United States

Recent studies use semantic structural priming to show that various cases of linguistic strengthening happen through a common mechanism: generation of implicatures through alternative-based (scalar) reasoning. In this paper, we used priming to investigate another group of cases, where strengthening is postulated to follow from the tendency to systematically neglect structures that verify a sentence by virtue of an empty configuration (neglect-zero): empty-set quantifiers (&#39;at most/fewer than&#39;) and disjunction under a universal quantifier. We report data indicating semantic priming between these two structures, but not between them and scalar &#39;some&#39;. We propose that 1. there is a common mechanism in use for strengthening constructions postulated to follow from the neglect-zero tendency, and that 2. this mechanism is different from the one involved in alternative-based reasoning.

CogSci 2025

Neglect zero: evidence from priming across constructions

language and thought

quantitative behavior

psychology

linguistics

pragmatics

Recent studies use semantic structural priming to show that various cases of linguistic strengthening happen through a common mechanism: generation of implicatures through alternative-based (scalar) reasoning. In this paper, we used priming to investigate another group of cases, where strengthening is postulated to follow from the tendency to systematically neglect structures that verify a sentence by virtue of an empty configuration (neglect-zero): empty-set quantifiers ('at most/fewer than') and disjunction under a universal quantifier. We report data indicating semantic priming between these two structures, but not between them and scalar 'some'. We propose that 1. there is a common mechanism in use for strengthening constructions postulated to follow from the neglect-zero tendency, and that 2. this mechanism is different from the one involved in alternative-based reasoning.

poster

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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.

Recent advancements in Large Language Models (LLMs) have brought them closer to matching human cognition across a variety of tasks. How well do these models align with human performance in detecting and mapping analogies? Prior research has shown that LLMs can extract similarities from analogy problems but lack robust human-like reasoning. Building on Webb, Holyoak, and Lu (2023), the current study focused on a story-based analogical mapping task and conducted a fine-grained evaluation of LLM reasoning abilities compared to human performance. First, it explored the semantic representation of analogies in LLMs, using sentence embeddings to assess whether they capture the similarity between the source and target texts of an analogy, and the dissimilarity between the source and distractor texts. Second, it investigated the effectiveness of explicitly prompting LLMs to explain analogies. Throughout, we examine whether LLMs exhibit similar performance profiles to those observed in humans by evaluating their reasoning at the level of individual analogies, and not just at the level of overall accuracy (as prior studies have done). Our experiments include evaluating the impact of model size (8B
vs. 70B parameters) and performance variation across state-of-the- art model architectures such as GPT-4 and LLaMA3. This work advances our understanding of the analogical reasoning abilities of LLMs and their potential as models of human reasoning.

Modeling Understanding of Story-Based Analogies Using Large Language Models

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.

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

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.

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Modeling Understanding of Story-Based Analogies Using Large Language Models

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Modeling Understanding of Story-Based Analogies Using Large Language Models

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