DnReLU · Decision noise in reward-guided learning amidst option unavailability

Navigating a new city's dining scene often confronts us with unavailable options, e.g., 'fully booked,' shaping our behavioral variability such as random exploratory choices and learning errors. Existing theories of reward-guided learning typically overlook option unavailability. Hypothesized strategies may vary, from adjusting action stochasticityover-exploration to broadly check alternatives, or under-exploration to quickly latch onto available favoritesto adjusting learning process, e.g., weighing past choices for maximal future rewards, or heuristically inflating the value of busy venues while devaluating others ('busier = better'). A nuanced understanding of these tactics requires a deep dive into information sampling, encompassing not just overt evidence-seeking actions but also covert attentional sampling during value updates in the internal model of the world. These complex sampling patterns, unfolding rapidly over hundreds of milliseconds, have been chiefly studied through eye-tracking that captures overt oculomotor variables, leaving the subtleties of covert sampling unexplored and detailed mechanistic insights elusive. I aim to address this issue across multiple scalesfrom neurotransmitter and neural dynamics to biophysically-grounded modeling of behavior. Using pharmaco-magnetoencephalography (MEG), I will track covert attentional focus across the entire timespan of every single reward-guided decision during learning, in detailed source-localized brain regions, while also perturbing neurochemical systems to establish causal links. This research promises to illuminate longstanding questions, such as why behavioral variability persists despite evolutionary pressures favoring reward-maximizing decisions. The training through this MSCA in circuit-level mechanistic models, imparted by world-leading experts, will complement my existing expertise, and establish me as an independent researcher in the burgeoning field of cognitive computational neuroscience.