title: Decisional separability, model identification, and statistical inference in the general recognition theory framework.
abstract: Recent work in the general recognition theory (GRT) framework indicates that there are serious problems with some of the inferential machinery designed to detect perceptual and decisional interactions in multidimensional identification and categorization (Mack, Richler, Gauthier, & Palmeri, 2011). These problems are more extensive than previously recognized, as we show through new analytic and simulation-based results indicating that failure of decisional separability is not identifiable in the Gaussian GRT model with either of two common response selection models. We also describe previously unnoticed formal implicational relationships between seemingly distinct tests of perceptual and decisional interactions. Augmenting these formal results with further simulations, we show that tests based on marginal signal detection parameters produce unacceptably high rates of incorrect statistical significance. We conclude by discussing the scope of the implications of these results, and we offer a brief sketch of a new set of recommendations for testing relationships between dimensions in perception and response selection in the full-factorial identification paradigm.
authors.affiliation: Center for Advanced Study of Language, University of Maryland, College Park, MD, USA. nsilbert@umd.edu
authors.forename: Noah H
authors.initials: NH
authors.lastname: Silbert
authors.forename: Robin D
authors.initials: RD
authors.lastname: Thomas
mesh_terms: D003657:Decision Making; D006801:Humans; D008960:Models, Psychological; D010364:Pattern Recognition, Visual; D011582:Psychological Theory; D021641:Recognition, Psychology; D017603:Signal Detection, Psychological