United States

This work explores the social dynamics triggered by abstract and concrete concepts (e.g., fantasy vs. bottle) integrating traditional research methods with an interactive paradigm. It introduces Vagueness, a semantic dimension that distinguishes between determinate and vague concepts (e.g., subtraction vs. freedom). A preliminary norming study reveals that while Vagueness is partially predicted by Abstractness, it represents a distinct and meaningful semantic component. The main study further demonstrates that Abstractness and Vagueness uniquely shape how dyads evaluate the quality of their collaborative interactions.

CogSci 2025

Interactive Dynamics with Concepts Varying in Abstractness and Vagueness

quantitative behavior

concepts and categories

semantic memory

bayesian modeling

psychology

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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.

Depictions are spatio-temporal arrangements of symbols carrying information about the entities the symbols stand for. We hypothesized that symbolic displays should specifically facilitate the encoding of spatial relations. We tested this hypothesis in 10-month-old infants by exposing them to configurations of unfamiliar objects in a communicative context (which should promote the interpretation of the objects as potential symbols) and in a non-communicative context. We assessed infants' memory for the object arrays by measuring looking times to arrays that matched the original display or altered the identity or the configuration of the objects. A Bayesian hierarchical model on log-transformed looking times revealed a main effect of identity change but no significant interaction between configuration change and communicative context. However, within-condition comparisons showed significantly longer looking to identity changes in both contexts, and to configuration changes only in the communicative condition, suggesting that symbolic displays specifically enhanced the encoding of spatial relations.

The detection of configuration and identity changes of object arrays in infancy

Cotard's delusion, featuring delusions of nonexistence, is rare and not well understood. We used lesion network mapping (LNM) to investigate how focal lesions disrupt large-scale circuits in CD and compared them with other neuropsychiatric conditions. Nineteen lesion-induced CD cases were identified from a systematic review; each lesion was mapped to a standard template, and resting-state fMRI from 1,000 healthy subjects provided functional connectivity. A group-level CD map was defined by stringent sensitivity and specificity thresholds, which then served as a seed for connectivity analysis. We assessed spatial correlations with 31 published lesion-network datasets. A CD-specific network emerged in the right inferior frontal cortex, anterior insula, anterior temporal pole, and temporoparietal junction. Strongest overlaps were observed with Capgras delusion, akinetic mutism, mania, and loss of consciousness, reflecting shared disruptions in self-awareness and salience processing. Unique peaks in the temporoparietal junction and frontal operculum highlight CD’s distinct nihilistic features.

Lesion Network Mapping of Cotard's Delusion:  Unique and Shared Neural Circuits in Nihilistic Delusions, Misidentification Delusions, and Altered Consciousness

This study explores inter-subject neural synchronization, measured via inter-subject correlation (ISC), using EEG during fractal animation observation. Fractal animations, characterized by iterative self-similarity and visual complexity, provide a controlled stimulus devoid of semantic or emotional content, facilitating analysis of core sensory and predictive mechanisms. Fifteen participants watched fractal animations while their EEG was recorded. Following preprocessing, including artifact removal and spatial filtering, ISC was calculated using correlated component analysis. Results showed robust synchronization in occipital regions, linked to early visual processing, and frontal areas, associated with attentional control. Two control manipulations—phase randomization and temporal shuffling—reduced ISC by 65% and 70% (p < 0.001), confirming coherence. Peak synchronization aligned with heightened visual complexity and abrupt transitions. A correlation between self-reported focus and ISC highlighted top-down modulation. These findings endorse fractal animations as a powerful paradigm for studying neural responses, yielding insights into multisensory integration and cognitive processing.

Exploring Neural Synchronization with EEG Using Fractal Animations

Learning motivation is a key variable in learning. Therefore, its assessment has consistently been a popular research topic. While traditional methods like self-report still dominate, methods integrating passive mobile sensing have emerged, using smartphones to collect behavioral data and assess learning motivation via statistical and machine learning techniques. Recent advances in large language models (LLMs) offer new perspectives for psychological measurements, yet their application in learning motivation assessment remains underexplored. To bridge this gap, we propose a novel approach that integrates LLMs with passive mobile sensing to assess and predict students’ learning motivation. We constructed our dataset using mobile sensing data and self-report measures, then designed zero-shot and few-shot tasks embedded in LLMs to evaluate the performance. Ultimately, our findings indicate the feasibility and highlight the potential of leveraging LLMs to predict learning motivation levels based on mobile sensing data.

