The online metacognitive control of decisions

Difficult decisions typically involve mental effort, which signals the subjective cost of processing decision-relevant information. But how does the brain regulate mental effort? A possibility is that the brain optimizes a resource allocation problem, whereby the amount of invested resources optimally balances its expected cost (i.e. effort) and benefit. Our working assumption is that subjective decision confidence serves as the benefit term of the resource allocation problem, hence the “metacognitive” nature of decision control (Lee & Daunizeau, 2021). In this work, we present a computational model for the online metacognitive control of decisions or oMCD. Formally, oMCD is a Markov Decision Process that optimally solves the ensuing resource allocation problem under agnostic assumptions about the inner workings of the underlying decision system. We disclose its main properties, when coupled with two standard decision processes (namely: the ideal observer and the attribute integration cases). Importantly, we show that oMCD reproduces most established empirical results in the field of value-based decision making. Finally, we discuss the possible connexions of the model with most prominent neurocognitive theories about mental effort, and highlight potential extensions.