United States

When choosing what we find visually attractive, men and women tend to focus on different features, even for simple shapes. This study investigates gender differences in visual feature preferences during the anthropomorphization of graphics in the context of sexual selection. We constructed a feature set consisting of 48 geometric attributes to explore how these elements affect sexual selection preferences across genders. In Study 1, we quantitatively visualized these features using genetic algorithms, GANs, and manual design. Study 2 assessed gender preferences through an online survey of 288 participants, revealing the most significant features and differences in male and female preferences.Finally, in Study 3, we applied these findings to real-world art (Chinese calligraphy) to verify the explanatory power of the features. Our results provide new insights into the role of visual features in sexual selection and have practical applications in art, product design, and user experience optimization.

CogSci 2025

Sexual Selection Preferences in Anthropomorphized Imagery of Interpretative Graphics in Quantitative Visualization

attractiveness

art and cognition

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. 

<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/amvr/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>

#### 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. 
<br><br>
* **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.
<br><br>
* **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>


You need to log in with the email address you registered with. 

Login credentials were sent to you from Underline -  subject line "Welcome to the CogSci 2025 Conference". Please be sure to check your spam/promotional inbox  if you do not see an email confirmation right away.





Please log in to join this event.

To access the site, please register [**here**](https://cognitivesciencesociety.org/registration/).

If you are registered and feel like you are seeing this message by mistake, please make sure you are logged in with the same email that you registered with. 

Please register!

The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Theories of music perception argue over whether observed motor area activation during passive music listening actively contributes to perception or is the product of a distributed representation. There is a growing amount of evidence linking Alpha rhythms in the motor cortex to action inhibition and imagination during passive music listening. In this work, we examine Alpha band power modulation and its association to beat perception using a sensorimotor synchronization task and a natural music listening task with electroencephalography (EEG). We sought to find an association between Alpha band modulation over the primary motor cortex and beat tapping performance. We found that greater Alpha power correlated with worse tapping performance. These results may point to a negative association between motor inhibition and beat perception and a complementary positive association between movement imagination and beat perception and production. We address these findings in terms of the HAPEM theory proposed by Schubotz (2003). This framework suggests that motor activation reflects a predictive representation formed from audio-motor association cortices, lacking proprioceptive information which could be acquired through musical training.

 Alpha band activity over the sensorimotor cortex during passive music listening correlates with beat tapping performance

Human memory exhibits vulnerability in cognitive tasks; comparing visual working memory with new perceptual input can cause unintended distortions. Prior studies report systematic memory distortions post-comparison, but understanding their impact on real-world objects and identifying contributing visual features remains challenging. We propose an AI-driven framework generating naturalistic stimuli based on behavioral object dimensions to elicit similarity-induced memory biases. Using two stimuli types—image wheels (dimension-edited) and dimension wheels (activation-based)—we conducted three visual working memory experiments under conditions: no perceptual comparison, image wheel comparison, and dimension wheel comparison. Results show that both similar images and dimensions induce memory distortions. Visual dimensions (e.g., shape/texture) are more distortion-prone than semantic ones (e.g., category), indicating that naturalistic stimuli’s object dimensions critically influence memory vulnerability.

Dimensions of Vulnerability in Visual Working Memory: An AI-Driven Approach to Perceptual Comparison

This study investigates how corpus-level properties in Korean child- and adult-directed speech shape word segmentation across four algorithms: Transitional Probability, Diphone-Based Segmentation, PUDDLE, and Adaptor Grammar. Utterance length consistently impacts segmentation, with shorter utterances improving performance, particularly for PUDDLE, DiBS, and AG. Word length affects transitional probability algorithms, while hapax legomena introduce challenges for forward TP and AG. Interjections negatively influence AG, but not the others, and larger corpus size benefits PUDDLE. Register effects are limited, with forward TP and PUDDLE performing better on child-directed speech. These patterns highlight algorithm-specific sensitivities, with utterance length emerging as the most consistent factor. Our findings underscore the importance of considering both input properties and algorithm design when studying word segmentation in Korean. Future work should explore cross-linguistic comparisons, larger balanced corpora, and the role of multimodal cues in segmentation.

Dissecting the interplay between corpus properties, algorithm, and word segmentation performance

The Traveling Salesman Problem (TSP) is a classic NP-hard problem, and research on its cognitive decision-making often focuses on internal factors like memory and experience, while neglecting the influence of the problem’s structural characteristics. This study identifies that potential linear features in the TSP point distribution (such as implied paths formed by visual aggregation) may significantly impact human path selection strategies. To test this hypothesis, we propose a method for quantifying the Line Stylization Degree and generate different TSP instances with varying characteristics by introducing disturbances. These are then combined with experimental analysis of participants' decision-making patterns. The results show that participants tend to plan paths along implied lines, and this strategy may reduce cognitive load. The contribution of this paper lies in revealing the shaping role of visual structural features on cognitive decision-making, providing theoretical support for designing human-centered path planning algorithms.

