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How learning to abstract shapes neural sound representations.

The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound feature... Full description

Journal Title: Frontiers in neuroscience 2014, Vol.8, p.132
Main Author: Ley, Anke
Other Authors: Vroomen, Jean , Formisano, Elia
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 1662-4548 ; DOI: 10.3389/fnins.2014.00132
Link: http://search.proquest.com/docview/1540107275/?pq-origsite=primo
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recordid: proquest1540107275
title: How learning to abstract shapes neural sound representations.
format: Article
creator:
  • Ley, Anke
  • Vroomen, Jean
  • Formisano, Elia
subjects:
  • Mvpa
  • Auditory Perception
  • Learning
  • Perceptual Categorization
  • Plasticity
ispartof: Frontiers in neuroscience, 2014, Vol.8, p.132
description: The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.
language: eng
source:
identifier: ISSN: 1662-4548 ; DOI: 10.3389/fnins.2014.00132
fulltext: fulltext
issn:
  • 16624548
  • 1662-4548
url: Link


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identifierISSN: 1662-4548 ; DOI: 10.3389/fnins.2014.00132
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descriptionThe transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes-even in absence of changes in overall signal level-these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations.
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