Memory for exemplars in category learning

Some argue that category learning is mediated by two competing learning systems: one explicit, one implicit (Ashby et al., 1998). These systems are hypothesised to be responsible for learning rule-based and information-integration category structures respectively. However, little experimental work has directly investigated whether people are conscious of category knowledge supposedly learned by the implicit system. Here we report one experiment that directly compared explicit recognition memory for exemplars between these two category structures. Contrary to the predictions of the dual-systems approach, we found preliminary evidence of superior exemplar memory after information-integration category learning compared to rule-based learning. This result is consistent with the hypothesis that participants learn information-integration category structures by using complex rules.

[1]  E. Pothos,et al.  Category structure and the two learning systems of COVIS , 2011, The European journal of neuroscience.

[2]  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 .

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

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

[5]  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.

[6]  R M Nosofsky,et al.  Recognition memory for exceptions to the category rule. , 1995, Journal of experimental psychology. Learning, memory, and cognition.

[7]  Dave F. Kleinschmidt,et al.  Procedural memory effects in categorization: Evidence for multiple systems or task complexity? , 2013, Memory & Cognition.

[8]  W. T. Maddox,et al.  Neural correlates of rule-based and information-integration visual category learning. , 2006, Cerebral cortex.

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

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

[11]  Yasuaki Sakamoto,et al.  Schematic influences on category learning and recognition memory. , 2004, Journal of experimental psychology. General.

[12]  Debra A. Fleischman,et al.  Double Dissociation Between Memory Systems Underlying Explicit and Implicit Memory in the Human Brain , 1995 .

[13]  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.

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

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

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

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

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

[19]  Emmanuel M. Pothos,et al.  Formal Approaches in Categorization: Contents , 2011 .

[20]  W. Todd Maddox,et al.  Rule-based category learning in patients with Parkinson's disease , 2009, Neuropsychologia.

[21]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

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