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Publications [#291346] of Marty G. Woldorff

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Journal Articles

  1. Gamble, ML; Woldorff, MG (2015). Rapid Context-based Identification of Target Sounds in an Auditory Scene.. Journal of Cognitive Neuroscience, 27(9), 1675-1684. [doi]
    (last updated on 2019/04/24)

    To make sense of our dynamic and complex auditory environment, we must be able to parse the sensory input into usable parts and pick out relevant sounds from all the potentially distracting auditory information. Although it is unclear exactly how we accomplish this difficult task, Gamble and Woldorff [Gamble, M. L., & Woldorff, M. G. The temporal cascade of neural processes underlying target detection and attentional processing during auditory search. Cerebral Cortex (New York, N.Y.: 1991), 2014] recently reported an ERP study of an auditory target-search task in a temporally and spatially distributed, rapidly presented, auditory scene. They reported an early, differential, bilateral activation (beginning at 60 msec) between feature-deviating target stimuli and physically equivalent feature-deviating nontargets, reflecting a rapid target detection process. This was followed shortly later (at 130 msec) by the lateralized N2ac ERP activation, that reflects the focusing of auditory spatial attention toward the target sound and parallels the attentional-shifting processes widely studied in vision. Here we directly examined the early, bilateral, target-selective effect to better understand its nature and functional role. Participants listened to midline-presented sounds that included target and nontarget stimuli that were randomly either embedded in a brief rapid stream or presented alone. The results indicate that this early bilateral effect results from a template for the target that utilizes its feature deviancy within a stream to enable rapid identification. Moreover, individual-differences analysis showed that the size of this effect was larger for participants with faster RTs. The findings support the hypothesis that our auditory attentional systems can implement and utilize a context-based relational template for a target sound, making use of additional auditory information in the environment when needing to rapidly detect a relevant sound.

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