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Disentangled behavioral representations
Conference

Disentangled behavioral representations

Abstract

Individual characteristics in human decision-making are often quantified by fitting a parametric cognitive model to subjects' behavior and then studying differences between them in the associated parameter space. However, these models often fit behavior more poorly than recurrent neural networks (RNNs), which are more flexible and make fewer assumptions about the underlying decision-making processes. Unfortunately, the parameter and latent …

Authors

Dezfouli A; Ashtiani H; Ghattas O; Nock R; Dayan P; Ong CS

Volume

32

Publication Date

January 1, 2019

Conference proceedings

Advances in Neural Information Processing Systems

ISSN

1049-5258

Labels

Fields of Research (FoR)