United States

We present two studies utilizing tDCS to investigate the impact of anodal stimulation at the Fp3 site on categorization learning indexed by the prototype effect. This phenomenon is characterized by superior categorization for unseen category prototypes compared to both seen and unseen category exemplars. In our double-blind experimental design, participants were randomly assigned to one of two groups: anodal tDCS or sham/control. In Experiment 1a, we observed a pronounced prototype effect in sham/control, demonstrating significantly enhanced categorization performance for unseen category prototypes over &#39;old&#39; (previously seen) exemplars. Critically, the application of anodal tDCS diminished this effect, hindering performance on prototype stimuli. Experiment 1b provided further validation of this finding, indicating that anodal tDCS disrupts the prototype effect concerning old exemplars. Interestingly, this significant reduction in the prototype effect was not replicated with &#39;new&#39; (unseen) category exemplars. We contextualize our results within the framework of the tDCS and perceptual learning literature.

CogSci 2025

Modulating categorization skills: The impact of transcranial Direct Current Stimulation (tDCS) on the Prototype Effect

cognitive neuroscience

learning

pattern recognition

psychology

perception

memory

### 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.

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#### Our History 

* **Society Creation**<br>
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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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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

We present two studies utilizing tDCS to investigate the impact of anodal stimulation at the Fp3 site on categorization learning indexed by the prototype effect. This phenomenon is characterized by superior categorization for unseen category prototypes compared to both seen and unseen category exemplars. In our double-blind experimental design, participants were randomly assigned to one of two groups: anodal tDCS or sham/control. In Experiment 1a, we observed a pronounced prototype effect in sham/control, demonstrating significantly enhanced categorization performance for unseen category prototypes over 'old' (previously seen) exemplars. Critically, the application of anodal tDCS diminished this effect, hindering performance on prototype stimuli. Experiment 1b provided further validation of this finding, indicating that anodal tDCS disrupts the prototype effect concerning old exemplars. Interestingly, this significant reduction in the prototype effect was not replicated with 'new' (unseen) category exemplars. We contextualize our results within the framework of the tDCS and perceptual learning literature.

poster

This study investigated how visual processing of simple and compound words in Thai can be modulated by the experimental tasks. A sentence reading eye-tracking experiment (Experiment 1) and a lexical decision experiment (Experiment 2) were conducted on Thai bisyllabic compounds and simple nouns. Experiment 1 had two interword spacing conditions and examined how adding interword spacing to Thai sentences (normally unspaced in writing) modulated lexical processing. Experiment 1’s results showed that compounds incurred longer fixation durations than simple words, whereas interword spacing facilitated word recognition for both word types. Experiment 2 revealed a different result. Word recognition was faster for compounds than simple words. We assumed the disparity stemmed from different task demands. Sentence reading requires integration of semantics and syntactic processing, and compounds would require additional semantic integration processing. By contrast, lexical decision focuses on isolated word recognition and the word’s syntactic and semantic features are not activated.

Comparing eye movement and lexical decision experiments on Thai compound recognition

This paper presents a framework for understanding metacognition as a distinct domain of skill, drawing on established research in motor and cognitive domains. It proposes that metacognitive expertise shares key characteristics with other skill domains, including goal-directed action, hierarchical organization, declarative and procedural knowledge, and automatization. By integrating theoretical and empirical insights, this paper aims to establish a comprehensive model of metacognitive skill development, with implications for research and practical applications in education, therapy, and beyond.

Metacognition as a domain of skill

Humans are enmeshed in many hierarchical relationships, such as that between a parent and child. Leaders in dominance hierarchies are typically strong and intimidating, while leaders in prestige hierarchies are respected for their expertise. In an ongoing study (N=13), we ask whether preschoolers ages 4–5 have different expectations about how dominant and prestigious leaders divide resources. Children learned about two social groups, the dominant Glerks and prestigious Zonks. Children watched a leader and subordinate from each group pick five apples and divide them into two baskets in a 5-0 or 3-2 split. We then pointed to one basket and asked children whether the leader or subordinate took that share of apples. In preliminary results, we find that children expect the dominant (76.9%) but not prestigious leader (46.2%) to take all of the apples, which suggests that children expect dominant leaders to claim a larger share of resources.

Children’s expectations of dominant and prestigious leaders 

Number words are often used in noun phrases referring to objects (e.g., “two balls”). This usage may suggest to children that numbers quantify same-kind sets. We investigated this hypothesis using a novel task that assessed English-speaking children’s (aged 3-11 years) preference for same-kind sets when asked for two or three objects. Across four experiments (N=167) children’s preference for same-kind sets was evaluated at the basic level (cows, horses, etc.), subordinate level (grey horses, brown horses), and superordinate level (animals, food). Children did not show any bias at the subordinate level, but they tended to produce same-kind sets when presented with items that differed at the basic or superordinate level. At the basic level, counting fluency was associated with children’s production of same-kind sets. Our results suggest that children interpret number words as referring to same-kind sets and this bias is particularly strong in children with limited number knowledge.

