Technical Program

Paper Detail

Paper: PS-2A.27
Session: Poster Session 2A
Location: Symphony/Overture
Session Time: Friday, September 7, 17:15 - 19:15
Presentation Time:Friday, September 7, 17:15 - 19:15
Presentation: Poster
Paper Title: Combining Biological and Artificial Approaches to Understand Perceptual Spaces for Categorizing Natural Acoustic Signals
Manuscript:  Click here to view manuscript
Authors: Marvin Thielk, Tim Sainburg, University of California San Diego, United States; Tatyana Sharpee, Salk Institute, United States; Timothy Gentner, University of California San Diego, United States
Abstract: Parametrizing complex natural stimuli is a difficult and long-standing challenge. We used a generative deep convergent network to represent and parametrize a large corpus of song from European starlings, a songbird species, into a compressed low-dimensional space. We applied psychophysical methods to probe categorical perception of natural starling song syllables, which reveal a shared categorical perceptual space. Some categorical boundaries are sensitive to the category assignment of training syllables, indicating that the consensus is context dependent and that underlying dimensions of the space are not independent. Consistent with this, we predict the behavioral psychometric function along one dimension by fitting the behavior for other dimensions to artificial neural network activations. Similar predictions are obtained by fitting spike timings of in-vivo neuronal populations, recorded simultaneously from 10's of neurons in a secondary auditory cortical region. Thus, knowing how the animal responds in one sub-region of the parametrized space informs responses in other sub-regions of both the artificial and in-vivo spaces. Our results implicate the importance of experience in shaping shared perceptual boundaries among complex communication signals, and suggest the categorical representation of natural signals in secondary sensory cortices is distributed much more densely than predicted by traditional hierarchical object recognition models.