An Explorative Investigation into Leveraging LLMs to Predict University Students' Learning Motivation

Normative concerns for ownership fundamentally regulate social interaction regarding resources in humans, but little is known about origins of ownership inferences and evaluations tin earliest human development. Here, we ask if normative evaluations of ownership respect are rooted in an early-developing protomoral dispreference for those who violate ownership. Across two studies (N = 66) infants saw puppet shows where an owner was approached by a non-owner who attempted to take the owner’s resource. Infants were subsequently allowed to choose between the two puppets. We controlled for the dominance implications of the outcomes of resource competition by systematically varying whether the non-owner or owner prevailed in attaining the resource. Both studies yielded clear Bayesian evidence that infants did not preferentially reach towards either the owner or ownership violator. These results suggest that infants do not hold strong negative evaluations of those who violate ownership.

Do infants prefer owners over thieves?

Accumulating knowledge and experience across the lifespan are bound to have an impact on the meaning of words. Here, we investigated this idea using primarily emotional valence of words as a test-case. We used French databases that gather psycholinguistic variables including emotional valence of words, in four groups of individuals including young (18-25; 26-39 years), middle-aged (40-59 years) and older (>60 years) adults. Following the hypothesis that words may display age-related differences in their psycholinguistic properties, we computed linear regressions over all individual words as a function of age-groups. Results revealed notably that between 5 and 10% of words show significant linear changes of emotional valence as a function of age. This pattern highlights the situated and flexible nature of word meanings and suggests that self-relevance of experience affects semantic memory.

From positive to negative "craziness": Changes in emotional valence of words across adulthood

Reinforcement learning (RL)—using past choices and outcomes to learn best policies—can model the effects of anxiety in human learning. RL mechanisms are commonly studied using game-like computer tasks; but real-life learning often happens in naturalistic settings, with artificial stimuli less involved than day-to-day situations and objects. We used an online “choose your own adventure” text game to test how modality of interaction—narrative versus gamified—impacts the effect of anxiety on RL. 211 participants completed five “chapters” (blocks) of seven choices, followed by informative feedback about underlying story structure. Higher anxiety scores linked to lower model-free accuracy and higher reaction times. An RL model with memory-decay and attention components showed comparable learning rates, but more decision noise and lower attention in anxious participants. This data shows that anxiety impacts RL in naturalistic processing contexts and holds potential insights into the use of narrative, RL-based gamification in educational settings.

Anxiety Impacts Reinforcement Learning Subprocesses In a Choose-Your-Own-Adventure Text-Based Game

Online public discourse faces many threats such as human and bot networks spreading disinformation or harassment campaigns aimed at excluding certain voices. One such threat is the strategy of 'flooding the zone': intentionally pumping into the discourse information that is irrelevant to, or distracting from, an important issue. This technique is employed by both individual and state actors with seeming success. How and why that technique is successful, by contrast, is less well understood. In this paper we use agent-based modelling to help elucidate the disruptive impact of flooding the zone on communication itself. Specifically, we probe the ways in which flooding hampers the spread of relevant information and show consequences of this even for idealized, rational, actors.

Flooding the Zone: An Agent-based Exploration

This study investigates real-time processing and interpretation of quantified sentences in English and Chinese, focusing on cases where an existential quantifier precedes a universal quantifier. Using a self-paced reading (SPR) task and comprehension questions, we found that surface scope was processed faster than inverse scope, with differences emerging in later processing regions, aligning with the Processing Scope Economy principle. Cross-linguistic differences revealed that inverse scope was less accessible in Chinese than in English, confirming scope rigidity in Chinese. Working memory influenced offline interpretation but not online processing. Additionally, large language models only partially resembled human performance, with BERT-based models aligning with human data in English but not in Chinese, likely due to training biases. These findings contribute to understanding scope processing mechanisms, cross-linguistic variation, and cognitive constraints, while also informing the limitations of LLMs in modeling human sentence comprehension.

Scope Interpretation: Evidence from Human and Large Language Models

Facial expression recognition lies at the intersection of computer science and cognitive psychology, yet the cognitive structure underlying facial action unit (AU) and emotion processing remains unclear. Are AUs and emotions processed in parallel or sequentially? Does gender influence this process? We constructed a 3D face dataset annotated with AU amplitudes and emotion labels. To model cognitive processing hypotheses, we implemented parallel and sequential architectures via multi-task learning and pipelined CNNs. Gender-specific models were compared using representational similarity analysis (RSA) with theoretical emotion spaces. The parallel model (F1 = 42.9%) outperformed the sequential one (F1 = 17.1%), supporting the parallel processing hypothesis. RSA revealed that females’ emotion recognition aligned with social distance between emotions, while males’ performance was selectively influenced by anger representations. These findings suggest sex-specific representational structures in emotion processing and support parallelism as a plausible cognitive mechanism in facial expression recognition.

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The detection of configuration and identity changes of object arrays in infancy

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