Mental Health Research Topics

Neueste Paper

• Self-Reports Do Not Identify Self-Models: An Identifiability Test for Counterfactual Reports

  Phongsakon Mark Konrad, Toygar Tanyel, Serkan Ayvaz · 30. September 2026

  Language-model self-reports are evidence about behavior in a prompt environment, not by themselves evidence of a self-model. We investigate counterfactual reports about affect-like states under activation interventions and ask whether the report remains bound to the named intervention when the demon…

• Ability-Residual Decoupled Modeling for Affective Cognitive Diagnosis

  Boyuan Zhao, Meng Ye · 21. September 2026

  Cognitive diagnosis infers students' concept mastery from response logs. However, students' responses are not determined by mastery alone: non-cognitive factors such as emotion, engagement, and fatigue can also affect performance. Affective cognitive diagnosis therefore extends conventional cognitiv…

• Efficient Diversity-based Experience Replay for Deep Reinforcement Learning

  Kaiyan Zhao, Yiming Wang, Yuyang Chen, Yan Li, Leong Hou U, Xiaoguang Niu · 10. September 2026

  Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences. However, existing experience replay methods, whether based on uniform or prioritized sampling, often suffer from low efficiency, particularly in real-world scenarios with high-di…

• A Computational Implementation of a Goal-Directed Theory of Affect

  Bernhard Hilpert, Tam\'as Sz\H{u}cs, Joost Broekens, Agnes Moors · 9. September 2026

  Computational modeling of emotion has long faced a tension between descriptive, "snapshot-based" appraisal models and granular, signal-driven architectures that often lack appropriate psychological grounding. This paper addresses this gap by presenting the first high-fidelity computational implement…

• Two locked tests of phase-structure features for transition prediction

  Abraham Chachamovits · 2. September 2026

  A published theoretical account of phase structure in rotary attention was subjected to two pre-specified empirical tests of whether phase-derived features improve prediction of a commitment or contradiction endpoint over a baseline that does not receive those features. Study 1 froze a contradiction…

• Different representation learning objectives recover distinct latent structures from the same psychometric data

  Cong Cao, Tassos C. Kyriakides, Pambos Vrasidas · 2. September 2026

  Psychometric questionnaires contain rich item-level information, yet it remains unclear whether different representation learning objectives recover the same latent organization. We investigated this question using 757 matched teacher-child pairs from the baseline assessment of the Cyprus ProW presc…

• Assessing mentalization in humans and large language models

  Aamir Sohail, Xintong Zhong, Arkady Konovalov, Patricia L. Lockwood, Lei Zhang · 28. August 2026

  Mentalization - the ability to infer others' beliefs and intentions to guide one's own choices - is a key cognitive function underlying human social interactions. Large language models (LLMs) demonstrate behaviour consistent with humans on theory-of-mind tasks, yet whether these models can guide ada…

• Detection != Reliable Control: Decodable Empathy Directions Yield at Most Partial Shifts in Automated Empathy Scores

  Haoran Jisun · 27. August 2026

  A decodable "empathy" direction is routinely read as a causal lever, conflating decodability, automated-metric control, and human-perceived change. We test this for two EPITOME-derived facets -- Recognition (cognitive) and Resonance (affective) -- in three instruction-tuned LLMs, scoring every inter…

• Evolutionary Recurrent Decision Model in Developing Adaptive and Maladaptive Behaviors

  Andrew Hu · 26. August 2026

  This study introduces the evolutionarily recurrent decision model (ERDM), a computational reinforcement learning framework designed to examine how evolutionary mismatch, bounded rationality, and satisficing contribute to adaptive and maladaptive behavior. ERDM simulates agents across evolutionary re…

• How AI Prompts Can Teach Us About the Structure of Human Behavior

  Matthew O. Jackson, Benjamin S. Manning, Yutong Xie, Walter Yuan, Qiaozhu Mei · 20. August 2026

  We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language model a ``type vector'' and then prompt it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) become…

• Small Foundation Models of Human Cognition and Behaviour

  Nick Oh, Fernand Gobet · 7. August 2026

  Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether these models process task structure or exploit statistical shortcuts, remain open questions. We train fourteen models from 135M to 14B parameters acr…

• Chain-of-Thought Monitoring Can Be Unreliable in Implicit-Influence Settings

  Agatha Duzan, Asa Cooper Stickland · 6. August 2026

  Chain-of-thought (CoT) monitoring is increasingly treated as an important safety layer for frontier reasoning models. Most monitorability evaluations study explicit-influence settings: setups where the prompt directly incentivizes the model to hide something, e.g., by instructing it to perform a hid…

• metasignal: A Python Package for Comprehensive Metacognitive Analysis and Decision-Making

  Saurabh Ranjan, Mukesh Makwana, Konstantina Sokratous, Brian Odegaard · 3. August 2026

  Metasignal is an open-source Python package for signal detection theory (SDT) and metacognitive measurement. It implements the 17 metacognitive measures evaluated by Rahnev (2025), together with the reference variables d' (perceptual sensitivity), response criterion c (response bias), and mean confi…

