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J Cogn Neurosci. 2011 Aug;23(8):2079-101. doi: 10.1162/jocn.2010.21570. Epub 2010 Sep 2.

Cortical networks representing object categories and high-level attributes of familiar real-world action sounds.

Author information

1
Department of Physiology and Pharmacology, PO Box 9229, West Virginia University, Morgantown, WV 26506, USA. jlewis@hsc.wvu.edu

Abstract

In contrast to visual object processing, relatively little is known about how the human brain processes everyday real-world sounds, transforming highly complex acoustic signals into representations of meaningful events or auditory objects. We recently reported a fourfold cortical dissociation for representing action (nonvocalization) sounds correctly categorized as having been produced by human, animal, mechanical, or environmental sources. However, it was unclear how consistent those network representations were across individuals, given potential differences between each participant's degree of familiarity with the studied sounds. Moreover, it was unclear what, if any, auditory perceptual attributes might further distinguish the four conceptual sound-source categories, potentially revealing what might drive the cortical network organization for representing acoustic knowledge. Here, we used functional magnetic resonance imaging to test participants before and after extensive listening experience with action sounds, and tested for cortices that might be sensitive to each of three different high-level perceptual attributes relating to how a listener associates or interacts with the sound source. These included the sound's perceived concreteness, effectuality (ability to be affected by the listener), and spatial scale. Despite some variation of networks for environmental sounds, our results verified the stability of a fourfold dissociation of category-specific networks for real-world action sounds both before and after familiarity training. Additionally, we identified cortical regions parametrically modulated by each of the three high-level perceptual sound attributes. We propose that these attributes contribute to the network-level encoding of category-specific acoustic knowledge representations.

PMID:
20812786
DOI:
10.1162/jocn.2010.21570
[Indexed for MEDLINE]

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