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Communication Dans Un Congrès Année : 2022

Bayesian gates: a probabilistic modeling tool for temporal segmentation of sensory streams into sequences of perceptual accumulators

Résumé

To explain how perception processes are performed, understanding how continuous sensory streams are temporally segmented into discrete units is central. This is particularly the case in speech perception where temporal segmentation is key for identifying linguistic units contained between consecutive events in time. We propose an original probabilistic construct, that we call "Bayesian gates", to segment temporally continuous streams of sensory stimuli into sequences of decoders. We first define Bayesian gates mathematically and describe their properties. We then illustrate their behavior in the context of a model of word recognition in speech perception. We show that, based on an event detection module, they sequentially parse the acoustic stimulus, so that each syllable decoder only processes a segment of the sensory signal.
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hal-03747788 , version 1 (08-08-2022)

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  • HAL Id : hal-03747788 , version 1

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Mamady Nabé, Jean-Luc Schwartz, Julien Diard. Bayesian gates: a probabilistic modeling tool for temporal segmentation of sensory streams into sequences of perceptual accumulators. CogSci 2022 - 44th Annual Conference of the Cognitive Science Society, Jul 2022, Toronto, Canada. ⟨hal-03747788⟩
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