Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time.

Social decision-making is increasingly studied with neurocomputational modeling. Here we discuss how this approach allows researchers to better understand and predict behavior in social settings. Using examples from the study of resource distributions and social learning, we illustrate how this methodology provides a flexible way to quantify social values and beliefs, identify specific motives and cognitive processes underlying social choice and learning, and arbitrate between competing theories of social behavior. We also critically discuss open questions and potential problems associated with this methodology.

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The brain is often able to learn complex structures of the environment using a very limited amount of evidence, which is crucial for model-based planning and sequential prediction. However, little is known about the neurocomputational mechanisms of deterministic sequential prediction, as prior work has primarily focused on stochastic transition structures. Here we find that human subjects' beliefs about a sequence of states, captured by reaction times, are well explained by a Bayesian pattern-learning model that tracks beliefs about both the current state and the underlying structure of the environment, taking into account prior beliefs about possible patterns in the sequence. Using functional magnetic resonance imaging, we find distinct neural signatures of uncertainty computations on both levels. These results support the hypothesis that structure learning in the brain employs Bayesian inference.