title: Using parameter space partitioning to evaluate a model's qualitative fit.
abstract: Parameter space partitioning (PSP) is a versatile tool for model analysis that detects the qualitatively distinctive data patterns a model can generate, and partitions a model's parameter space into regions corresponding to these patterns. In this paper, we propose a PSP fit measure that summarizes the outcome of a PSP analysis into a single number, which can be used for model selection. In contrast to traditional model selection methods, PSP-based model selection focuses on qualitative data. We demonstrate PSP-based model selection by use of application examples in the area of category learning. A large-scale model recovery study reveals excellent recovery properties, suggesting that PSP fit is useful for model selection.
authors: Faculty of Psychology and Educational Sciences, KU Leuven, University of Leuven, 3000, Leuven, Belgium. sara.steegen@kuleuven.be.
authors: Sara
authors: S
authors: Steegen
authors: Faculty of Psychology and Educational Sciences, KU Leuven, University of Leuven, 3000, Leuven, Belgium.
authors: Francis
authors: F
authors: Tuerlinckx
authors: Faculty of Psychology and Educational Sciences, KU Leuven, University of Leuven, 3000, Leuven, Belgium.
authors: Wolf
authors: W
authors: Vanpaemel
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D013223:Statistics as Topic
keywords: Categorization; Model selection; Parameter space partitioning; Qualitative model fit