Award Abstract # 2120834
CompCog: Computational Models of Plasticity and Learning in Speech Perception

NSF Org: BCS
Division of Behavioral and Cognitive Sciences
Recipient: UNIVERSITY OF MARYLAND, COLLEGE PARK
Initial Amendment Date: July 28, 2021
Latest Amendment Date: May 11, 2023
Award Number: 2120834
Award Instrument: Standard Grant
Program Manager: Betty Tuller
btuller@nsf.gov
 (703)292-7238
BCS
 Division of Behavioral and Cognitive Sciences
SBE
 Directorate for Social, Behavioral and Economic Sciences
Start Date: August 1, 2021
End Date: July 31, 2026 (Estimated)
Total Intended Award Amount: $496,805.00
Total Awarded Amount to Date: $508,931.00
Funds Obligated to Date: FY 2021 = $496,805.00
FY 2023 = $12,126.00
History of Investigator:
  • Naomi Feldman (Principal Investigator)
    nhf@umd.edu
Recipient Sponsored Research Office: University of Maryland, College Park
3112 LEE BUILDING
COLLEGE PARK
MD  US  20742-5100
(301)405-6269
Sponsor Congressional District: 04
Primary Place of Performance: University of Maryland, College Park
3112 LEE BLDG 7809 Regents Drive
College Park
MD  US  20742-1800
Primary Place of Performance
Congressional District:
04
Unique Entity Identifier (UEI): NPU8ULVAAS23
Parent UEI: NPU8ULVAAS23
NSF Program(s): Linguistics,
Perception, Action & Cognition,
Robust Intelligence
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7252, 7495, 9251, 1311
Program Element Code(s): 131100, 725200, 749500
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.075

ABSTRACT

When it comes to speech perception, listeners are lifelong learners. Although infants? perception becomes tuned to their native language in their first year of life, their speech sound categories continue to change well into childhood and adolescence. Adults also continue to show substantial capacity for perceptual learning, particularly in settings that involve feedback or rewards. This project uses computational modeling to investigate the learning mechanisms that allow listeners to adapt their speech perception to particular languages and environments. By building theories of auditory perceptual learning, the project will contribute to our understanding of the difficulties that adults face when learning another language. It could also provide a framework for understanding the difficulties faced by certain populations, such as children with cochlear implants, when learning their first language and may facilitate future development of treatments or interventions for these populations.

Two types of computational models are developed based on adult perceptual learning data: probabilistic cue weighting models, which are designed to capture fast, trial-by-trial changes in listeners? reliance on different parts of the speech signal, and reinforcement learning models, which are designed to capture longer term, implicit perceptual learning of speech sounds that occurs in response to a reward, such as points in a video game. The models are tested on their ability to capture adults? perceptual learning behavior in experimental settings. A second series of simulations then explores whether and how these models that were developed on adult data can predict aspects of children?s perceptual learning of speech sound categories in laboratory discrimination tasks that involve rewards, such as exciting toys, and in naturalistic settings where the speech is more complex and there is a less obvious reward structure. Results from the project are expected to provide insight into what types of speech representations children and adults have at different stages of development, as well as which perceptual learning strategies learners rely on at different ages and in different learning environments.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Brown, Grace C. and Feldman, Naomi H. "Linking cognitive and neural models of audiovisual processing to explore speech perception in autism" Proceedings of the Annual Conference of the Cognitive Science Society , 2024 Citation Details
Famularo, Ruolan Leslie and Aboelata, Ali and Schatz, Thomas and Feldman, Naomi H. "Language discrimination may not rely on rhythm: A computational study" Proceedings of the Annual Conference of the Cognitive Science Society , 2024 Citation Details
Jurov, Nika and Idsardi, William and Feldman, Naomi H. "A neural architecture for selective attention to speech features" Proceedings of Interspeech , 2023 Citation Details
Jurov, Nika and Wolf, Grayson and Idsardi, William and Feldman, Naomi H. "Speech features are weighted by selective attention" Proceedings of the Conference on Cognitive Computational Neuroscience , 2023 Citation Details
Li, Ruolan and Schatz, Thomas and Feldman, Naomi H. "Modeling Rhythm in Speech as in Music: Towards a Unified Cognitive Representation" Proceedings of the Conference on Cognitive Computational Neuroscience , 2022 Citation Details
Rodriguez, Joselyn and Sreepada, Kamala and Famularo, Ruolan Leslie and Goldwater, Sharon and Feldman, Naomi H "Self-supervised speech representations display some human-like cross-linguistic perceptual abilities" , 2024 Citation Details
Thorburn, Craig A and Lau, Ellen and Feldman, Naomi H "Exploring the effectiveness of reward-based learning strategies for second-language speech sounds" Psychonomic Bulletin & Review , v.32 , 2025 https://doi.org/10.3758/s13423-024-02541-0 Citation Details
Thorburn, Craig and Lau, Ellen and Feldman, Naomi "A reinforcement learning approach to speech category acquisition" Proceedings of the Annual Boston University Conference on Language Development , 2022 Citation Details

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