Joint population coding and temporal coherence link an attended talker’s voice and location features in naturalistic multi-talker scenes

Author:

van der Heijden KikiORCID,Patel Prachi,Bickel StephanORCID,Herrero Jose L.ORCID,Mehta Ashesh D.ORCID,Mesgarani NimaORCID

Abstract

ABSTRACTListeners readily extract multi-dimensional auditory objects such as a ‘localized talker’ from complex acoustic scenes with multiple talkers. Yet, the neural mechanisms underlying simultaneous encoding and linking of different sound features – for example, a talker’s voice and location – are poorly understood. We analyzed invasive intracranial recordings in neurosurgical patients attending to a localized talker in real-life cocktail party scenarios. We found that sensitivity to an individual talker’s voice and location features was distributed throughout auditory cortex and that neural sites exhibited a gradient from sensitivity to a single feature to joint sensitivity to both features. On a population level, cortical response patterns of both dual-feature sensitive sites but also single-feature sensitive sites revealed simultaneous encoding of an attended talker’s voice and location features. However, for single-feature sensitive sites, the representation of the primary feature was more precise. Further, sites which selective tracked an attended speech stream concurrently encoded an attended talker’s voice and location features, indicating that such sites combine selective tracking of an attended auditory object with encoding of the object’s features. Finally, we found that attending a localized talker selectively enhanced temporal coherence between single-feature voice sensitive sites and single-feature location sensitive sites, providing an additional mechanism for linking voice and location in multi-talker scenes. These results demonstrate that a talker’s voice and location features are linked during multi-dimensional object formation in naturalistic multi-talker scenes by joint population coding as well as by temporal coherence between neural sites.SIGNIFICANCE STATEMENTListeners effortlessly extract auditory objects from complex acoustic scenes consisting of multiple sound sources in naturalistic, spatial sound scenes. Yet, how the brain links different sound features to form a multi-dimensional auditory object is poorly understood. We investigated how neural responses encode and integrate an attended talker’s voice and location features in spatial multi-talker sound scenes to elucidate which neural mechanisms underlie simultaneous encoding and linking of different auditory features. Our results show that joint population coding as well as temporal coherence mechanisms contribute to distributed multi-dimensional auditory object encoding. These findings shed new light on cortical functional specialization and multidimensional auditory object formation in complex, naturalistic listening scenes.HIGHLIGHTSCortical responses to an single talker exhibit a distributed gradient, ranging from sites that are sensitive to both a talker’s voice and location (dual-feature sensitive sites) to sites that are sensitive to either voice or location (single-feature sensitive sites).Population response patterns of dual-feature sensitive sites encode voice and location features of the attended talker in multi-talker scenes jointly and with equal precision.Despite their sensitivity to a single feature at the level of individual cortical sites, population response patterns of single-feature sensitive sites also encode location and voice features of a talker jointly, but with higher precision for the feature they are primarily sensitive to.Neural sites which selectively track an attended speech stream concurrently encode the attended talker’s voice and location features.Attention selectively enhances temporal coherence between voice and location selective sites over time.Joint population coding as well as temporal coherence mechanisms underlie distributed multi-dimensional auditory object encoding in auditory cortex.

Publisher

Cold Spring Harbor Laboratory

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