The representation of value in the prefrontal cortex during novel choices

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Decision-making requires knowing and learning the value of choices. Neurons encoding value attributes of decision variables have been widely reported in the prefrontal cortex (PFC) of non-human primates (NHPs). However, most evidence to date has stemmed from animals performing overtrained tasks which poorly reproduce naturalistic decision-making scenarios often involving novel options. The central aim of this project will be to investigate PFC's basic coding principles for value during novel or flexible choices. Recent data points to a 'distributed' value code akin to the well-understood spatial computations in the hippocampus, with neurons encoding specific 'places' in value space linked in a 'grid-code' fashion. This, together with PFC's capacity for probabilistic inference and encoding of abstract rules, may support the inference of value in novel contexts. Additionally, we will explore probabilistic representations of value by drawing comparisons between distributed coding schemes and probabilistic population codes, which rely on populations of neurons each tuned to one specific value of a given variable to encode the variable's entire probability distribution. One such example which may apply to PFC's diverse value and prediction error signals is distributional reinforcement learning, which proposes neurons learn to expect different parts of a reward probability distribution. We will probe these value-coding frameworks by performing high density recordings from the PFC, hippocampus and entorhinal cortex while NHPs make overtrained and novel choices in large probabilistic-value spaces.