Hu Mianzhi, Don Hilary J, Worthy Darrell A
Texas A&M University, College Station, TX, USA.
University College London, London, UK.
Commun Psychol. 2025 Apr 14;3(1):61. doi: 10.1038/s44271-025-00249-y.
In an uncertain world, human decision-making often involves adaptively leveraging different strategies to maximize gains. These strategic shifts, however, are overlooked by many traditional reinforcement learning models. Here, we incorporate parallel evaluation systems into distribution-based modeling and propose an entropy-weighted dual-process model that leverages Dirichlet and multivariate Gaussian distributions to represent frequency and value-based decision-making strategies, respectively. Model simulations and empirical tests demonstrated that our model outperformed traditional RL models by uniquely capturing participants' strategic change from value-based to frequency-based learning in response to heightened uncertainty. As reward variance increased, participants switched from focusing on actual rewards to using reward frequency as a proxy for value, thereby showing greater preference for more frequently rewarded but less valuable options. These findings suggest that increased uncertainty encourages the compensatory use of diverse evaluation methods, and our dual-process model provides a promising framework for studying multi-system decision-making in complex, multivariable contexts.
在一个不确定的世界中，人类决策通常涉及适应性地利用不同策略来实现收益最大化。然而，许多传统强化学习模型忽略了这些策略转变。在此，我们将并行评估系统纳入基于分布的建模中，并提出一种熵加权双过程模型，该模型利用狄利克雷分布和多元高斯分布分别表示基于频率和基于价值的决策策略。模型模拟和实证测试表明，我们的模型通过独特地捕捉参与者在不确定性增加时从基于价值的学习到基于频率的学习的策略转变，优于传统强化学习模型。随着奖励方差的增加，参与者从关注实际奖励转向使用奖励频率作为价值的代理，从而表现出对奖励频率更高但价值较低选项的更大偏好。这些发现表明，不确定性增加会促使人们补偿性地使用多种评估方法，并且我们的双过程模型为研究复杂多变量环境中的多系统决策提供了一个有前景的框架。
