Joint modeling of choices and reaction times based on Bayesian contextual behavioral control

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models, for example in a joint reinforcement learning and diffusion decision model. We propose a novel joint model of both choices and reaction times by combining a mechanistic account of Bayesian sequential decision making with a sampling procedure. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by an MCMC sampler to obtain both choices and reaction times. We show that we can explain and reproduce well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. First, we use the proposed model to explain how instrumental learning and automatized behavior result in decreased reaction times and improved accuracy. Second, we reproduce classical results in the Eriksen flanker task. Third, we reproduce established findings in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in all these types of decision making paradigms.