Literature DB >> 29681473

Tracing the Trajectory of Sensory Plasticity across Different Stages of Speech Learning in Adulthood.

Rachel Reetzke1, Zilong Xie1, Fernando Llanos1, Bharath Chandrasekaran2.   

Abstract

Although challenging, adults can learn non-native phonetic contrasts with extensive training [1, 2], indicative of perceptual learning beyond an early sensitivity period [3, 4]. Training can alter low-level sensory encoding of newly acquired speech sound patterns [5]; however, the time-course, behavioral relevance, and long-term retention of such sensory plasticity is unclear. Some theories argue that sensory plasticity underlying signal enhancement is immediate and critical to perceptual learning [6, 7]. Others, like the reverse hierarchy theory (RHT), posit a slower time-course for sensory plasticity [8]. RHT proposes that higher-level categorical representations guide immediate, novice learning, while lower-level sensory changes do not emerge until expert stages of learning [9]. We trained 20 English-speaking adults to categorize a non-native phonetic contrast (Mandarin lexical tones) using a criterion-dependent sound-to-category training paradigm. Sensory and perceptual indices were assayed across operationally defined learning phases (novice, experienced, over-trained, and 8-week retention) by measuring the frequency-following response, a neurophonic potential that reflects fidelity of sensory encoding, and the perceptual identification of a tone continuum. Our results demonstrate that while robust changes in sensory encoding and perceptual identification of Mandarin tones emerged with training and were retained, such changes followed different timescales. Sensory changes were evidenced and related to behavioral performance only when participants were over-trained. In contrast, changes in perceptual identification reflecting improvement in categorical percept emerged relatively earlier. Individual differences in perceptual identification, and not sensory encoding, related to faster learning. Our findings support the RHT-sensory plasticity accompanies, rather than drives, expert levels of non-native speech learning.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  auditory; frequency-following response; perceptual identification; perceptual learning; plasticity; reverse hierarchy theory; sensory encoding

Mesh:

Year:  2018        PMID: 29681473      PMCID: PMC5940549          DOI: 10.1016/j.cub.2018.03.026

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  22 in total

1.  Acoustic noise and vision differentially warp the auditory categorization of speech.

Authors:  Gavin M Bidelman; Lauren Sigley; Gwyneth A Lewis
Journal:  J Acoust Soc Am       Date:  2019-07       Impact factor: 1.840

2.  Functional Logistic Mixed-Effects Models for Learning Curves From Longitudinal Binary Data.

Authors:  Giorgio Paulon; Rachel Reetzke; Bharath Chandrasekaran; Abhra Sarkar
Journal:  J Speech Lang Hear Res       Date:  2019-03-25       Impact factor: 2.297

Review 3.  Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses.

Authors:  Zilong Xie; Rachel Reetzke; Bharath Chandrasekaran
Journal:  J Speech Lang Hear Res       Date:  2019-03-25       Impact factor: 2.297

4.  Non-invasive peripheral nerve stimulation selectively enhances speech category learning in adults.

Authors:  Matthew K Leonard; Bharath Chandrasekaran; Fernando Llanos; Jacie R McHaney; William L Schuerman; Han G Yi
Journal:  NPJ Sci Learn       Date:  2020-08-06

5.  Afferent-efferent connectivity between auditory brainstem and cortex accounts for poorer speech-in-noise comprehension in older adults.

Authors:  Gavin M Bidelman; Caitlin N Price; Dawei Shen; Stephen R Arnott; Claude Alain
Journal:  Hear Res       Date:  2019-08-27       Impact factor: 3.208

6.  Biometric identification of listener identity from frequency following responses to speech.

Authors:  Fernando Llanos; Zilong Xie; Bharath Chandrasekaran
Journal:  J Neural Eng       Date:  2019-07-23       Impact factor: 5.379

7.  Brainstem correlates of concurrent speech identification in adverse listening conditions.

Authors:  Anusha Yellamsetty; Gavin M Bidelman
Journal:  Brain Res       Date:  2019-02-20       Impact factor: 3.252

8.  Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions.

Authors:  Rakib Al-Fahad; Mohammed Yeasin; Gavin M Bidelman
Journal:  J Neural Eng       Date:  2020-02-05       Impact factor: 5.379

9.  Brain-behavior relationships in incidental learning of non-native phonetic categories.

Authors:  Sahil Luthra; Pamela Fuhrmeister; Peter J Molfese; Sara Guediche; Sheila E Blumstein; Emily B Myers
Journal:  Brain Lang       Date:  2019-09-12       Impact factor: 2.381

10.  Emerging native-similar neural representations underlie non-native speech category learning success.

Authors:  Gangyi Feng; Yu Li; Shen-Mou Hsu; Patrick C M Wong; Tai-Li Chou; Bharath Chandrasekaran
Journal:  Neurobiol Lang (Camb)       Date:  2021-06-09
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