Sequential effects: A Bayesian analysis of prior bias on reaction time and behavioral choice

Human subjects exhibit “sequential effects ” in many psychological experiments, in which they respond more rapidly and accurately to a stimulus when it reinforces a local pattern in stimulus history, compared to when it violates such a pattern. This is often the case even if the local pattern arises by chance, such that stimu-lus history has no real predictive power, and therefore any behavioral adjustment based on these erroneous pre-dictions essentially amounts to superstition. Earlier, we proposed a normative Bayesian learning model, the Dy-namic Belief Model (DBM), to demonstrate that such be-havior reflects the engagement of mechanisms that iden-tify and adapt to changing patterns in the environment (Yu & Cohen, 2009). In that earlier work, we assumed a monotonic relationship between prior bias and response time (bias toward a stimulus was assumed to result in faster reaction time when that was the actual stimu-lus; conversely, when the other stimulus was present, it was assumed to result in a slower response). Here, we present a more detailed and quantitative analysis of the relationship between prior bias and behavioral out-come, in terms of response time and choice accuracy. We also present novel behavioral data, along with a frame-work for jointly identifying subject-specific parameters of the trial-by-trial learning (Dynamic Belief Model, DBM) and within-trial sensory processing and decision-making (Drift-Diffusion Model, DDM) based on the behavioral data. Our results provide strong evidence for DBM, and reveal two distinct sub-populations of individuals, who have differential beliefs about the timescale at which lo-cal patterns persist in sequential data