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

- Tim Yates,
*University of Birmingham*
- Nathanael Larigaldie,
*Durham University*
- Ulrik Beierholm,
*Durham University*

## Abstract

Human perceptual grouping of sequential auditory cues has
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 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

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