A healthy fear of the unknown: perspectives on the interpretation of parameter fits from computational models in neuroscience.

ABSTRACT: Fitting models to behavior is commonly used to infer the latent computational factors responsible for generating behavior. However, the complexity of many behaviors can handicap the interpretation of such models. Here we provide perspectives on problems that can arise when interpreting parameter fits from models that provide incomplete descriptions of behavior. We illustrate these problems by fitting commonly used and neurophysiologically motivated reinforcement-learning models to simulated behavioral data sets from learning tasks. These model fits can pass a host of standard goodness-of-fit tests and other model-selection diagnostics even when the models do not provide a complete description of the behavioral data. We show that such incomplete models can be misleading by yielding biased e

SUBMITTER: Nassar MR 

PROVIDER: S-EPMC3617224 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature