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数据驱动的方程发现揭示了人类的非线性强化学习

Kyle J LaFollette^1,2, Janni Yuval³, Roey Schurr⁴

¹Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH 44106.

Proceedings of the National Academy of Sciences of the United States of America

|July 31, 2025

概括

一个新的方程Q权重模型通过结合非线性动态和消极偏差来改进强化学习 (RL) 预测. 与传统的线性方法相比,这种先进的计算模型为人类学习和决策提供了更好的洞察力.

科学领域:

认知科学认知科学
计算神经科学是一种神经科学.
行为经济学是一种行为经济学.

背景情况:

强化学习 (RL) 模型对于理解人类决策至关重要.
传统的RL模型通常使用线性更新来预期奖励,这可能过于简化了复杂的人类行为.
需要更细微的计算模型来捕捉学习和奖励处理的复杂性.

研究的目的:

开发和验证一种新的强化学习 (RL) 计算模型,解决传统线性模型的局限性.
探索方程发现算法的应用,从行为数据中发现新的RL模型.
调查非线性动态和消极偏见在人类奖励预测错误中的作用.

主要方法:

利用方程发现算法,一种从物理和生物学中改编的方法,以识别潜在的RL模型.
提出了一种新的模型,即二进制Q加权模型,基于捕捉线性和非线性函数的微分方程.
使用九个已发表的数据集对经典RL模型进行了模型的概括性和预测准确性的测试.

主要成果:

在9个已发表的数据集中,8个中,方程Q权重模型表现出比传统模型更高的预测准确度.
该模型显示,奖励预测错误遵循非线性动态,并表现出消极偏见.
研究结果表明,当期望较低时,奖励的权重较低,当期望较高时,奖励缺席的权重过高.

结论:

方程Q权重模型提供了一个更准确,更易于解释的方法来建模人类的学习和决策.
这项研究强调了将行为任务与先进的计算方法整合在一起,以发现认知模式的力量.
这些发现代表了在开发人类认知的广泛适用和有洞察力的计算模型方面取得的重大进展.

关键词:

动态系统是动态系统.机器学习是机器学习.非线性建模非线性建模强化学习是一种强化学习.

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