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Detecting and representing predictable structure during auditory scene analysis
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AI
DOI：10.7554/eLife.19113.png)
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Abstract
En 中文
We use psychophysics and MEG to test how sensitivity to input statistics facilitates auditory-scene-analysis (ASA). Human subjects listened to 'scenes' comprised of concurrent tone pip streams (sources). On occasional trials a new source appeared partway. Listeners were more accurate and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather than random (RAND), sources. MEG in passive listeners and those actively detecting appearance events revealed increased sustained activity in auditory and parietal cortex in REG relative to RAND scenes, emerging 400 ms of scene-onset. Over and above this, appearance in REG scenes was associated with increased responses relative to RAND scenes. The effect of temporal structure on appearance-evoked responses was delayed when listeners were focused on the scenes relative to when listening passively, consistent with the notion that attention reduces 'surprise'. Overall, the results implicate a mechanism that tracks predictability of multiple concurrent sources to facilitate active and passive ASA.
Keywords:
FIGURE-GROUND SEGREGATION
EVOKED-POTENTIALS
TEMPORAL PREDICTABILITY
REPETITION SUPPRESSION
UNDERLYING MECHANISMS
SOURCE RECONSTRUCTION
NEURAL DYNAMICS
VISUAL-CORTEX
MEG-DATA
ATTENTION