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A non-parametric Bayesian prior for causal inference of auditory streaming

Yates, Tim; Larigaldie, Nathanael; Beierholm, Ulrik

A non-parametric Bayesian prior for causal inference of auditory streaming Thumbnail


Authors

Tim Yates

Nathanael Larigaldie



Contributors

G. Gunzelmann
Editor

A. Howes
Editor

T. Tenbrink
Editor

E. J. Davelaar
Editor

Abstract

traditionally been modeled using a mechanistic approach. The problem however is essentially one of source inference – a problem that has recently been tackled using statistical Bayesian models in visual and auditory-visual modalities. Usually the models are restricted to performing inference over just one or two possible sources, but human perceptual systems have to deal with much more complex scenarios. To characterize human perception we have developed a Bayesian inference model that allows an unlimited number of signal sources to be considered: it is general enough to allow any discrete sequential cues, from any modality. The model uses a non-parametric prior, hence increased complexity of the signal does not necessitate more parameters. The model not only determines the most likely number of sources, but also specifies the source that each signal is associated with. The model gives an excellent fit to data from an auditory stream segregation experiment in which the pitch and presentation rate of pure tones determined the perceived number of sources.

Citation

Yates, T., Larigaldie, N., & Beierholm, U. (2017). A non-parametric Bayesian prior for causal inference of auditory streaming. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. . J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (1381-1386). https://doi.org/10.1101/139188

Conference Name Annual Conference of the Cognitive Science Society
Conference Location London, UK
Acceptance Date Apr 11, 2017
Publication Date Jan 1, 2017
Deposit Date Aug 25, 2017
Publicly Available Date Apr 24, 2018
Pages 1381-1386
Book Title Proceedings of the 39th Annual Conference of the Cognitive Science Society.
DOI https://doi.org/10.1101/139188
Public URL https://durham-repository.worktribe.com/output/1146523
Related Public URLs https://www.biorxiv.org/content/early/2017/05/17/139188

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