Tootfinder

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
Lionel Wong, Katherine M. Collins, Lance Ying, Cedegao E. Zhang, Adrian Weller, Tobias Gersternberg, Timothy O'Donnell, Alexander K. Lew, Jacob D. Andreas, Joshua B. Tenenbaum, Tyler Brooke-Wilson
https://arxiv.org/abs/2507.12547

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
When faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions. What permits us to draw in globally relevant information and reason over it coherently? Here, we explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations. We propose a computational implementation of this idea -- a `…

Interpreting Radiologist's Intention from Eye Movements in Chest X-ray Diagnosis
Trong-Thang Pham, Anh Nguyen, Zhigang Deng, Carol C. Wu, Hien Van Nguyen, Ngan Le
https://arxiv.org/abs/2507.12461

Interpreting Radiologist's Intention from Eye Movements in Chest X-ray Diagnosis
Radiologists rely on eye movements to navigate and interpret medical images. A trained radiologist possesses knowledge about the potential diseases that may be present in the images and, when searching, follows a mental checklist to locate them using their gaze. This is a key observation, yet existing models fail to capture the underlying intent behind each fixation. In this paper, we introduce a deep learning-based approach, RadGazeIntent, designed to model this behavior: having an intention t…

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Manish Bhatt
https://arxiv.org/abs/2506.11423 https://

Bhatt Conjectures: On Necessary-But-Not-Sufficient Benchmark Tautology for Human Like Reasoning
Debates about whether Large Language or Reasoning Models (LLMs/LRMs) truly reason or merely pattern-match suffer from shifting goal posts. In my personal opinion, two analytic--hence "tautological"--benchmarks cut through that fog in my mental model. In this paper, I attempt to write down my mental model in concrete terms.

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery https://arxiv.org/abs/2506.06898

NSD-Imagery: A benchmark dataset for extending fMRI vision decoding methods to mental imagery
We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired with seen images that enabled unprecedented improvements in fMRI-to-image reconstruction efforts. Recent models trained on NSD have been evaluated only on seen image reconstruction. Using NSD-Imagery, it is possible to assess how well these models perform on mental image reconstruction. This is a chal…

Human vs. LLM-Based Thematic Analysis for Digital Mental Health Research: Proof-of-Concept Comparative Study
Karisa Parkington, Bazen G. Teferra, Marianne Rouleau-Tang, Argyrios Perivolaris, Alice Rueda, Adam Dubrowski, Bill Kapralos, Reza Samavi, Andrew Greenshaw, Yanbo Zhang, Bo Cao, Yuqi Wu, Sirisha Rambhatla, Sridhar Krishnan, Venkat Bhat
https:/…

Human vs. LLM-Based Thematic Analysis for Digital Mental Health Research: Proof-of-Concept Comparative Study
Thematic analysis provides valuable insights into participants' experiences through coding and theme development, but its resource-intensive nature limits its use in large healthcare studies. Large language models (LLMs) can analyze text at scale and identify key content automatically, potentially addressing these challenges. However, their application in mental health interviews needs comparison with traditional human analysis. This study evaluates out-of-the-box and knowledge-base LLM-based t…

How attention simplifies mental representations for planning
Jason da Silva Castanheira, Nicholas Shea, Stephen M. Fleming
https://arxiv.org/abs/2506.09520

How attention simplifies mental representations for planning
Human planning is efficient -- it frugally deploys limited cognitive resources to accomplish difficult tasks -- and flexible -- adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception, and perception shapes planning -- but the perceptual and attentional …

On the capabilities of LLMs for classifying and segmenting time series of fruit picking motions into primitive actions
Eleni Konstantinidou, Nikolaos Kounalakis, Nikolaos Efstathopoulos, Dimitrios Papageorgiou
https://arxiv.org/abs/2507.07745

On the capabilities of LLMs for classifying and segmenting time series of fruit picking motions into primitive actions
Despite their recent introduction to human society, Large Language Models (LLMs) have significantly affected the way we tackle mental challenges in our everyday lives. From optimizing our linguistic communication to assisting us in making important decisions, LLMs, such as ChatGPT, are notably reducing our cognitive load by gradually taking on an increasing share of our mental activities. In the context of Learning by Demonstration (LbD), classifying and segmenting complex motions into primitiv…

