Models that learn how humans learn: The case of decision-making and its disorders.

ABSTRACT: Popular computational models of decision-making make specific assumptions about learning processes that may cause them to underfit observed behaviours. Here we suggest an alternative method using recurrent neural networks (RNNs) to generate a flexible family of models that have sufficient capacity to represent the complex learning and decision- making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task and, in this way, learns to imitate the processes underlying subjects' choices and their learning abilities. We demonstrate the benefits of this approach using a new dataset drawn from patients with either unipolar (n = 34) or bipolar (n = 33) depression and matched healthy controls (n = 34) making decis

SUBMITTER: Dezfouli A 

PROVIDER: S-EPMC6588260 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature