Volume 5, 2021
Topical Issue - Auditory models: from binaural processing to multimodal cognition
|Number of page(s)||14|
|Published online||24 December 2021|
Decision making in auditory externalization perception: model predictions for static conditions
* Corresponding author: email@example.com
Accepted: 29 November 2021
Under natural conditions, listeners perceptually attribute sounds to external objects in their environment. This core function of perceptual inference is often distorted when sounds are produced via hearing devices such as headphones or hearing aids, resulting in sources being perceived unrealistically close or even inside the head. Psychoacoustic studies suggest a mixed role of various monaural and interaural cues contributing to the externalization process. We developed a model framework for perceptual externalization able to probe the contribution of cue-specific expectation errors and to contrast dynamic versus static strategies for combining those errors within static listening environments. Effects of reverberation and visual information were not considered. The model was applied to various acoustic distortions as tested under various spatially static conditions in five previous experiments. Most accurate predictions were obtained for the combination of monaural and interaural spectral cues with a fixed relative weighting (approximately 60% of monaural and 40% of interaural). That model version was able to reproduce the externalization rating of the five experiments with an average error of 12% (relative to the full rating scale). Further, our results suggest that auditory externalization in spatially static listening situations underlies a fixed weighting of monaural and interaural spectral cues, rather than a dynamic selection of those auditory cues.
Key words: Computational modeling / Distal attribution / Perceptual decision making / Predictive processing / Sound externalization / Spatial hearing
© R. Baumgartner & P. Majdak, Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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