Literature DB >> 29457054

Towards a whole brain model of Perceptual Learning.

Marcello Maniglia1, Aaron R Seitz1.   

Abstract

A hallmark of modern Perceptual Learning (PL) is the extent to which learning is specific to the trained stimuli. Such specificity to orientation, spatial location and even eye of training has been used as psychophysical evidence of the neural basis of learning. This argument that specificity of PL implies regionalization of brain plasticity implicitly assumes that examination of a singular locus of PL is an appropriate approach to understand learning. However, recent research shows that learning effects once thought to be specific depend on subtleties of the training paradigm and that within even a simple training procedure there are multiple aspects of the task and stimuli that are learned simultaneously. Here, we suggest that learning on any task involves a broad network of brain regions undergoing changes in representations, read-out weights, decision rules, attention and feedback processes as well as oculomotor changes. However, importantly, the distribution of learning across the neural system depends upon the details of the training procedure and the characterstics of the individual being trained. We propose that to advance our understanding of PL, the field must move towards understanding how distributed brain processes jointly contribute to behavioral learning effects.

Entities:  

Year:  2017        PMID: 29457054      PMCID: PMC5810967          DOI: 10.1016/j.cobeha.2017.10.004

Source DB:  PubMed          Journal:  Curr Opin Behav Sci        ISSN: 2352-1546


  93 in total

1.  Learning strengthens the response of primary visual cortex to simple patterns.

Authors:  Christopher S Furmanski; Denis Schluppeck; Stephen A Engel
Journal:  Curr Biol       Date:  2004-04-06       Impact factor: 10.834

2.  Perceptual learning improves neural processing in myopic vision.

Authors:  Fang-Fang Yan; Jiawei Zhou; Wuxiao Zhao; Min Li; Jie Xi; Zhong-Lin Lu; Chang-Bing Huang
Journal:  J Vis       Date:  2015       Impact factor: 2.240

3.  Differences in perceptual learning transfer as a function of training task.

Authors:  C Shawn Green; Florian Kattner; Max H Siegel; Daniel Kersten; Paul R Schrater
Journal:  J Vis       Date:  2015       Impact factor: 2.240

4.  Prolonged training at threshold promotes robust retinotopic specificity in perceptual learning.

Authors:  Shao-Chin Hung; Aaron R Seitz
Journal:  J Neurosci       Date:  2014-06-18       Impact factor: 6.167

5.  Function and structure of human left fusiform cortex are closely associated with perceptual learning of faces.

Authors:  Taiyong Bi; Juan Chen; Tiangang Zhou; Yong He; Fang Fang
Journal:  Curr Biol       Date:  2014-01-09       Impact factor: 10.834

6.  Physiological correlates of perceptual learning in monkey V1 and V2.

Authors:  Geoffrey M Ghose; Tianming Yang; John H R Maunsell
Journal:  J Neurophysiol       Date:  2002-04       Impact factor: 2.714

7.  Exogenous Attention Enables Perceptual Learning.

Authors:  Sarit F A Szpiro; Marisa Carrasco
Journal:  Psychol Sci       Date:  2015-10-26

8.  Perceptual learning of basic visual features remains task specific with Training-Plus-Exposure (TPE) training.

Authors:  Lin-Juan Cong; Ru-Jie Wang; Cong Yu; Jun-Yun Zhang
Journal:  J Vis       Date:  2016       Impact factor: 2.240

9.  The effect of aging on crowded letter recognition in the peripheral visual field.

Authors:  Andrew T Astle; Alan J Blighe; Ben S Webb; Paul V McGraw
Journal:  Invest Ophthalmol Vis Sci       Date:  2014-07-01       Impact factor: 4.799

10.  The effect of normal aging and age-related macular degeneration on perceptual learning.

Authors:  Andrew T Astle; Alan J Blighe; Ben S Webb; Paul V McGraw
Journal:  J Vis       Date:  2015       Impact factor: 2.240

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  32 in total

1.  Learning efficient visual search for stimuli containing diagnostic spatial configurations and color-shape conjunctions.

Authors:  Eric A Reavis; Sebastian M Frank; Peter U Tse
Journal:  Atten Percept Psychophys       Date:  2018-07       Impact factor: 2.199

2.  Deep Neural Networks for Modeling Visual Perceptual Learning.

Authors:  Li K Wenliang; Aaron R Seitz
Journal:  J Neurosci       Date:  2018-05-23       Impact factor: 6.167

3.  Functional MRI and EEG Index Complementary Attentional Modulations.

Authors:  Sirawaj Itthipuripat; Thomas C Sprague; John T Serences
Journal:  J Neurosci       Date:  2019-05-24       Impact factor: 6.167

4.  Individual difference predictors of learning and generalization in perceptual learning.

Authors:  Gillian Dale; Aaron Cochrane; C Shawn Green
Journal:  Atten Percept Psychophys       Date:  2021-03-15       Impact factor: 2.199

5.  General learning ability in perceptual learning.

Authors:  Jia Yang; Fang-Fang Yan; Lijun Chen; Jie Xi; Shuhan Fan; Pan Zhang; Zhong-Lin Lu; Chang-Bing Huang
Journal:  Proc Natl Acad Sci U S A       Date:  2020-07-23       Impact factor: 11.205

6.  Evaluating environmental and inhibitory control strategies to improve outcomes in a widely available weight loss program.

Authors:  Nenette A Cáceres; Qihan Yu; Jessica Capaldi; Márcio Augusto Diniz; Hollie Raynor; Gary D Foster; Aaron R Seitz; Sarah-Jeanne Salvy
Journal:  Contemp Clin Trials       Date:  2022-07-05       Impact factor: 2.261

7.  Endogenous spatial attention during perceptual learning facilitates location transfer.

Authors:  Ian Donovan; Marisa Carrasco
Journal:  J Vis       Date:  2018-10-01       Impact factor: 2.240

8.  Individuals with autism spectrum disorder have altered visual encoding capacity.

Authors:  Jean-Paul Noel; Ling-Qi Zhang; Alan A Stocker; Dora E Angelaki
Journal:  PLoS Biol       Date:  2021-05-12       Impact factor: 8.029

9.  Sensory-Induced Human LTP-Like Synaptic Plasticity - Using Visual Evoked Potentials to Explore the Relation Between LTP-Like Synaptic Plasticity and Visual Perceptual Learning.

Authors:  Lilly Lengali; Johannes Hippe; Christoffer Hatlestad-Hall; Trine Waage Rygvold; Markus Handal Sneve; Stein Andersson
Journal:  Front Hum Neurosci       Date:  2021-06-25       Impact factor: 3.169

10.  Feature-based attention enables robust, long-lasting location transfer in human perceptual learning.

Authors:  Shao-Chin Hung; Marisa Carrasco
Journal:  Sci Rep       Date:  2021-07-06       Impact factor: 4.379

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