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BioLLMAgent: A Hybrid Framework with Enhanced Structural Interpretability for Simulating Human Decision-Making in Computational Psychiatry

arXiv:2603.05016v1 Announce Type: new Abstract: Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language model (LLM) agents generate realistic behaviors but lack structural interpretability. We introduce BioLLMAgent, a novel hybrid framework that combines validated cognitive models with the generative capabilities of LLMs. The framework comprises three core components: (i) an Internal RL Engine for experience-driven value learning; (ii) an External LLM Shell for high-level cognitive strategies and therapeutic interventions; and (iii) a Decision Fusion Mechanism for integrating components via weighted utility. Comprehensive experiments on the Iowa Gambling Task (IGT) across six clinical and healthy datasets demonstrate that BioLLMAgent accurately reproduces human behavioral patterns while maintaining excellent parameter identifiability (correlations $>0.67$). Furthermo

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Zuo Fei, Kezhi Wang, Xiaomin Chen, Yizhou Huang · · 1 min read · 5 views
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arXiv:2603.05016v1 Announce Type: new Abstract: Computational psychiatry faces a fundamental trade-off: traditional reinforcement learning (RL) models offer interpretability but lack behavioral realism, while large language model (LLM) agents generate realistic behaviors but lack structural interpretability. We introduce BioLLMAgent, a novel hybrid framework that combines validated cognitive models with the generative capabilities of LLMs. The framework comprises three core components: (i) an Internal RL Engine for experience-driven value learning; (ii) an External LLM Shell for high-level cognitive strategies and therapeutic interventions; and (iii) a Decision Fusion Mechanism for integrating components via weighted utility. Comprehensive experiments on the Iowa Gambling Task (IGT) across six clinical and healthy datasets demonstrate that BioLLMAgent accurately reproduces human behavioral patterns while maintaining excellent parameter identifiability (correlations $>0.67$). Furthermore, the framework successfully simulates cognitive behavioral therapy (CBT) principles and reveals, through multi-agent dynamics, that community-wide educational interventions may outperform individual treatments. Validated across reward-punishment learning and temporal discounting tasks, BioLLMAgent provides a structurally interpretable "computational sandbox" for testing mechanistic hypotheses and intervention strategies in psychiatric research.

Executive Summary

The article introduces BioLLMAgent, a novel hybrid framework that combines the strengths of reinforcement learning (RL) and large language model (LLM) agents to simulate human decision-making in computational psychiatry. By integrating cognitive models with the generative capabilities of LLMs, BioLLMAgent achieves both behavioral realism and structural interpretability. Comprehensive experiments demonstrate its accuracy in reproducing human behavioral patterns and its potential in simulating cognitive behavioral therapy (CBT) principles. BioLLMAgent also reveals the effectiveness of community-wide educational interventions in psychiatric research. This framework provides a valuable tool for testing mechanistic hypotheses and intervention strategies in psychiatric research, while promoting further exploration of LLMs in computational psychiatry.

Key Points

  • ▸ BioLLMAgent combines validated cognitive models with LLMs for enhanced structural interpretability and behavioral realism.
  • ▸ The framework achieves accurate simulation of human decision-making in computational psychiatry.
  • ▸ BioLLMAgent demonstrates potential in simulating CBT principles and community-wide educational interventions.

Merits

Strength in Combining Theoretical Models

BioLLMAgent successfully integrates cognitive models with LLMs, addressing the trade-off between interpretability and behavioral realism in computational psychiatry.

Structural Interpretability and Realism

The framework achieves both structural interpretability and behavioral realism, making it a valuable tool for psychiatric research.

Potential for Simulating CBT Principles

BioLLMAgent demonstrates potential in simulating CBT principles, revealing its potential in therapeutic research and interventions.

Demerits

Complexity and Scalability

The framework's complexity and scalability may pose challenges in its practical application and real-world deployment.

Limited Generalizability

The results may not generalize to other psychiatric conditions or populations, requiring further validation and refinement.

Dependence on LLMs

BioLLMAgent's performance may be heavily reliant on the capabilities and limitations of LLMs, which may evolve over time.

Expert Commentary

BioLLMAgent represents a significant advancement in computational psychiatry, combining the strengths of RL and LLMs to simulate human decision-making. The framework's ability to achieve both structural interpretability and behavioral realism is a testament to its potential in psychiatric research. However, its complexity and scalability may pose challenges in its practical application. Furthermore, the results may not generalize to other psychiatric conditions or populations, requiring further validation and refinement. Nevertheless, BioLLMAgent has the potential to revolutionize psychiatric research and therapeutic interventions, making it an exciting development in the field.

Recommendations

  • ✓ Further research is needed to validate the framework's generalizability across different psychiatric conditions and populations.
  • ✓ Investment in large-scale educational programs should be considered to address mental health issues, as suggested by the framework's simulation of community-wide educational interventions.

Sources

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