Vloeberghs Robin, Urai Anne E, Desender Kobe, Linderman Scott W
Brain and Cognition, KU Leuven, Leuven, Belgium.
Cognitive Psychology, Leiden University, Leiden, The Netherlands.
PLoS Comput Biol. 2025 Jul 29;21(7):e1013291. doi: 10.1371/journal.pcbi.1013291. eCollection 2025 Jul.
Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise to apparent history effects and underestimates perceptual sensitivity. We then present a hierarchical estimation procedure capable of reliably recovering the underlying state of the fluctuating decision criterion with as few as 500 trials per participant, offering a robust tool for researchers with typical human datasets. Critically, hMFC does not only accurately recover the state of the underlying decision criterion, it also effectively deals with the confounds caused by criterion fluctuations. Lastly, we provide code and a comprehensive demo to enable widespread application of hMFC in decision-making research.
经典决策模型假设，产生选择行为的参数是稳定的，但新兴研究表明，这些参数可能会随时间波动。在神经活动和行为策略中观察到的这种波动，对于理解决策过程具有重要意义。然而，由于估计这些波动需要大量数据，关于人类决策策略波动的实证研究一直有限。在此，我们引入了hMFC（标准波动分层模型），这是一个贝叶斯框架，旨在从有限的数据中估计决策标准的缓慢波动。我们首先展示了考虑决策标准波动的重要性：错误地假设标准稳定会产生明显的历史效应，并低估感知敏感性。然后，我们提出了一种分层估计程序，能够以每位参与者少至500次试验可靠地恢复波动决策标准的潜在状态，为拥有典型人类数据集的研究人员提供了一个强大的工具。至关重要的是，hMFC不仅能准确恢复潜在决策标准的状态，还能有效处理由标准波动引起的混淆因素。最后，我们提供了代码和全面的演示，以实现hMFC在决策研究中的广泛应用。
