Literature DB >> 19715714

How much practice is needed to produce perceptual learning?

Zahra Hussain1, Allison B Sekuler, Patrick J Bennett.   

Abstract

We examined the amount of practice needed to improve performance on 10-AFC face- and texture identification tasks. On Day 1, subjects were grouped by amount of practice: a control group had 0 trials of practice, and several experimental groups had practice that ranged from 1 to 40 trials per condition. On Day 2, all groups performed 40 trials per condition of the trained task. The effect of practice was estimated by comparing performance across groups on Day 2. In both tasks, increasing practice was associated with greater learning, but surprisingly small amounts of practice were required to improve performance. In the face identification task, for example, only one trial per condition on Day 1 was required to increase performance relative to the control group at the start of testing on Day 2. In the texture identification task, five trials per condition on Day 1 were required to increase performance relative to the control group. In both tasks, the advantage associated with small amounts of practice declined during the Day 2 session due to larger within-session learning in the control group. Sleep had little to no effect on learning; performance depended primarily on the amount of preceding practice.

Mesh:

Year:  2009        PMID: 19715714     DOI: 10.1016/j.visres.2009.08.022

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  14 in total

1.  The dynamics of practice effects in an optotype acuity task.

Authors:  Sven P Heinrich; Katja Krüger; Michael Bach
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2011-04-21       Impact factor: 3.117

2.  Disruption of Perceptual Learning by a Brief Practice Break.

Authors:  David F Little; Yu-Xuan Zhang; Beverly A Wright
Journal:  Curr Biol       Date:  2017-11-22       Impact factor: 10.834

3.  An expert advantage in detecting unfamiliar visual signals in noise.

Authors:  Zahra Hussain
Journal:  Proc Natl Acad Sci U S A       Date:  2020-09-30       Impact factor: 11.205

4.  Memory reactivation improves visual perception.

Authors:  Rotem Amar-Halpert; Rony Laor-Maayany; Shlomi Nemni; Jonathan D Rosenblatt; Nitzan Censor
Journal:  Nat Neurosci       Date:  2017-08-28       Impact factor: 24.884

5.  Cortical contributions to impaired contour integration in schizophrenia.

Authors:  Steven M Silverstein; Michael P Harms; Cameron S Carter; James M Gold; Brian P Keane; Angus MacDonald; J Daniel Ragland; Deanna M Barch
Journal:  Neuropsychologia       Date:  2015-07-06       Impact factor: 3.139

6.  Learning to identify crowded letters: does the learning depend on the frequency of training?

Authors:  Susana T L Chung; Sandy R Truong
Journal:  Vision Res       Date:  2012-11-30       Impact factor: 1.886

7.  Perceptual learning: functions, mechanisms, and applications.

Authors:  Zhong-Lin Lu; Cong Yu; Takeo Watanabe; Dov Sagi; Dennis Levi
Journal:  Vision Res       Date:  2009-10       Impact factor: 1.886

8.  Self-motion perception training: thresholds improve in the light but not in the dark.

Authors:  Matthias Hartmann; Sarah Furrer; Michael H Herzog; Daniel M Merfeld; Fred W Mast
Journal:  Exp Brain Res       Date:  2013-02-08       Impact factor: 1.972

9.  The rapid emergence of stimulus specific perceptual learning.

Authors:  Zahra Hussain; Paul V McGraw; Allison B Sekuler; Patrick J Bennett
Journal:  Front Psychol       Date:  2012-07-05

10.  Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning.

Authors:  Katharine Molloy; David R Moore; Ediz Sohoglu; Sygal Amitay
Journal:  PLoS One       Date:  2012-05-14       Impact factor: 3.240

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