Our method offers a practical solution to augmenting limited, sensitive data while preserving statistical integrity. This framework provides a robust framework for advancing mental health research and applications without compromising patient privacy.

Synthetic Data Generation with Large Language Models for Improved Depression Prediction

Large Language Models like ChatGPT are becoming every-day writing partners in the workplace. This study asked: how does simply knowing an email was “edited by ChatGPT” affect its persuasiveness and the perceived cred-ibility of the sender? We collected data from 308 profes-sionals using experimental vignettes that simulated realis-tic workplace emails. Some emails were described as en-tirely human-written, while others were labeled as AI-edited, with variations in the sender's reliability (who is sending the message) and strength of the argument (how well the content is constructed). A Bayesian Model of Ar-gumentation provided normative predictions for how reli-ability and argument quality should influence persuasion. We found that when an email was labeled as “edited by ChatGPT,” receivers saw it as less persuasive overall. However, AI-mediation did not diminish the relative in-fluence of source reliability and argument quality. In other words, while the AI-edited label reduced overall persua-siveness, it didn’t change how recipients inherently evalu-ated credibility. They still adjusted their beliefs primarily based on who sent the message and how strong the argu-ment was. To our knowledge, this is the first study to ap-ply a Bayesian framework to understanding how people process AI-mediated communication.

“Can you tell I used ChatGPT?” How Perceived AI-Mediation Affects Workplace Email Persuasiveness— A Bayesian Approach

Increased cognitive load is linked to decreased fixation counts and longer dwell times during visual search. However, past work studied eye movements in 2D tasks, whereas in 3D environments the head and body help the eyes gather information. Thus, we examined how cognitive load affects the eyes, head, and body in 3D visual search. Cognitive load may create difficulty planning the eye, head, and body movements needed to search a 3D space. We used an eye-tracker to record gaze and inertial sensors to measure the motion of the head and body during a dual task paradigm: Participants searched a set of 27 images distributed in a 270º space for stimuli of a specified criteria. Cognitive load was manipulated by having participants count backwards by set intervals (1, 3, 5, 7, and no counting). Results showed a decrease in total fixations and an increase in stimulus dwell times when under load.

The Effects of Cognitive Load on Full-Body Gaze Control During 3D Visual Search

We commonly use the language of physics to describe social interactions, even those that do not involve physical contact at all (e.g., pushing, pressuring, blocking). Is there a common conceptual basis for perceiving causal interactions? This study asks whether there is a shared neural code for physical and social interactions. For instance, when a gust of wind knocks a sailboat off its trajectory, is that interaction processed similarly to when an employee is kept from going home by their boss? To investigate domain-invariant representations of causal interactions involving enabling vs. preventing, we presented fMRI participants with vignettes depicting physical interactions between objects or purely social interactions (agents interacting socially but not physically). Multivoxel pattern analyses revealed that brain regions classically associated with physical reasoning and social reasoning contained domain-invariant representations of enabling vs. preventing. These results suggest a shared conceptual structure for making sense of physical and social interactions.

Pushing people: the neural basis of social interaction perception

Short-term phonetic accommodation is a fundamental driver behind accent change, but how does real-time input from another speaker's voice shape the speech planning representations of an interlocutor? We advance a computational model of change in speech planning representations during phonetic accommodation, grounded in dynamic neural field equations for movement planning and memory dynamics. A dual-layer planning/memory field predicts that convergence to a model talker on one trial can trigger divergence on subsequent trials, due to a delayed inhibitory effect in the more slowly evolving memory field. The model's predictions are compared with empirical patterns of accommodation from an experimental pilot study. We show that observed empirical phenomena may correspond to variation in the magnitude of inhibitory memory dynamics, which could reflect resistance to accommodation due to phonological and/or sociolinguistic pressures. We discuss the implications of these results for the relations between short-term phonetic accommodation and sound change.

Phonetic accommodation and inhibition in a dynamic neural field model

Children must learn individual word meanings and the semantic connections among words. Prior work has probed children’s sensitivity to different semantic relations, but little is known about how this knowledge develops. This study examined whether parents and children jointly created semantic regularities in naturalistic everyday interactions. Forty-four parents and their toddlers (M = 20.4 months, range: 13.2-31 months) participated in a 10-minute free play session with 27 toys from three categories (food, animals, vehicles) while wearing head-mounted eye-trackers. Children’s looking from one object to another was categorized as a same category (e.g., dog-cat) or different category (e.g., dog-car) transition, revealing temporal sequences of semantically related toy play: Children were more likely to transition between objects from the same category and heard more unique words from the same category during these toy transitions. Together, these findings demonstrate that parents and children create semantic regularities for learning through shared attention and action.

Parents and Children Create Semantic Regularities During Naturalistic Toy Play

People use their knowledge to evaluate information and make decisions. Yet this knowledge may be faulty, like many lay beliefs on health remedies. This can lead to incorrect decisions and choosing ineffective interventions. While people often overestimate their knowledge, as shown in prior research, less is known about how metacognitive factors such as perceived versus actual knowledge interact with new information we receive during decision-making. Prior work has found that the simplest causal models are most helpful for everyday decisions, but did not examine the role of people’s existing knowledge. To address this gap, we conducted an online experiment to examine how metacognitive abilities influence decision-making with causal diagrams for Type 2 diabetes management. Actual knowledge positively predicted decision-making accuracy, while perceived knowledge had a negative effect, and simpler diagrams led to higher accuracy regardless of prior knowledge. We discuss the implications of our findings for designing decision support interventions.

Understanding the Impact of Metacognitive Ability on Decision-Making with Causal Diagrams

Human planning is incredibly efficient. Even in complex situations with many possible courses of action, people are able to make good decisions. Recent proposals suggest that a primary contributor to this efficiency is the intelligent use of cognitive resources, but how people allocate these resources under time constraints is not fully understood. In this work, we conduct a resource-rational analysis of planning in a large data set of online chess games. We first demonstrate that players spent more time thinking when they had more time to do so, and that this effect was especially prevalent when computation was more valuable. Then, we show that additional time spent planning resulted in better selected moves when one existed, and compare between signals of general and immediate time pressure. Finally, we highlight the role of expertise in this setting. Our results provide evidence that people make resource-rational choices when planning under time pressure.

Exploring resource-rational planning under time pressure in online chess

Egodystonia refers to thoughts and behaviors that conflict with one’s values or beliefs, which is often observed in psychiatric conditions like obsessive-compulsive disorder (OCD). While prior work has demonstrated dissociations between beliefs and actions (Vaghi et al., 2017, 2019), we lack a computational framework to explain the mechanisms underlying this mismatch. In a novel experiment combining behavior and subjective report, we induced egodystonic feelings in a healthy population with a range of obsessive-compulsive traits. Individuals scoring higher on the Obsessive-Compulsive Inventory (OCI-R) reported greater egodystonic experiences. Egodystonicity was not influenced by reward availability or action rate, but was driven by perceived consequences of inaction, as captured by a computational model of the task. This study provides the first experimental evidence of induced egodystonia and offers a foundation for theoretical advances in understanding this phenomenon.

Towards a computational account of egodystonia

Converging evidence suggests that languages are shaped by
a drive for efficient communication. In particular, it has
been shown that languages efficiently compress meanings
into words via the Information Bottleneck (IB) principle in
domains ranging from visual percepts, such as colors and
objects, to non-visual high-level concepts, such as pronouns
and number. These domains, however, capture only static
elements described by adjectives, nouns, function words, or
grammatical markers, leaving open the question of whether the
same theory could also apply to verb meanings, which often
refer to dynamical aspects of the environment. We address
this question by considering locomotion verbs (e.g., walk, run,
and jump) across four languages (English, Dutch, Spanish,
and Japanese). We show that locomotion verb meanings
across languages are shaped by pressure for efficiency, which
resonates with similar findings in other domains and suggests
that the IB principle may apply more broadly across the
lexicon. Our results also open a new avenue for future work
to explore whether semantic categories of actions are rooted
in a strictly perceptual representation, or perhaps in motor and
functional representations as well.

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