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Due Process in Dual Process: Model‐Recovery Simulations of Decision‐Bound Strategy Analysis in Category Learning
Author(s) -
Edmunds Charlotte E. R.,
Milton Fraser,
Wills Andy J.
Publication year - 2018
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12607
Subject(s) - dual (grammatical number) , process (computing) , computer science , decision process , artificial intelligence , process management , engineering , art , literature , operating system
Behavioral evidence for the COVIS dual‐process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, [Ashby, F. G., 2016]). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants' categorization strategies, a task that usually falls to Decision Bound analysis (Maddox & Ashby, [Maddox, W. T., 1993]). Here, we examine the accuracy of this analysis in a series of model‐recovery simulations. In Simulation 1, over a third of simulated participants using an Explicit (conjunctive) strategy were misidentified as using a Procedural strategy. In Simulation 2, nearly all simulated participants using a Procedural strategy were misidentified as using an Explicit strategy. In Simulation 3, we re‐examined a recently reported COVIS ‐supporting dissociation (Smith et al., [Smith, J. D., 2014]) and found that these misidentification errors permit an alternative, single‐process, explanation of the results. Implications for due process in the future evaluation of dual‐process theories, including recommendations for future practice, are discussed.