Homogeneity Bias in Small Number Word Comprehension

Large-scale annotated data is essential for age prediction in social media, yet obtaining such data is costly. Age is a key psychological and cognitive marker influencing communication and social behavior. Understanding age-related patterns in online interactions can provide insights into cognitive development and identity formation. To address data limitations, we propose a semi-supervised multimodal regression model leveraging Transformer-based variational autoencoders to infer age from textual and social features. This approach aligns with cognitive and social science theories on age-related behavioral patterns. Our framework effectively utilizes unlabeled data, reducing annotation dependency while enhancing predictive accuracy. Empirical results demonstrate superior performance over traditional classification and supervised baselines, advancing interdisciplinary research in age inference and online behavior modeling.

Age Inference on both Textual and Social Perspectives with Semi-supervised Learning

A child invents a game, describes its rules, and in an instant, we can play it, judge progress, and even suggest new variations. What mental representations enable such flexible reasoning? We build on recent work formalizing naturally expressed goals as a type of program, grounding linguistic descriptions into precise scoring systems. To support this notion, we study human-created objectives in a physics game environment. We leverage the formal representations to quantitatively analyze relationships between reward geometry, goal complexity, and perceived difficulty. We then propose a proof-of-concept of a computational goal inference method using these program representations and behavioral demonstrations, offering a concrete proposal of how humans reason about others' goals.

Goal Inference using Reward-Producing Programs in a Novel Physics Environment

Life often presents choices that are not mutually exclusive, yet there has been insufficient research on human learning and directed exploration involved in combinatorial settings. We investigated human behavior in a four-armed combinatorial bandit (CB) task (N=107) where participants combined "nutrients" affecting required nurture time of virtual plants. Participants demonstrated effective learning but converged to suboptimal strategies, preferring combinations of one or two options. To model learning, two computational models were proposed and compared: a naïve extension of upper confidence bound (NaiveUCB), and a linear UCB model (LinUCB), both incorporating heuristic components. The NaiveUCB model with penalty for multiple selections, value decay, stickiness, and recency-based credit assignment best explained behavior, outperforming both LinUCB and simplified variants, suggesting that humans may navigate uncertainty through simple heuristics rather than sophisticated estimation. These findings extend our understanding of exploration and credit assignment in CB, and provide insight into daily decision making.

Modeling human learning and exploration in a temporal combinatorial bandit task

Convolutional neural networks (CNNs) not only recognize objects with high accuracy, but also acquire from images abstract statistical concepts such as numerosity and correlations. However, it remains unclear whether the CNN architectures implement inductive biases that mimic human biases in statistical judgments. In this paper, we examined whether VGG-19 models, a popular CNN architecture, that are trained to make correlation judgments from scatterplots display human-like biases. In comparisons between model predictions and human data, we found that there was a high correspondence between human biases and machine biases in VGG-19 models. Using explainable AI visualization with saliency maps to unpack the regions on which VGG-19 rely to make correlation judgments, we found that the late layers of the model tend to focus on regions similar to human participants’ fixation distributions as captured by eye tracking. We further demonstrate that such models were nearly sufficient to predict human data at an accuracy level rivaling the state-of-the-art model trained on human data in three large-scale correlation discrimination datasets. Our results suggest that VGG-19 models may employ strategies that are similar to those used by human participants for statistical judgments from visual graphs and, therefore, pave the way to address human cognitive biases in visualization-based statistical judgments through the lens of deep neural networks.

VGG-19 Displays Human-like Biases in Statistical Judgment from Visual Graphs 

Human adults think of the natural world as orchestrated by rules, yet many of them are neither equally rigid nor clearly evident. Some are beset by exceptions, and others are not intuitive. The problem of rule learning is especially salient in development, as children are continuously learning how the world works. What cognitive mechanisms underpin this rule learning? 
We propose a computational model for formulating and testing hypotheses in naturalistic contexts, that combines Bayesian inference under uncertainty over self-generated and social evidence with formal rules and optimistic information-seeking heuristics. We validate our model experimentally, showing that it explains 7- to 10-year-olds' behavior in a rule-based, physical task, including the distribution and the types of evidence children sampled. The proposed model outperforms both a purely rule-based Bayesian hypothesis search and a resource-rational random sampling approach. Our results suggest that children implement an internal mechanism for generating and testing a limited number of hypotheses, including formal programmatic rules and heuristics generated from salient problem features to seek more evidence when formal rule generation fails.

Think outside the box: Making up casual hypotheses from unreliable evidence

Human decision-making involves navigating unbounded spaces of possible goals, subgoals, and action sequences. Yet, computational models typically assume pre-defined option sets. This creates a critical gap between the algorithms developed in cognitive science research on decision-making and the open nature of real-world decisions. We propose that option generation in open-ended settings operates as a search through structured decision space. Drawing on foraging theory, we hypothesized that option generation follows Lévy flight distributions, a pattern observed in both spatial foraging and memory retrieval. We found that the inter-generation time between consecutive responses in open-ended option generation problems approximated a Lévy distribution, while semantic distances demonstrated properties of heavy-tailed distributions. These findings reveal connections between action planning, information search, and memory retrieval, suggesting shared computational principles in how humans explore unbounded decision spaces.

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