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- W3172995384 abstract "Abstract Perception relies on being able to segregate stimuli from different objects and causes to perform inference and further processing. For simple binary stimuli (e.g. auditory and visual) we have models of how the human brain can perform such causal inference, but the complexity of the models increases dramatically with more than 2 stimuli. To characterize human perception with more complex stimuli we have developed a Bayesian inference model that allows an unlimited number of stimulus 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. As a test application we show that such a model can explain several phenomena in the auditory stream perception literature, that it gives an excellent fit to experimental data, and that it makes novel predictions that we confirm experimentally. These results have implications not just for human auditory temporal perception but for a large range of other perceptual phenomena." @default.
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- W3172995384 date "2021-05-29" @default.
- W3172995384 modified "2023-09-23" @default.
- W3172995384 title "Perceptual clustering in auditory streaming" @default.
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- W3172995384 doi "https://doi.org/10.1101/2021.05.27.446050" @default.
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