GenIR: Generative Visual Feedback for Mental Image Retrieval
Diji Yang, Minghao Liu, Chung-Hsiang Lo, Yi Zhang, James Davis
https://arxiv.org/abs/2506.06220

GenIR: Generative Visual Feedback for Mental Image Retrieval
Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind, that is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which…

Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency
Chadha Degachi, Samuel Kernan Freire, Evangelos Niforatos, Gerd Kortuem
https://arxiv.org/abs/2506.03807

Understanding Mental Models of Generative Conversational Search and The Effect of Interface Transparency
The experience and adoption of conversational search is tied to the accuracy and completeness of users' mental models -- their internal frameworks for understanding and predicting system behaviour. Thus, understanding these models can reveal areas for design interventions. Transparency is one such intervention which can improve system interpretability and enable mental model alignment. While past research has explored mental models of search engines, those of generative conversational search re…

Political alignment correlates with self-reported mental health. I hate this.
I wonder how much of the correlation is actual mental health as opposed to willingness to admit when it’s bad.
(source: https://www.natesilver.net/p/sbsq-21-why-young-men-do…

SBSQ #21: Why young men don't like Democrats
Plus, the New York City mayor’s race — and whether win probability models are broken.

Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence
Bosubabu Sambana, Kondreddygari Archana, Suram Indhra Sena Reddy, Shaik Meethaigar Jameer Basha, Shaik Karishma
https://arxiv.org/abs/2506.23503

Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence
Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analy…

This https://arxiv.org/abs/2505.17663 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States
As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks assess basic ToM abilities, they predominantly focus on static snapshots of mental states, overlooking the temporal evolution that characterizes real-world social interactions. We present \textsc{DynToM}, a novel benchmark specifically designed to evaluate LLM…

Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
Yifan Gao, Jiao Fu, Long Guo, Hong Liu
https://arxiv.org/abs/2507.00693

Leveraging Large Language Models for Spontaneous Speech-Based Suicide Risk Detection
Early identification of suicide risk is crucial for preventing suicidal behaviors. As a result, the identification and study of patterns and markers related to suicide risk have become a key focus of current research. In this paper, we present the results of our work in the 1st SpeechWellness Challenge (SW1), which aims to explore speech as a non-invasive and easily accessible mental health indicator for identifying adolescents at risk of suicide.Our approach leverages large language model (LLM…

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
T. T. J. E. Arets, G. Perugia, M. Houben, W. A. IJsselsteijn
https://arxiv.org/abs/2506.10927

The Role of Generative AI in Facilitating Social Interactions: A Scoping Review
Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to…

Visualizing Cloud-native Applications with KubeDiagrams
Philippe Merle, Fabio Petrillo
https://arxiv.org/abs/2505.22879 https://arxiv…

Visualizing Cloud-native Applications with KubeDiagrams
Modern distributed applications increasingly rely on cloud-native platforms to abstract the complexity of deployment and scalability. As the de facto orchestration standard, Kubernetes enables this abstraction, but its declarative configuration model makes the architectural understanding difficult. Developers, operators, and architects struggle to form accurate mental models from raw manifests, Helm charts, or cluster state descriptions. We introduce KubeDiagrams, an open-source tool that trans…

"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Kellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei Choo
https://arxiv.org/abs/2506.09354

"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support. Peer support, grounded in lived experience, offers a valuable complement to professional care. However, variability in training, effectiveness, and definitions raises concerns about quality, consistency, and safety. Large Language Models (LLMs) present new opportunities to enhance peer support interactions, particularly in real-time, text-based interactions. We present …

A Quantum-Inspired Conceptual Model of Collective Subjective Evaluation via Bloch Sphere Dynamics and Like-Polarization
Bumned Soodchomshom
https://arxiv.org/abs/2506.01847

A Quantum-Inspired Conceptual Model of Collective Subjective Evaluation via Bloch Sphere Dynamics and Like-Polarization
We propose a conceptual quantum-inspired model to describe subjective evaluation processes such as taste testing or consumer preference scoring-through the continuous time evolution of cognitive states represented on the Bloch sphere. Unlike conventional quantum cognition models that rely on projective measurements and state collapse, our framework treats evaluation as a weak or generalized measurement, preserving quantum coherence during the act of judgment. The mental state of preference is m…

