Due Process in Dual Process: Model-Recovery Simulations of Decision-Bound Strategy Analysis in Category Learning.

Behavioral evidence for the COVIS dual-process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, ). 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, ). 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., ) 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.

[1]  P C Molenaar,et al.  Finite mixture distribution models of simple discrimination learning , 2001, Memory & cognition.

[2]  M. Kalish,et al.  Working memory does not dissociate between different perceptual categorization tasks. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[3]  Daphna Shohamy,et al.  Strategies in probabilistic categorization: results from a new way of analyzing performance. , 2006, Learning & memory.

[4]  D. Lagnado,et al.  Insight and strategy in multiple-cue learning. , 2006, Journal of experimental psychology. General.

[5]  State-trace analysis can be an appropriate tool for assessing the number of cognitive systems: A reply to Ashby (2014) , 2014, Psychonomic bulletin & review.

[6]  D. Shanks,et al.  Characteristics of dissociable human learning systems , 1994, Behavioral and Brain Sciences.

[7]  Estes Wk The problem of inference from curves based on group data. , 1956 .

[8]  Shawn W. Ell,et al.  Procedural learning in perceptual categorization , 2003, Memory & cognition.

[9]  W T Maddox,et al.  On the dangers of averaging across observers when comparing decision bound models and generalized context models of categorization , 1999, Perception & psychophysics.

[10]  Andrew Heathcote,et al.  An introduction to good practices in cognitive modeling , 2015 .

[11]  T. Griffiths,et al.  Modeling individual differences using Dirichlet processes , 2006 .

[12]  M. Kalish,et al.  The effect of feedback delay and feedback type on perceptual category learning: the limits of multiple systems. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[13]  Fraser Milton,et al.  Memory for exemplars in category learning , 2016, CogSci.

[14]  F. Gregory Ashby,et al.  Chapter 25 – MULTIPLE SYSTEMS OF PERCEPTUAL CATEGORY LEARNING: THEORY AND COGNITIVE TESTS , 2005 .

[15]  Fraser Milton,et al.  Is overall similarity classification less effortful than single-dimension classification? , 2013, Quarterly journal of experimental psychology.

[16]  F Gregory Ashby,et al.  The role of feedback contingency in perceptual category learning. , 2016, Journal of experimental psychology. Learning, memory, and cognition.

[17]  M. Kalish,et al.  The dimensionality of perceptual category learning: A state-trace analysis , 2010, Memory & cognition.

[18]  H. Akaike A new look at the statistical model identification , 1974 .

[19]  Shawn W. Ell,et al.  Critrial noise effects on rule-based category learning: The impact of delayed feedback , 2009, Attention, perception & psychophysics.

[20]  W T Maddox,et al.  Comparing decision bound and exemplar models of categorization , 1993, Perception & psychophysics.

[21]  W. T. Maddox,et al.  Annals of the New York Academy of Sciences Human Category Learning 2.0 Brief Review of First-generation Research , 2022 .

[22]  Shawn W. Ell,et al.  Prefrontal Contributions to Rule-based and Information-integration Category Learning , 2022 .

[23]  Andy J. Wills,et al.  State-Trace Analysis: Dissociable Processes in a Connectionist Network? , 2015, Cogn. Sci..

[24]  J. D. Smith,et al.  Deferred Feedback Sharply Dissociates Implicit and Explicit Category Learning , 2014, Psychological science.

[25]  F. Ashby,et al.  The effects of concurrent task interference on category learning: Evidence for multiple category learning systems , 2001, Psychonomic bulletin & review.

[26]  W. T. Maddox,et al.  Dual-task interference in perceptual category learning , 2006, Memory & cognition.

[27]  Daniel R. Little,et al.  Logical-rule models of classification response times: a synthesis of mental-architecture, random-walk, and decision-bound approaches. , 2010, Psychological review.

[28]  Fraser Milton,et al.  Combination or Differentiation? Two theories of processing order in classification , 2015, Cognitive Psychology.

[29]  F. Gregory Ashby,et al.  Formal Approaches in Categorization: COVIS , 2011 .

[30]  W Todd Maddox,et al.  Disrupting feedback processing interferes with rule-based but not information-integration category learning , 2004, Memory & cognition.

[31]  A. Wills,et al.  A Comparison of the neural correlates that underlie rule‐based and information‐integration category learning , 2016, Human brain mapping.

[32]  R. Siegler The perils of averaging data over strategies: An example from children's addition. , 1987 .

[33]  W. Estes The problem of inference from curves based on group data. , 1956, Psychological bulletin.