Cognitive Decision-Making in TSP Tasks: The Impact of Line Stylization Features of Point Arrays

Affective Brain-Computer Interfaces has achieved remarkable advancements, enabling researchers to interpret labeled EEG data accurately. However, the annotation of EEG data is time-consuming and requires substantial effort, which limits the application in practical scenarios. In this paper, we propose a self-supervised EEG representation learning framework based on temporal prediction and spatial reconstruction (EEG-TPSR) to learn EEG representations from a large amount of unlabeled data. Our model consists of two stages: 1) In the pre-training stage, we use contrastive temporal prediction and spatial reconstruction as proxy tasks, which utilize the spatio-temporal information to learn the generic representations from EEG data; 2) In the fine-tuning stage, few data is used to calibrate the pre-trained model. We conduct extensive experiments on three emotion EEG datasets. The results demonstrate that our proposed model achieves excellent performance, with over 20% relative accuracy improvement and more than 15% improvement using only 1% labeled data.

Self-supervised EEG Representation Learning based on Temporal Prediction and Spatial Reconstruction for Emotion Recognition

Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analysis via Semiotic Square), a structured analytical framework based on Greimas Semiotic Square (GSS), to enhance LLMs' ability to conduct in-depth literary analysis. GLASS facilitates the rapid dissection of narrative structures and deep meanings in narrative works. We propose the first dataset for GSS-based literary criticism, featuring detailed analyses of 48 works. Then we propose quantitative metrics for GSS-based literary criticism using the LLM-as-a-judge paradigm. Our framework's results, compared with expert criticism across multiple works and LLMs, show high performance. Finally, we applied GLASS to 39 classic works, producing original and high-quality analyses that address existing research gaps. This research provides an AI-based tool for literary research and education, offering insights into the cognitive mechanisms underlying literary engagement.

Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models

To tackle the issue of hallucination in generative question answering (GQA)—where the generated answer is nonsensical or unfaithful to the provided document—we introduce a novel framework called evidence-enhanced triplet generation (EATQA). This framework incentivizes the model to generate all possible combinations of ⟨Question, Evidence, Answer⟩ triplets by reversing the source pair and target label to grasp their logical interrelationships. Specifically, the model predicts the Answer (A), Question (Q), and Evidence (E) given the QE, EA, and QA pairs, respectively. Furthermore, we address the distribution gap during the inference stage to extract knowledge from the evidence more effectively. Our framework ensures that the model comprehends the logical connections between queries, evidence, and answers, thereby simultaneously enhancing evidence generation and question answering capabilities. In this study, we apply the EATQA framework to the LLama model, demonstrating superior performance compared to other large language model (LLM)-based methods and hallucination mitigation techniques on two challenging GQA benchmarks. Further analysis reveals that our method not only preserves the pre-existing knowledge within the LLM but also reduces hallucination and produces more accurate answers.

Evidence-Enhanced Triplet Generation Framework for Hallucination Alleviation in Generative Question Answering

In the era of large-scale artificial intelligence, Large Language Models (LLMs) have made significant strides in natural language processing. However, they often lack transparency and generate unreliable outputs, raising concerns about their interpretability. To address this, the Chain of Thought (CoT) prompting method structures reasoning into step-by-step deductions. Yet, not all reasoning chains are valid, and errors can lead to unreliable conclusions. We propose ECCoT, an End-to-End Cognitive Chain of Thought Validation Framework, to evaluate and refine reasoning chains in LLMs. ECCoT integrates the Markov Random Field-Embedded Topic Model (MRF-ETM) for topic-aware CoT generation and Causal Sentence-BERT (CSBert) for causal reasoning alignment. By filtering ineffective chains using structured ordering statistics, ECCoT improves interpretability, reduces biases, and enhances the trustworthiness of LLM-based decision-making. Key contributions include the introduction of ECCoT, MRF-ETM for topic-driven CoT generation, and CSBert for causal reasoning enhancement.

ECCoT: A Framework for Enhancing Effective Cognition via Chain of Thought in Large Language Model

Medical relation extraction (MRE) is commonly known to extract entities and their relations jointly from a medical text, which has attracted much attention in recent years. Previous studies treat MRE as a sequence tagging task, which results in either a challenging design of the tagging schema or a failed extraction of multiple relations, due to intricate relationships among medical entities. In this work, we review the task from a linguistic perspective and propose a novel pipeline framework, PiPMRE, developed on language models to enhance MRE performance. Specifically, PiPMRE consists of a relation generator and a relation filter. Given a text, the generator first yields multiple relational triplets, and then the filter scores each triplet and retains only those that pass the borderline as the final results. Implementing PiPMRE requires no tagging schema, instead, we use a simple template to reformulate the input text while ensuring entities and relations are generated in contextual order. Extensive experimental results on two public datasets demonstrate the advancement of PiPMRE. It surpasses the previous state-of-the-art by an average of 5.6 recall points and 4.4 accuracy points. PiPMRE's superiorities are also demonstrated in few-shot settings.

PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction

Recent research in Natural Language Processing (NLP) has focused on estimating the difficulty of text content, culminating in a shared task conducted in 2025. However, since many researchers in NLP are not experts in educational psychology, the item difficulty in these shared task datasets is commonly defined by the proportion of examinees who answer an item correctly, and language model performance is evaluated accordingly. This definition is inherently sensitive to changes in the set of examinees who answer correctly, thereby altering item difficulty. To overcome this issue, educational psychology employs item response theory (IRT) to separate item difficulty from the examinee population. In this study, we investigate the extent to which language model performance evaluations differ when using IRT compared to the traditional method, based on the proportion of examinees who answered items correctly.

Downloads

Next from CogSci 2025

Alpha band activity over the sensorimotor cortex during passive music listening correlates with beat tapping performance

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES