Vocalization categorization behavior explained by a feature-based auditory categorization model

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously...

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Bibliographic Details
Published in:	eLife Vol. 11
Main Authors:	Kar, Manaswini, Pernia, Marianny, Williams, Kayla, Parida, Satyabrata, Schneider, Nathan Alan, McAndrew, Madelyn, Kumbam, Isha, Sadagopan, Srivatsun
Format:	Journal Article
Language:	English
Published:	England eLife Sciences Publications Ltd 13-10-2022 eLife Sciences Publications, Ltd
Subjects:	Acoustic Stimulation Animals auditory cortex Auditory Cortex - physiology Auditory Perception - physiology Behavior Behavior, Animal - physiology categorization Communication Cortex (auditory) Cortex (temporal) guinea pig Guinea Pigs Neuroscience Sound vocalization Vocalization behavior Vocalization, Animal - physiology guinea pig auditory cortex categorization behavior vocalization neuroscience
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Summary:	Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs (GPs) on call categorization tasks using natural calls. We then tested categorization by the model and GPs using temporally and spectrally altered calls. Both the model and GPs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, the model predicted about 50% of the variance in GP behavior. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work. These authors also contributed equally to this work.
ISSN:	2050-084X 2050-084X
DOI:	10.7554/eLife.78278