Bayesian Research

Bayesian Research

LINKS:

    ASA Vancouver Poster

    ASA Minneapolis Poster
    Tutorial on Bayesian Approach (pdf)
    Reverend Bayes
    ISBA
    ASA: Bayesian Statistics

A number of auditory tasks, including speech perception, require listeners to categorize stimuli on the basis of one or more features of the input. In many cases, especially speech, there is no one-to-one mapping between values along continuous features and discrete categories (e.g., phonemes). How then do perceptual systems categorize stimuli under uncertainty? One possible solution is to use probabilistic information from experienced stimulus distributions to optimize accuracy.

We propose that perceivers incorporate distributional knowledge about the acoustic environment with the information provided by the signal in order to make optimal (i.e., maximal accuracy) categorical decisions. Statistical approaches such as this are widely used in vision research but are rarely applied to auditory or speech perception. Our goal in this study was to develop a framework that will provide testable hypotheses about the nature of statistical (distributional) learning in auditory perception, in general, and speech perception, specifically.

A number of auditory tasks, including speech perception, require listeners to categorize stimuli on the basis of one or more features of the input. In many cases, especially speech, there is no one-to-one mapping between values along continuous features and discrete categories (e.g., phonemes). How then do perceptual systems categorize stimuli under uncertainty? One possible solution is to use probabilistic information from experienced stimulus distributions to optimize accuracy.

We propose that perceivers incorporate distributional knowledge about the acoustic environment with the information provided by the signal in order to make optimal (i.e., maximal accuracy) categorical decisions. Statistical approaches such as this are widely used in vision research but are rarely applied to auditory or speech perception. Our goal in this study was to develop a framework that will provide testable hypotheses about the nature of statistical (distributional) learning in auditory perception, in general, and speech perception, specifically.