Paper List
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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
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Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
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Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
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Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
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The Effective Reproduction Number in the Kermack-McKendrick model with age of infection and reinfection
This paper addresses the challenge of accurately estimating the time-varying effective reproduction number ℛ(t) in epidemics by incorporating two crit...
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Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
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Covering Relations in the Poset of Combinatorial Neural Codes
This work addresses the core challenge of navigating the complex poset structure of neural codes to systematically test the conjecture linking convex ...
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Revealing stimulus-dependent dynamics through statistical complexity
This paper addresses the core challenge of detecting stimulus-specific patterns in neural population dynamics that remain hidden to traditional variab...
Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
School of Culture and Communication, Swansea University, United Kingdom | Department of Informatics, University of Oslo, Norway
The 30-Second View
IN SHORT: This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, particularly for international students facing linguistic and cultural barriers.
Innovation (TL;DR)
- Methodology Introduces the first publicly available text-to-image evaluation dataset with human judgment scores specifically for mental health communication, comprising 100 textual descriptions, 400 AI-generated images, and 400 categorical evaluation scores.
- Methodology Develops and evaluates four persona-based prompt templates (basic, illustrator, photographer, creative artist) rooted in contemporary counselling practices, with the illustrator persona achieving the highest total helpfulness score (284 out of possible 600).
- Biology Demonstrates that AI-generated images can facilitate self-expression of mental distress, with 44% of images rated as 'slightly helpful' and 27% as 'helpful', achieving a mean helpfulness score of 2.4 on a 0-6 scale.
Key conclusions
- The illustrator persona prompt achieved the highest total helpfulness score (284) and was selected as the 'best' image in 31% of cases, significantly outperforming other prompts (basic: 252, creative artist: 218, photographer: 210).
- Human evaluation shows minimal correlation with automatic semantic alignment metrics (Spearman's ρ=0.0271, Kendall's τ=0.0201), highlighting the need for emotion-aware evaluation frameworks beyond traditional similarity measures.
- AI-generated images demonstrated positive utility for mental distress expression, with 71% of images rated as at least 'slightly helpful' (score ≥2) and only 29% rated as 'not helpful' (score=0).
Abstract: Effective communication is central to achieving positive healthcare outcomes in mental health contexts, yet international students often face linguistic and cultural barriers that hinder their communication of mental distress. In this study, we evaluate the effectiveness of AI-generated images in supporting self-expression of mental distress. To achieve this, twenty Chinese international students studying at UK universities were invited to describe their personal experiences of mental distress. These descriptions were elaborated using GPT-4o with four persona-based prompt templates rooted in contemporary counselling practice to generate corresponding images. Participants then evaluated the helpfulness of generated images in facilitating the expression of their feelings based on their original descriptions. The resulting dataset comprises 100 textual descriptions of mental distress, 400 generated images, and corresponding human evaluation scores. Findings indicate that prompt design substantially affects perceived helpfulness, with the illustrator persona achieving the highest ratings. This work introduces the first publicly available text-to-image evaluation dataset with human judgment scores in the mental health domain, offering valuable resources for image evaluation, reinforcement learning with human feedback, and multi-modal research on mental health communication.