The Importance of Falsification in Computational Cognitive Modeling. Issue 6 (June 2017)

• Record Type: Journal Article
• Authors: Palminteri, Stefano; Wyart, Valentin; Koechlin, Etienne
• Is Part Of: Trends in cognitive sciences. Volume 21:Issue 6(2017)
• Page Start: 425
• Page End: 433
• Publication Date: 2017-06
• Subjects: Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2017.03.011
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract : In the past decade the field of cognitive sciences has seen an exponential growth in the number of computational modeling studies. Previous work has indicated why and how candidate models of cognition should be compared by trading off their ability to predict the observed data as a function of their complexity. However, the importance of falsifying candidate models in light of the observed data has been largely underestimated, leading to important drawbacks and unjustified conclusions. We argue here that the simulation of candidate models is necessary to falsify models and therefore support the specific claims about cognitive function made by the vast majority of model-based studies. We propose practical guidelines for future research that combine model comparison and falsification. Trends: Computational modeling has grown exponentially in cognitive sciences in the past decade. Model selection most often relies on evaluating the ability of candidate models to predict the observed data. The ability of a candidate model to generate a behavioral effect of interest is rarely assessed, but can be used as an absolute falsification criterion. Recommended guidelines for model selection should combine the evaluation of both the predictive and generative performance of candidate models.