• Structured Prompting and Automated Evaluation in Fixed Synthetic Japanese-Language Counseling Dialogues

  Keita Kiuchi, Yoshikazu Fujimoto, Hideyuki Goto, Tomonori Hosokawa, Makoto Nishimura, Yosuke Sato, Izumi Sezai, Tomohiro Inoue · 7. Juli 2026

  Large language models (LLMs) may support counseling training, yet evidence from Japanese-language interactions and automated quality ratings remains limited. We examined 18 fixed Japanese-language counseling transcripts generated through artificial intelligence (AI)-to-AI interactions under three co…

• DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data

  Amartya Bhattacharya · 15. Juni 2026

  The Differential Time-Varying Effect Model (DTVEM) of Jacobson et al. (2019) is a popular tool for finding the best time lag in intensive longitudinal data, but it assumes everyone shares the same lag structure. The original authors named fixing this as future work, and it clashes with the premise o…

• Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs

  Mohammad Jalali, Azim Ospanov, Amin Gohari, Farzan Farnia · 10. Juni 2026

  Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce outputs remains underexplored. Existing diversity metrics such as Vendi and RKE, which are based on the von Neumann and R\'enyi entropies of kernel matrices, were developed f…

• Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology

  Ka Chun Lam, Bridget W Mahony, Armin Raznahan, Francisco Pereira · 8. Juni 2026

  Psychiatry research seeks to understand the manifestations of psychopathology in behavior, as measured in questionnaire data, by identifying a small number of latent factors that explain them. While factor analysis is the traditional tool for this purpose, the resulting factors may not be interpreta…

• Behavioral and Performance Indicators of Depression and Anxiety in Electronic Learning Systems

  Arya VarastehNezhad, Fattaneh Taghiyareh · 4. Juni 2026

  This study investigates whether behavioral and performance indicators derived from a Moodle-based learning management system are associated with university students' depression and anxiety in two undergraduate Computer Engineering courses. Using a quantitative observational design, LMS event logs, a…

• The Unsampled Truth: Psychometrics in SLMs Measure Prompt Artifacts, Not Psychological Constructs

  Nils Schwager, Christoph Hau, Simon M\"unker, Achim Rettinger · 3. Juni 2026

  When prompting SLMs for psychometric assessments, researchers assume the outputs reflect semantic reasoning. We evaluate this premise across 13 open-weights models (0.6B to 14B parameters) using a prompt variation framework that separates semantic signals from prompt artifacts. By systematically var…

• Psychological Constructs in Shared Semantic Space

  Hubert Plisiecki · 27. Mai 2026

  Psychological constructs are often measured in separate instruments, datasets, and research traditions, which makes direct comparison difficult. This paper proposes a framework for making such constructs semantically commensurate by representing and comparing them as directions in a shared word-embe…

• Bayesian Distributional Models of Executive Functioning

  Robert Kasumba, Zeyu Lu, Dom CP Marticorena, Mingyang Zhong, Paul Beggs, Anja Pahor, Geetha Ramani, Imani Goffney, Susanne M Jaeggi, Aaron R Seitz, Jacob R Gardner, Dennis L Barbour · 26. Mai 2026

  This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian Distributional Active LEarning (DALE) perform in comparison to conventional Independent Maximum Likelihood Estimation (IMLE). DLVM integrates observatio…

• DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods

  Maryia Zhyrko, Daisy Monika Lal, Erik van Mulligen, Lifeng Han · 25. Mai 2026

  We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task. Our system addresses three tasks: psychological state modeling, temporal change detection, and sequence-level summarization. For Task 1, we combine LLM-base…

• CARDIO-Affect: A Hamiltonian-Variability Framework for Spatio-Temporal Emotional Pattern Recognition with Manifold-Based Individual and Group Profiling

  Xiao Sun · 19. Mai 2026

  We present CARDIO-Affect, a complex-systems theoretical framework for long-term emotional dynamics in bounded social groups, with explicit uncertainty quantification at every layer. Long-period naturalistic emotion in stable small groups exhibits hallmarks of complex systems -- multi-stable attracto…

• Dissecting Discrete Soft Actor-Critic: Limitations and Principled Alternatives

  Reza Asad, Reza Babanezhad, Sharan Vaswani · 13. Mai 2026

  While Soft Actor-Critic (SAC) is highly effective in continuous control, its discrete counterpart (DSAC) performs poorly on challenging discrete-action domains such as Atari. Consequently, starting from DSAC, we revisit the design of actor-critic methods in this setting. First, we determine that the…

• Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data

  Pedro Henrique Ramos Pinto, Maria Jullyanna Ferreira Marques, Luiz Carlos Serramo Lopez · 11. Mai 2026

  K-means clustering is widely used in psychological and psychometric research to identify profiles, subgroups, and potential typologies, yet its classical formulation does not test whether such groups exist as latent psychological categories. Instead, K-means partitions multidimensional space into re…