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<p>This study introduces a physics-inspired generative neural network model to explore the emergence and stabilization of psychological traits such as motivation, learned helplessness, and social anxiety in workplace contexts. By integrating self-esteem, self-efficacy, and self-concept as foundational cognitive states, the model employs the Maxwell-Boltzmann distribution and sigmoid activation to simulate dynamic equilibrium. Simulations across varied workplace environments demonstrate how supportive feedback fosters motivation, while high or low stress amplifies social anxiety or learned helplessness. This novel approach bridges gaps in cognitive modeling by providing a framework to predict trait adaptability and resilience in organizational settings, offering insights for workplace interventions and psychological well-being.</p>

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A generative neural network for psychological traits in workplace self-confidence: a physics-inspired approach

  • 期刊论文

  • 发表日期：

    2025年5月13日

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  • Billel Arbaoui,
  • Herison Surbakti

摘要

This study introduces a physics-inspired generative neural network model to explore the emergence and stabilization of psychological traits such as motivation, learned helplessness, and social anxiety in workplace contexts. By integrating self-esteem, self-efficacy, and self-concept as foundational cognitive states, the model employs the Maxwell-Boltzmann distribution and sigmoid activation to simulate dynamic equilibrium. Simulations across varied workplace environments demonstrate how supportive feedback fosters motivation, while high or low stress amplifies social anxiety or learned helplessness. This novel approach bridges gaps in cognitive modeling by providing a framework to predict trait adaptability and resilience in organizational settings, offering insights for workplace interventions and psychological well-being.