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Level-weighted averaging in elevation to synchronous amplitude-modulated sounds

To program a goal-directed response in the presence of multiple sounds, the audiomotor system should separate the sound sources. The authors examined whether the brain can segregate synchronous broadband sounds in the midsagittal plane, using amplitude modulations as an acoustic discrimination cue....

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Detalles Bibliográficos
Autores principales: Van Bentum, Guus C., Van Opstal, A. John, Van Aartrijk, Chaline M. M., Van Wanrooij, Marc M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6147220/
https://www.ncbi.nlm.nih.gov/pubmed/29195479
http://dx.doi.org/10.1121/1.5011182
Descripción
Sumario:To program a goal-directed response in the presence of multiple sounds, the audiomotor system should separate the sound sources. The authors examined whether the brain can segregate synchronous broadband sounds in the midsagittal plane, using amplitude modulations as an acoustic discrimination cue. To succeed in this task, the brain has to use pinna-induced spectral-shape cues and temporal envelope information. The authors tested spatial segregation performance in the midsagittal plane in two paradigms in which human listeners were required to localize, or distinguish, a target amplitude-modulated broadband sound when a non-modulated broadband distractor was played simultaneously at another location. The level difference between the amplitude-modulated and distractor stimuli was systematically varied, as well as the modulation frequency of the target sound. The authors found that participants were unable to segregate, or localize, the synchronous sounds. Instead, they invariably responded toward a level-weighted average of both sound locations, irrespective of the modulation frequency. An increased variance in the response distributions for double sounds of equal level was also observed, which cannot be accounted for by a segregation model, or by a probabilistic averaging model.