ArticlePsychonomic bulletin & review2017
Using parameter space partitioning to evaluate a model's qualitative fit.
Article in Psychonomic bulletin & review, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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3 authors.
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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.
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