United States

Recently, AI-generated content (AIGC) technologies have made remarkable advancements, even achieving superhuman performance across various domains. However, few previous studies have investigated its impact on emotion-focused therapy with artistic content, e.g., music. In this paper, we conducted an EEG experiment to explore the effects of generative music on emotion-focused music therapy based on the ISO principle. This experiment compared AI-generated and human-created music regarding the changes in participants&#39; valence and arousal following negative emotion induction with the ISO principle adherence and non-adherence. The results show that generative music, with its harmonic consistency and simple rhythm, is more effective in supporting positive emotions and improving temporal lobe activity. Besides, the therapeutic effectiveness of generative music adhering to the ISO principle has also been validated. This study highlights the distinct emotional and neural mechanisms of AI-generated music, offering valuable insights into future AI-powered emotion-focused therapy strategies.

CogSci 2025

How Generative Music Affects the ISO Principle-Based Emotion-Focused Therapy: An EEG Study

electroencephalography (eeg)

mood

music

artificial intelligence

emotion

Recently, AI-generated content (AIGC) technologies have made remarkable advancements, even achieving superhuman performance across various domains. However, few previous studies have investigated its impact on emotion-focused therapy with artistic content, e.g., music. In this paper, we conducted an EEG experiment to explore the effects of generative music on emotion-focused music therapy based on the ISO principle. This experiment compared AI-generated and human-created music regarding the changes in participants' valence and arousal following negative emotion induction with the ISO principle adherence and non-adherence. The results show that generative music, with its harmonic consistency and simple rhythm, is more effective in supporting positive emotions and improving temporal lobe activity. Besides, the therapeutic effectiveness of generative music adhering to the ISO principle has also been validated. This study highlights the distinct emotional and neural mechanisms of AI-generated music, offering valuable insights into future AI-powered emotion-focused therapy strategies.

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.

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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.

Empathy was mainly considered a stable trait, and few studies have investigated whether
it can vary across different situations. This investigation explores how contextual empathy
of study participants varies across different social group relationship and positive/negative
event valence. In this study, participants were divided into high- or low-empathy group by
their scores of Empathy Scale. The in/out group membership was manipulated through a
point estimation paradigm, and event valence was operationalized by the emotion status
of the character in the story event. As expected, results showed main effects across all
factors that participants demonstrated contextual empathy differently. More importantly, an
ingroup bias is significantly emerged, with participants exhibiting enhanced contextual
empathy toward ingroup than outgroup character. Furthermore, positive story events
elicited more contextual empathic responses than negative events. These findings
evidently provide an empirical support for the context-dependent nature of empathy,
challenging its traditional conceptualization as an invariant trait.

Contextual Malleability of Empathy: Effects of Trait Level, Group Membership, and Event Valence on Emotional Empathy

Access to higher education (HE) is heralded as a pathway to social mobility and equity but remains elusive for Indigenous populations in high-income countries like the United States and Australia. Systemic racial inequities, deeply rooted in colonial histories, perpetuate barriers to HE access and attainment for Native American and Aboriginal and Torres Strait Islander communities. This essay employs a comparative analysis based on statistics and Indigenous policy frameworks, using Critical Race Theory (CRT) and marketisation as analytical lenses to interrogate these challenges. It examines how “Whiteness” shapes educational discourses and institutional practices, reinforcing exclusion and inequality. Key disparities are analyzed, including lower enrollment, geographic isolation, socio-economic disadvantage, and financial barriers. Contrasting outcomes—declining Native American enrollment in the U.S. versus rising Indigenous completion rates in Australia—underscore the importance of community-led, equity-focused policies. The essay advocates for transformative reforms that prioritize Indigenous voices, dismantle systemic barriers, and address colonial and neoliberal legacies.

Systemic Barriers to Indigenous Higher Education: A Comparative Analysis of the United States and Australia

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.

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

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.

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Contextual Malleability of Empathy: Effects of Trait Level, Group Membership, and Event Valence on Emotional Empathy