The Neurocomputational Basis of Serial Decision-Making

A hallmark of human behavior is serial decision-making, in which decisions are linked across time: the choices we make are informed by our past decisions and, in turn, influence our future decisions. Flexible, accurate goal-directed behavior breaks down when decisions become inconsistent with previous decisions and their outcomes. Such impairments contribute to the difficulty that people with schizophrenia and other psychiatric disorders have functioning in society. While there has been a large amount of research investigating the behavioral and neuronal mechanisms responsible for making individual decisions, there is a dearth of research on serial decision-making. The goal of my work has been to establish the formal study of serial decision-making and provide a psychophysical, computational, and neural foundation for future work. In Study 1, we showed that rhesus monkeys, a prime animal model for decision-making, can perform serial decision-making in a novel rule-selection task. The animals selected behavioral rules rationally and used those rules to flexibly discriminate between complex visual stimuli. In Study 2, we had human and monkey subjects perform variations on the rule-selection task to study how behavioral strategies for serial decision-making are dependent on task characteristics. We developed a set of normative probabilistic behavioral models and used Bayesian model selection to determine which model features best explained the observed behavioral data. Specifically, we found that whether or not humans use sensory information (in addition to reward information) to guide their future decisions is dependent on the lower-level features of the task. In Study 3, we investigated the role of one particular brain region, the supplementary eye field (SEF), in serial decision-making. The SEF is part of frontal cortex and sits at the intersection of oculomotor function and broader cognition, and previous studies have implicated it in linking sequences of decisions. We found that neuronal activity in the SEF encoded the rules used for decisions, predicted the outcomes of future decisions, and reacted to the outcomes of past decisions. The two outcome-related signals match what we expect of control signals necessary for flexibly and adaptively updating stimulus values in accordance with past decisions. Taken together, these three studies demonstrate that serial decision-making strategies are dependent on decision context and that the SEF may contribute to serial decision-making in dynamic environments.