The ability to learn new perceptual categories enables some of the most complex human behaviors, from speech perception to visual object recognition. Current understanding of the mechanisms involved in perceptual category learning relies on the fundamental assumption that the processes underlying such learning are shared across the senses. However, the vast majority of this work has focused on the visual modality. As a consequence, the research regarding how humans learn to group complex auditory information into categories has relied greatly on conclusions from the research in the visual domain without testing this critical assumption. However, recent evidence from the attention literature suggests that even seemingly domain-general cognitive processes, such as working memory, are accomplished via sensory-biased regions in frontal cortex. The current investigation will directly compare the computational and neural mechanisms supporting auditory and visual category learning by training the same individuals on categories in both modalities while in an fMRI scanner.
Aim #1 of this investigation will identify the shared and sensory-biased circuits supporting feedback processing during auditory and visual category learning. If the neural circuits supporting perceptual category learning are shared across the modalities, it is expected that similar regions will be recruited to a similar extent during feedback processing. If instead, the neural circuits are distinct for particular modalities, it is expected that sensory-biased regions will emerge as supporting category learning for auditory and visual modalities.
Aim #2 will utilize advanced machine learning techniques (multivariate pattern classification and representational similarity analyses) to characterize the emergence of category-level neural representations over the course of learning.
Aim #3 will identify the functional and structural connectivity of the circuits as they contribute to perceptual category learning. The proposed research will directly test the fundamental assumption about the nature of this complex problem that affects everyday behaviors. This research has the potential to impact understanding of cases where modality- specific learning abilities might be impaired, such as phonetic learning and language-related impairments in dyslexia, autism, and specific language impairment. The proposed research will provide the training foundation to support the PI?s long-term objective of developing theories of perceptual category learning that are constrained by neurobiology and behavior and will specify the behavioral, computational, and neural mechanisms of such learning. This project presents the opportunity to directly test a critical assumption underlying understanding of perceptual category learning. The proposed research will take place in an exceptional training environment and the PI will be mentored by a team of knowledgeable and accomplished scientists. The research will provide the PI with training in functional magnetic resonance experiment design and analysis which will prepare her well for a career as an independent scientist in computational cognitive neuroscience.

Public Health Relevance

The proposed research will contribute to fundamental knowledge about how seemingly general-purpose cognitive systems may demonstrate modality specificity. The goal of this investigation is to characterize the differences in cognitive processing during category learning when the information comes from the auditory or visual modalities. The findings from this work may inform mechanistic approaches to understanding modality- specific deficits in language-based disorders, such as dyslexia, autism, and specific language impairment.

National Institute of Health (NIH)
National Institute on Deafness and Other Communication Disorders (NIDCD)
Postdoctoral Individual National Research Service Award (F32)
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Special Emphasis Panel (ZDC1)
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Rivera-Rentas, Alberto L
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University of Pittsburgh
United States
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