A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion

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.Author summary: Every day, we make numerous decisions, from choosing what to eat to how we interpret the world around us. Traditionally, researchers have assumed that a key part of our decision-making process, namely the decision criterion, stays stable over time. However, increasing evidence suggests that instead of being stable, the decision criterion can fluctuate from moment to moment. In this work, we introduce the Hierarchical Model for Fluctuations in Criterion (hMFC), a new computational model that can accurately estimate these fluctuations, even with limited data. Capturing these moment-to-moment changes is critical: ignoring them can bias estimates of perceptual sensitivity and the influence of past choices on current decisions. By estimating criterion fluctuations, hMFC can correct these biases. With hMFC, we offer researchers a powerful tool for uncovering the dynamic nature of human decisions. To support wide adoption, we also provide open source software together with a demo, making this approach accessible to the broader scientific community.