[34]  G. Loftus,et al.  Linear theory, dimensional theory, and the face-inversion effect. , 2004, Psychological review.

[35]  A. Wills,et al.  On the adequacy of current empirical evaluations of formal models of categorization. , 2012, Psychological bulletin.

[36]  E. Wagenmakers,et al.  AIC model selection using Akaike weights , 2004, Psychonomic bulletin & review.

[37]  A. Wills,et al.  Feedback can be superior to observational training for both rule-based and information-integration category structures , 2015, Quarterly journal of experimental psychology.

[38]  D. Bamber State-trace analysis: A method of testing simple theories of causation , 1979 .

[39]  M. Kalish,et al.  More Is Generally Better: Higher Working Memory Capacity Does Not Impair Perceptual Category Learning , 2017, Journal of experimental psychology. Learning, memory, and cognition.

[40]  I. J. Myung,et al.  Applying Occam’s razor in modeling cognition: A Bayesian approach , 1997 .

[41]  A. Wills,et al.  Initial training with difficult items does not facilitate category learning , 2019, Quarterly journal of experimental psychology.

[42]  Safa R. Zaki,et al.  Exemplar and prototype models revisited: response strategies, selective attention, and stimulus generalization. , 2002, Journal of experimental psychology. Learning, memory, and cognition.

[43]  F Gregory Ashby,et al.  Initial Training With Difficult Items Facilitates Information Integration, but Not Rule-Based Category Learning , 2008, Psychological science.

[44]  Gregory Ashby,et al.  Suboptimality in human categorization and identification. , 2001, Journal of experimental psychology. General.

[45]  Kenneth J. Kurtz,et al.  Human Category Learning: Toward a Broader Explanatory Account , 2015 .

[46]  R. Nosofsky,et al.  Feedback interference and dissociations of classification: Evidence against the multiple-learning-systems hypothesis , 2007, Memory & cognition.

[47]  W Todd Maddox,et al.  Category number impacts rule-based but not information-integration category learning: further evidence for dissociable category-learning systems. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[48]  J. D. Smith,et al.  The time course of explicit and implicit categorization , 2015, Attention, Perception, & Psychophysics.

[49]  W. T. Maddox,et al.  Delayed feedback disrupts the procedural-learning system but not the hypothesis-testing system in perceptual category learning. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[50]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[51]  Corey J. Bohil,et al.  Delayed feedback effects on rule-based and information-integration category learning. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[52]  Gregory Ashby,et al.  Decision rules in the perception and categorization of multidimensional stimuli. , 1988, Journal of experimental psychology. Learning, memory, and cognition.

[53]  M. Kalish,et al.  Systems of Category Learning: Fact or Fantasy? , 2011 .

[54]  Corey J. Bohil,et al.  Evidence for a procedural-learning-based system in perceptual category learning , 2004, Psychonomic bulletin & review.

[55]  David L. Faigman,et al.  Human category learning. , 2005, Annual review of psychology.

[56]  A. Kappers,et al.  Tactile perception of thermal diffusivity , 2009, Attention, perception & psychophysics.

[57]  Mark A. Pitt,et al.  Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example , 2008, Cogn. Sci..

[58]  F. Ashby Is state-trace analysis an appropriate tool for assessing the number of cognitive systems? , 2014, Psychonomic bulletin & review.

[59]  Gregory Ashby,et al.  A neuropsychological theory of multiple systems in category learning. , 1998, Psychological review.

[60]  R. Nosofsky,et al.  Identifying strategy use in category learning tasks: a case for more diagnostic data and models. , 2015, Journal of experimental psychology. Learning, memory, and cognition.

[61]  F Gregory Ashby,et al.  Analogical transfer in perceptual categorization , 2011, Memory & Cognition.

[62]  Fabian A. Soto,et al.  Multidimensional Signal Detection Theory , 2015, Cognitive Choice Modeling.

[63]  M. Lee,et al.  Modeling individual differences in cognition , 2005, Psychonomic bulletin & review.

[64]  M. Sidman A note on functional relations obtained from group data. , 1952, Psychological bulletin.

[65]  Sébastien Hélie,et al.  Trial-by-trial identification of categorization strategy using iterative decision-bound modeling , 2017, Behavior research methods.

[66]  W Todd Maddox,et al.  Removing the Frontal Lobes , 2010, Psychological science.

[67]  Corey J Bohil,et al.  Observational versus feedback training in rule-based and information-integration category learning , 2002, Memory & cognition.

[68]  Safa R. Zaki,et al.  Procedural interference in perceptual classification: Implicit learning or cognitive complexity? , 2005, Memory & cognition.