title: Trial-by-trial identification of categorization strategy using iterative decision-bound modeling.
abstract: Identifying the strategy that participants use in laboratory experiments is crucial in interpreting the results of behavioral experiments. This article introduces a new modeling procedure called iterative decision-bound modeling (iDBM), which iteratively fits decision-bound models to the trial-by-trial responses generated from single participants in perceptual categorization experiments. The goals of iDBM are to identify: (1) all response strategies used by a participant, (2) changes in response strategy, and (3) the trial number at which each change occurs. The new method is validated by testing its ability to identify the response strategies used in noisy simulated data. The benchmark simulation results show that iDBM is able to detect and identify strategy switches during an experiment and accurately estimate the trial number at which the strategy change occurs in low to moderate noise conditions. The new method is then used to reanalyze data from Ell and Ashby (2006). Applying iDBM revealed that increasing category overlap in an information-integration category learning task increased the proportion of participants who abandoned explicit rules, and reduced the number of training trials needed to abandon rules in favor of a procedural strategy. Finally, we discuss new research questions made possible through iDBM.
authors: Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA. shelie@purdue.edu.
authors: Sébastien
authors: S
authors: Hélie
authors: Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, USA.
authors: Benjamin O
authors: BO
authors: Turner
authors: Information and Computing Sciences, SRI International, Menlo Park, CA, USA.
authors: Matthew J
authors: MJ
authors: Crossley
authors: Department of Psychology, University of Maine, Orono, ME, USA.
authors: Shawn W
authors: SW
authors: Ell
authors: Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, USA.
authors: F Gregory
authors: FG
authors: Ashby
mesh_terms: D003198:Computer Simulation; D003657:Decision Making; D005260:Female; D006801:Humans; D007858:Learning; D008960:Models, Psychological
keywords: Decision-bound modeling; Perceptual category learning; Response strategy; System switching