| Paper: | PS-2B.66 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Explaining Human Auditory Scene Analysis Through Bayesian Clustering | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1227-0 | ||
| Authors: | Nathanael Larigaldie, Ulrik Beierholm, Durham University, United Kingdom | ||
| Abstract: | The way auditory stimuli are being processed to form perceptual unitary or segregated groups of sounds is still an ongoing discussion in the Auditory Scene Analysis literature. Mechanistic approaches to model this phenomenon have been somewhat successful but are often overly complicated and constrained to specific paradigms. Our approach is that of simplicity. We have previously proposed a higher-level source inference model in the Bayesian statistical framework that only implements a few simple but sensible rules applied to the stimuli’s statistics. Yet, it still captures results from behavioral data (Yates, Larigaldie, & Beierholm, 2017). We have expanded on this model to show its ability to adapt to a wider range of well-known perceptual auditory phenomena. Several original experiments have also been conducted to explore a broader range of stimuli statistics. Our model’s responses give insight into possible underlying processes in the brain that could provide a guide towards more behavioral experiments or medical exploration. | ||