Literature DB >> 22799282

Human learning of elemental category structures: revising the classic result of Shepard, Hovland, and Jenkins (1961).

Kenneth J Kurtz1, Kimery R Levering, Roger D Stanton, Joshua Romero, Steven N Morris.   

Abstract

The findings of Shepard, Hovland, and Jenkins (1961) on the relative ease of learning 6 elemental types of 2-way classifications have been deeply influential 2 times over: 1st, as a rebuke to pure stimulus generalization accounts, and again as the leading benchmark for evaluating formal models of human category learning. The litmus test for models is the ability to simulate an observed advantage in learning a category structure based on an exclusive-or (XOR) rule over 2 relevant dimensions (Type II) relative to category structures that have no perfectly predictive cue or cue combination (including the linearly-separable Type IV). However, a review of the literature reveals that a Type II advantage over Type IV is found only under highly specific experimental conditions. We investigate when and why a Type II advantage exists to determine the appropriate benchmark for models and the psychological theories they represent. A series of 8 experiments link particular conditions of learning to outcomes ranging from a traditional Type II advantage to compelling non-differences and reversals (i.e., Type IV advantage). Common interpretations of the Type II advantage as either a broad-based phenomenon of human learning or as strong evidence for an attention-mediated similarity-based account are called into question by our findings. Finally, a role for verbalization in the category learning process is supported. (c) 2013 APA, all rights reserved.

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Mesh:

Year:  2012        PMID: 22799282     DOI: 10.1037/a0029178

Source DB:  PubMed          Journal:  J Exp Psychol Learn Mem Cogn        ISSN: 0278-7393            Impact factor:   3.051


  12 in total

1.  Revisiting the linear separability constraint: New implications for theories of human category learning.

Authors:  Kimery R Levering; Nolan Conaway; Kenneth J Kurtz
Journal:  Mem Cognit       Date:  2020-04

2.  Combining error-driven models of associative learning with evidence accumulation models of decision-making.

Authors:  David K Sewell; Hayley K Jach; Russell J Boag; Christina A Van Heer
Journal:  Psychon Bull Rev       Date:  2019-06

3.  Similar to the category, but not the exemplars: A study of generalization.

Authors:  Nolan Conaway; Kenneth J Kurtz
Journal:  Psychon Bull Rev       Date:  2017-08

4.  A dimensional summation account of polymorphous category learning.

Authors:  Andy J Wills; Lyn Ellett; Fraser Milton; Gareth Croft; Tom Beesley
Journal:  Learn Behav       Date:  2020-03       Impact factor: 1.986

5.  On the learning difficulty of visual and auditory modal concepts: Evidence for a single processing system.

Authors:  Ronaldo Vigo; Karina-Mikayla C Doan; Charles A Doan; Shannon Pinegar
Journal:  Cogn Process       Date:  2017-10-26

6.  Classification errors and response times over multiple distributed sessions as a function of category structure.

Authors:  Derek E Zeigler; Ronaldo Vigo
Journal:  Mem Cognit       Date:  2018-10

7.  Pigeon category learning: Revisiting the Shepard, Hovland, and Jenkins (1961) tasks.

Authors:  Victor M Navarro; Ridhi Jani; Edward A Wasserman
Journal:  J Exp Psychol Anim Learn Cogn       Date:  2019-03-14       Impact factor: 2.478

8.  REFRESH: A new approach to modeling dimensional biases in perceptual similarity and categorization.

Authors:  Adam N Sanborn; Katherine Heller; Joseph L Austerweil; Nick Chater
Journal:  Psychol Rev       Date:  2021-09-13       Impact factor: 8.934

9.  Concurrent Dynamics of Category Learning and Metacognitive Judgments.

Authors:  Valnea Žauhar; Igor Bajšanski; Dražen Domijan
Journal:  Front Psychol       Date:  2016-09-27

10.  Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research.

Authors:  Matthew J C Crump; John V McDonnell; Todd M Gureckis
Journal:  PLoS One       Date:  2013-03-13       Impact factor: 3.240

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