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel, Seyed Yahya Shirazi, Isabelle Guyon, Alexandre R. Franco, Michael P. Milham, Aviv Dotan, Scott Makeig, Alexandre Gramfort, Jean-Remi King, Marie-Constance Corsi, Pedro A. Vald\'es-Sosa, Amit Majumdar, Alan Evans, Terrence J Sejnowski, Oren Shriki, Sylvain Chevallier, Arnaud Delorme

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG da…

The Thin Line Between Comprehension and Persuasion in LLMs
Adrian de Wynter, Tangming Yuan
https://arxiv.org/abs/2507.01936 https://a…

The Thin Line Between Comprehension and Persuasion in LLMs
Large language models (LLMs) are excellent at maintaining high-level, convincing dialogues. They are being fast deployed as chatbots and evaluators in sensitive areas, such as peer review and mental health applications. This, along with the disparate accounts on their reasoning capabilities, calls for a closer examination of LLMs and their comprehension of dialogue. In this work we begin by evaluating LLMs' ability to maintain a debate--one of the purest yet most complex forms of human communic…

Bayesian Regression Analysis with the Drift-Diffusion Model
Zekai Jin (Mental Health Data Science, New York State Psychiatric Institute, New York, USA), Yaakov Stern (Departments of Neurology, Columbia University Irving Medical Center, New York, USA), Seonjoo Lee (Mental Health Data Science, New York State Psychiatric Institute, New York, USA, Department of Biostatistics, Columbia University Irving Medical Center, New York, USA)

Bayesian Regression Analysis with the Drift-Diffusion Model
The Drift-Diffusion Model (DDM) is widely used in neuropsychological studies to understand the decision process by incorporating both reaction times and subjects' responses. Various models have been developed to estimate DDM parameters, with some employing Bayesian inference. However, when examining associations between phenotypes of interest and DDM parameters, most studies adopt a two-step approach: first estimating DDM parameters, then applying a separate statistical model to the estimated v…

This https://arxiv.org/abs/2503.16456 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care
This position paper argues for a fundamental shift in how Large Language Models (LLMs) are integrated into the mental health care domain. We advocate for their role as co-creators rather than mere assistive tools. While LLMs have the potential to enhance accessibility, personalization, and crisis intervention, their adoption remains limited due to concerns about bias, evaluation, over-reliance, dehumanization, and regulatory uncertainties. To address these challenges, we propose two structured …

From Reality to Recognition: Evaluating Visualization Analogies for Novice Chart Comprehension
Oliver Huang, Patrick Lee, Carolina Nobre
https://arxiv.org/abs/2506.03385

From Reality to Recognition: Evaluating Visualization Analogies for Novice Chart Comprehension
Novice learners often have difficulty learning new visualization types because they tend to interpret novel visualizations through the mental models of simpler charts they have previously encountered. Traditional visualization teaching methods, which usually rely on directly translating conceptual aspects of data into concrete data visualizations, often fail to attend to the needs of novice learners navigating this tension. To address this, we conducted an empirical exploration of how analogies…

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Jiayue Melissa Shi, Dong Whi Yoo, Keran Wang, Violeta J. Rodriguez, Ravi Karkar, Koustuv Saha
https://arxiv.org/abs/2506.15047

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers
Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot …

Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
Runlong Ye, Zeling Zhang, Boushra Almazroua, Michael Liut
https://arxiv.org/abs/2506.20062

Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents
AI-powered code assistants are widely used to generate code completions, significantly boosting developer productivity. However, these tools typically present suggestions without explaining their rationale, leaving their decision-making process inscrutable. This opacity hinders developers' ability to critically evaluate the output, form accurate mental models, and build calibrated trust in the system. To address this, we introduce CopilotLens, a novel interactive framework that reframes code co…

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Weiheng Lu, Chunfeng Song, Jiamin Wu, Pengyu Zhu, Yuchen Zhou, Weijian Mai, Qihao Zheng, Wanli Ouyang
https://arxiv.org/abs/2506.18962

UniMind: Unleashing the Power of LLMs for Unified Multi-Task Brain Decoding
Decoding human brain activity from electroencephalography (EEG) signals is a central challenge at the intersection of neuroscience and artificial intelligence, enabling diverse applications in mental state assessment, clinical monitoring, and human-machine interaction. Recent efforts have extensively explored EEG-based brain foundation models for generalized brain decoding, employing large-scale training on multiple datasets. However, most of these attempts struggle with generalizability and fa…