Time as a supervisor: temporal regularity and auditory object learning


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Sensory systems appear to learn to transform incoming sensory information into perceptual representations that can inform and guide behavior, or objects, with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background, and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than a conventional feature-selection algorithm, i.e., principal component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes, and that the auditory brain should utilize these slowly changing temporal features. ### Competing Interest Statement The authors have declared no competing interest.
computational neuroscience, audition, perception, theoretical neuroscience, vocalization acoustic structure
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