Literature DB >> 23421513

When does fading enhance perceptual category learning?

Harold Pashler1, Michael C Mozer.   

Abstract

Training that uses exaggerated versions of a stimulus discrimination (fading) has sometimes been found to enhance category learning, mostly in studies involving animals and impaired populations. However, little is known about whether and when fading facilitates learning for typical individuals. This issue was explored in 7 experiments. In Experiments 1 and 2, observers discriminated stimuli based on a single sensory continuum (time duration and line length, respectively). Adaptive fading dramatically improved performance in training (unsurprisingly) but did not enhance learning as assessed in a final test. The same was true for nonadaptive linear fading (Experiment 3). However, when variation in length (predicting category membership) was embedded among other (category-irrelevant) variation, fading dramatically enhanced not only performance in training but also learning as assessed in a final test (Experiments 4 and 5). Fading also helped learners to acquire a color saturation discrimination amid category-irrelevant variation in hue and brightness, although this learning proved transitory after feedback was withdrawn (Experiment 7). Theoretical implications are discussed, and we argue that fading should have practical utility in naturalistic category learning tasks, which involve extremely high dimensional stimuli and many irrelevant dimensions. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Mesh:

Year:  2013        PMID: 23421513     DOI: 10.1037/a0031679

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


  12 in total

1.  Category-Induced Transfer of Visual Perceptual Learning.

Authors:  Qingleng Tan; Zhiyan Wang; Yuka Sasaki; Takeo Watanabe
Journal:  Curr Biol       Date:  2019-03-28       Impact factor: 10.834

2.  Improved Classification of Mammograms Following Idealized Training.

Authors:  Adam N Hornsby; Bradley C Love
Journal:  J Appl Res Mem Cogn       Date:  2014-06-01

3.  Easy-to-hard effects in perceptual learning depend upon the degree to which initial trials are "easy".

Authors:  Matthew G Wisniewski; Barbara A Church; Eduardo Mercado; Milen L Radell; Alexandria C Zakrzewski
Journal:  Psychon Bull Rev       Date:  2019-12

4.  Predicting favorable and unfavorable consequences of perceptual learning: worsening and the peak shift.

Authors:  Matthew G Wisniewski
Journal:  Exp Brain Res       Date:  2017-02-11       Impact factor: 1.972

Review 5.  Model-guided search for optimal natural-science-category training exemplars: A work in progress.

Authors:  Robert M Nosofsky; Craig A Sanders; Xiaojin Zhu; Mark A McDaniel
Journal:  Psychon Bull Rev       Date:  2019-02

6.  A practical guide for studying human behavior in the lab.

Authors:  Joao Barbosa; Heike Stein; Sam Zorowitz; Yael Niv; Christopher Summerfield; Salvador Soto-Faraco; Alexandre Hyafil
Journal:  Behav Res Methods       Date:  2022-03-09

7.  Linear separability, irrelevant variability, and categorization difficulty.

Authors:  Luke A Rosedahl; F Gregory Ashby
Journal:  J Exp Psychol Learn Mem Cogn       Date:  2021-04-19       Impact factor: 3.140

8.  How experimental trial context affects perceptual categorization.

Authors:  Thomas J Palmeri; Michael L Mack
Journal:  Front Psychol       Date:  2015-02-19

9.  The easy-to-hard training advantage with real-world medical images.

Authors:  Brett D Roads; Buyun Xu; June K Robinson; James W Tanaka
Journal:  Cogn Res Princ Implic       Date:  2018-10-03

10.  Benefits of fading in perceptual learning are driven by more than dimensional attention.

Authors:  Matthew G Wisniewski; Milen L Radell; Barbara A Church; Eduardo Mercado
Journal:  PLoS One       Date:  2017-07-19       Impact factor: 3.240

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