United States

&quot;FutureMind&quot; is the composite human mental ability to imagine distant, large, complicated futures, often wildly unlike any human memory. It is an astonishing, ubiquitous, powerful, distributed ability, mostly absent from other species, yet mostly unstudied in Cognitive Science. It is indispensable for human activities from political systems to personal ambition. Our understanding of human history consists largely of notions of how previous generations used their own FutureMind. FutureMind relies on both deeply entrenched and highly creative mental operations. In this presentation, we outline a research program for a systematic science of FutureMind, with an initial theoretical framework.We present the cognitive mechanisms underlying FutureMind, which include framing, blending, compression, analogy, selective projection, viewpoint blending, and the construction of networks of mental spaces.

CogSci 2025

FutureMind: How Human Cognition Shapes the Way We Think About Our Futures

cognition of time

language and thought

creativity

"FutureMind" is the composite human mental ability to imagine distant, large, complicated futures, often wildly unlike any human memory. It is an astonishing, ubiquitous, powerful, distributed ability, mostly absent from other species, yet mostly unstudied in Cognitive Science. It is indispensable for human activities from political systems to personal ambition. Our understanding of human history consists largely of notions of how previous generations used their own FutureMind. FutureMind relies on both deeply entrenched and highly creative mental operations. In this presentation, we outline a research program for a systematic science of FutureMind, with an initial theoretical framework.We present the cognitive mechanisms underlying FutureMind, which include framing, blending, compression, analogy, selective projection, viewpoint blending, and the construction of networks of mental spaces.

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.

Context-dependent reinforcement learning (RL) challenges the assumption that decision makers encode the absolute values of choice outcomes. This study investigates whether the associated choice biases arise from a relative encoding of outcomes or an alternative mechanism involving cumulative reward learning and selective attention to outcomes. Using eye tracking, participants completed a RL task where choice options were initially learned in fixed contexts before being tested in novel pairings. Results revealed an overall preference for options that were contextually favored in the learning phase, even when these preferences violated expected value maximization. Computational model comparisons demonstrated that hybrid encoding models, incorporating absolute and relative values, provided the best overall account of individual behavior. While eye fixations on choice outcomes decreased over trials, fixation-dependent RL models did not fit the data well, suggesting that overt visual attention patterns do not fully explain context-dependent choice biases.

Can Visual Fixations Explain Context-Dependent Reinforcement Learning?

One proposed explanation for humans’ unique cognitive capacity is our ability to extract hierarchical, recursive structures from ambiguous input. However, little work has successfully tested when humans represent hierarchical structures, and whether nonhuman animals do so. Using a serial reaction time task, we test if human adults, children, and rhesus macaques predict upcoming items in a Lindenmayer grammar containing self-similar recursive constituents. Recursively merging constituents makes the sequence more predictable. Constituents of different levels vary in predictive power, allowing measurement of depth of embedding in these representations. We test the human and nonhuman capacity to represent recursive structures, measured by reaction times, and its developmental and evolutionary origins. Preliminary results indicate human subjects recursively merge chunks to build multiple levels of embedded structures spontaneously. With similar training, macaques use simpler, linear strategies to predict items. Follow-up experiments will test whether macaques can learn to extract hierarchical structures for better prediction.

Human and Nonhuman Learning of Hierarchical Structures in a Lindenmayer Grammar

Humans naturally interpret numbers non-literally, effortlessly combining context, world knowledge, and speaker intent. We investigate whether large language models (LLMs)  interpret numbers similarly, focusing on hyperbole and pragmatic halo effects. Through systematic comparison with human data and computational models of pragmatic reasoning, we find that LLMs diverge from human interpretation in striking ways.
By decomposing pragmatic reasoning into testable components, grounded in the Rational Speech Act framework, we pinpoint where LLM processing diverges from human cognition --- not in prior knowledge, but in reasoning with it. This insight leads us to develop a targeted solution --- chain-of-thought prompting inspired by an RSA model makes LLMs' interpretations more human-like. Our work demonstrates how computational cognitive models can both diagnose AI-human differences and guide development of more human-like language understanding capabilities.

Non-literal Understanding of Number Words by Language Models

The onset of spontaneous thoughts are reflective of dynamic interactions between cognition, emotion, and attention. Typically, these experiences are studied through subjective appraisals that focus on their triggers, phenomenology, and emotional salience. In this work, we use linguistic signatures to investigate Déjà Vu, Involuntary Autobiographical Memories, and Unexpected Thoughts. Specifically, we analyze the inherent characteristics of the linguistic patterns in participant generated descriptions of these thought types. We show how, by positioning language as a window into spontaneous cognition, existing theories on these attentional states can be updated and reaffirmed. Our findings align with prior research, reinforcing that Déjà Vu is a metacognitive experience characterized by abstract and spatial language, Involuntary Autobiographical Memories are rich in personal and emotionally significant detail, and Unexpected Thoughts are marked by unpredictability and cognitive disruption. This work is demonstrative of languages' potential to reveal deeper insights into how internal spontaneous cognitive states manifest through expression.

