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Poster A92 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
Discovering the perceptual space of natural sounds from similarity judgments
Jarrod M Hicks1 (), Bryan J Medina1, Josh H McDermott1,2; 1Massachusetts Institute of Technology, 2Harvard Univeristy
Perceptual similarity is critical to many aspects of perception and cognition, but is poorly characterized for realistic stimuli. We examined the perceptual space of natural sounds using a similarity judgment task applied to large numbers of natural sound textures. Participants judged the similarity of sound textures using an odd-one-out task. We then fit a linear transform to best predict human similarity judgments from a set of candidate representations taken from contemporary auditory models (trained convolutional neural networks or a standard auditory texture model). We found that the learned linear transformations were critical to predicting the human judgments, and that intermediate-to-late stages of the trained neural networks yielded the highest prediction accuracy of human judgments. Surprisingly, only a few dimensions were required to reach peak prediction accuracy. This result suggests that when comparing randomly chosen natural sounds, human similarity is dominated by a small number of dimensions. This general result could constrain memory errors, category formation, and other cognitive phenomena that are dependent on similarity.
Keywords: sound similarity auditory textures computational modeling deep neural networks