The neurobiological and computational origins of behavioral variability
Human behavior is inherently variable. Even when faced with the same exact choice options, we often take different actions. The causes for this inconsistency are unknown, but economic and cognitive theories assume this is due to noise that is injected when making a decision. However, I have recently demonstrated that noise might not just arise during the decision process, but that the learning process itself (i.e update of internal representations based on feedback) is subject to substantial and meaningful noise. Concretely, I have shown that noise during learning accounts for the majority of what is traditionally reported as ‘decision noise’. However, the neural mechanisms underlying this learning noise remains unknown. In this fellowship, I will examine the contributions of the locus coeruleus-noradrenaline (LC-NA) system to this learning noise. NA has previously been associated with decision noise and here I will test whether activity in the LC is the driving factor behind learning noise. I will use a cutting-edge real-time fMRI framework that allows to causally test whether ongoing LC activity directly influences learning noise. Moreover, I will examine whether this learning noise is relevant to impulsivity, which has previously been implicated in decision noise. This fellowship has the potential to overthrown the traditional view on behavioral variability in decision making and will provide a novel neurobiological, computational and psychiatric grounding for understanding why humans are consistently inconsistent.