Format

Send to

Choose Destination
Cogn Sci. 2008 Dec;32(8):1248-84. doi: 10.1080/03640210802414826.

A survey of model evaluation approaches with a tutorial on hierarchical bayesian methods.

Author information

1
Departments of Psychology & Cognitive Science, Indiana UniversityDepartment of Cognitive Sciences, University of California, IrvineDepartment of Psychology, University of Amsterdam.

Abstract

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways.

PMID:
21585453
DOI:
10.1080/03640210802414826
Free full text

LinkOut - more resources

Full Text Sources

Other Literature Sources

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center