A Linguistic Analysis of Spontaneous Thoughts: Investigating Experiences of Deja Vu, Unexpected Thoughts, and Involuntary Autobiographical Memories

Surprisal theory holds that the processing difficulty of a word is determined by its predictability in context (Hale, 2001; Levy, 2008). However, memory limitations hinder the integration of the full context, as evidenced by dependency locality effects (Gibson 1998). We propose that the local context for predictive processing may be established by cortical tracking of perceptual chunks, which are periodically occurring linguistic units (Giraud & Poeppel, 2012). We identified perceptual chunks in 97 extracts of natural speech using behavioural data. For each word, we derived surprisal values from GPT-2 based on three contexts: the full extract, the current perceptual chunk, and the previous three words. Surprisal conditioned on the chunk context was higher than on the full extract but lower than on the previous three words. This suggests that perceptual chunking may offer an optimal window for predictive processing within working memory capacity.

Does perceptual chunking facilitate predictive processing in spontaneous speech?

Imagine pouring a box of granola into a bowl. Are you considering hundreds of individual chunks or the motion of the group as a whole? Human perceptual limits suggest we cannot be representing the individuals, implying we simulate ensembles of objects. If true, we would need to represent group physical properties beyond individual aggregates, similar to perceiving ensemble properties like color, size, or facial expression. Here we investigate whether people do hold ensemble representations of mass, using tasks in which participants watch a video of a single marble or set of marbles falling onto an elastic cloth and judge the individual or average mass. We find first that people better judge average masses than individual masses, then find evidence that the better ensemble judgments are not just due to aggregating information from individual marbles. Together, this supports the concept of ensemble perception in intuitive physics, extending our understanding of how people represent and simulate sets of objects.

Ensemble Physics: Perceiving the Mass of Groups of Objects is More Than the Sum of Its Parts

Late talkers are children with fewer than 50 words and no two-word combinations by age 2. While some late talkers catch up with typically developing peers, others remain susceptible to developmental language disorders and language-related academic challenges throughout school years. Although maternal input plays a crucial role in language development, its impact on late talkers remains underexplored. This study examines how maternal input quality affects late talkers' lexical diversity and productive syntax, utilizing conversational data from Ellis Weismer's (2007) corpus in CHILDES (MacWhinney, 2000). We analyzed 76 mother-child samples from 38 late talkers (ages 2;6–3;6) in CLAN, assessing lexical diversity with lexical D, productive syntax with IPSyn, and maternal input quality with MLU, lexical D, and IPSyn. Linear regression models indicate that maternal input quality contributes to late talkers’ syntactic development but negatively affects their lexical diversity. These findings underscore the complex nature of maternal input in late talkers’ language development.

Maternal Input Quality and Its Impact on Late Talkers’ Syntax and Lexical Development

In daily life, humans selectively search for information about options to make decisions. The metalevel MDP framework, proposed to understand this information search process, has so far been evaluated only for its predictive performance regarding group differences in summary measures under unrealistic scenarios. This study examined whether metalevel MDP can explain information search processes in more realistic decision-making situations. We proposed a chatbot-based experiment that enables diverse information search actions, as well as a novel analysis method that extends metalevel MDP as a probabilistic model, allowing for the estimation of latent variables and more rigorous evaluation of statistical model fit. The experimental results showed that the model partially explains the information search process, and we discussed potential directions for model improvement.

Stochastic Metalevel Markov Decision Processes: Proposal and Validation through Ecologically Valid Experiments

Humans can quickly infer social relationships from minimal cues, such as where people choose to sit in a meeting room. We investigated how people make graded, context-sensitive judgments about social attitudes beyond simple proximity-based heuristics. Using controlled seating scenarios, we compared participants' judgments to the predictions of Bayesian models: the interaction-probability model, which captures how one person's seat choice affects the probability that another person will initiate the conversation, and the interaction-cost model, which accounts for the effort required based on how far apart they sit from each other. Results showed that participants' inferences aligned best with the interaction-cost model, indicating sensitivity to effort and moving trajectory, rather than relying solely on proximity. Our findings suggest that higher-order cognition refines perceptual cues, enabling nuanced, graded social reasoning essential for complex social interactions.

When Seating Matters: Modeling Graded Social Attitudes as Bayesian Inference

Computational modeling has been a crucial tool in cognitive science to understand human cognitive functions and impairments in neurocognitive disorders. Convolutional Neural Networks (CNNs) exhibit striking similarities to human visual processing systems for object recognition, making them a powerful tool for studying visual processes. In this study, we examined the neurobiological theories, namely, the Excitation/Inhibition (E/I) Imbalance and Internal Noise (IN) in explaining face recognition challenges in autism spectrum disorder (ASD) using CNNs, and revealed that over-excitation and increased noises in the CNNs led to compromised performance on face recognition and atypical patterns of internal representations of face stimuli. This approach enables systematic comparisons between typical and atypical cognition, offering a theory-driven perspective to investigate cognitive challenges and their neurocognitive mechanisms with a computational approach.

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