A Neural Circuit Framework for Economic Choice: From Building Blocks of Valuation to Compositionality in Multitasking.

Value-guided decisions are at the core of reinforcement learning and neuroeconomics, yet the basic computations they require remain poorly understood at the mechanistic level. For instance, how does the brain implement the multiplication of reward magnitude by probability to yield an expected value? Where within a neural circuit is the indifference point for comparing reward types encoded? How do learned values generalize to novel options? Here, we introduce a biologically plausible model that adheres to Dale's law and is trained on five choice tasks, offering potential answers to these questions. The model captures key neurophysiological observations from the orbitofrontal cortex of monkeys and generalizes to novel offer values. Using a single network model to solve diverse tasks, we identified compositional neural representations-quantified via task variance analysis and corroborated by curriculum learning. This work provides testable predictions that probe the neural basis of decision making and its disruption in neuropsychiatric disorders.