Detecting changes in dynamic and complex acoustic environments
Abstract
Natural sounds such as wind or rain, are characterized by the statistical occurrence of their constituents. Despite their complexity, listeners readily detect changes in these contexts. We here address the neural basis of statistical decision-making using a combination of psychophysics, EEG and modelling. In a texture-based, change-detection paradigm, human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. The potential's amplitude scaled with the duration of pre-change exposure, suggesting a time-dependent decision threshold. Auditory cortex-related potentials showed no response to the change. A dual timescale, statistical estimation model accounted for subjects' performance. Furthermore, a decision-augmented auditory cortex model accounted for performance and reaction times, suggesting that the primary cortical representation requires little post-processing to enable change-detection in complex acoustic environments.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 06, 2017
- Source ID
- 10.7554/elife.24910
Entities
People
- Bernhard Englitz
- Jennifer Lawlor
- Shihab A Shamma
- Urszula Górska
- Yves Boubenec
Organizations
- Agence Nationale de la Recherche
- Army Research Office
- European Research Council
- Jagiellonian University
- University of Maryland
- École Normale Supérieure