2014
DOI: 10.1016/j.jml.2014.06.007
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Neural systems underlying perceptual adjustment to non-standard speech tokens

Abstract: It has long been noted that listeners use top-down information from context to guide perception of speech sounds. A recent line of work employing a phenomenon termed ‘perceptual learning for speech’ shows that listeners use top-down information to not only resolve the identity of perceptually ambiguous speech sounds, but also to adjust perceptual boundaries in subsequent processing of speech from the same talker. Even so, the neural mechanisms that underlie this process are not well understood. Of particular i… Show more

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Cited by 32 publications
(50 citation statements)
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“…Collectively, the results from experiment 1 indicate that the lexical information provided during the exposure phase resulted in comprehensive perceptual learning; learning was reflected not only with a change in the mapping of acoustic variants to the /S/ and /s/ categories, in line with previous studies (i.e., Myers and Mesite, 2014), but also by a reorganization of exemplar space within the /S/ category.…”
Section: Resultssupporting
confidence: 84%
See 1 more Smart Citation
“…Collectively, the results from experiment 1 indicate that the lexical information provided during the exposure phase resulted in comprehensive perceptual learning; learning was reflected not only with a change in the mapping of acoustic variants to the /S/ and /s/ categories, in line with previous studies (i.e., Myers and Mesite, 2014), but also by a reorganization of exemplar space within the /S/ category.…”
Section: Resultssupporting
confidence: 84%
“…The stimulus set is the same as that used in Myers and Mesite (2014), to which the reader is referred for a complete description of the methods for stimulus construction. In brief, the exposure stimuli consisted of 100 auditory words and 100 auditory nonwords produced by a native female speaker of American English.…”
Section: Methodsmentioning
confidence: 99%
“…In our final analysis, we further tested whether the same neural source in the temporal lobe was modulated by prior knowledge and perceptual learning or whether these two effects originated from spatially distinct neural sources (e.g., in the STG versus the middle temporal gyrus) (25,26). We did so by using a more constrained method of source reconstruction (42) in which the center of neural activity in a local cortical patch was modeled as a single focal source (an equivalent current dipole, ECD) with a "soft" Bayesian prior for locations in bilateral STG.…”
Section: Resultsmentioning
confidence: 99%
“…Thus, this account makes two key experimental predictions, that (i) prior knowledge and perceptual learning should affect neural responses in the same brain network and (ii) the effect of prior knowledge observed online during perception should predict the magnitude of subsequent perceptual learning. However, because the brain systems supporting the influences of prior knowledge and perceptual learning typically have been observed separately (3,4,(21)(22)(23)(24)(25)(26), neither of these predictions has been tested successfully before.In this study, we obtained concurrent high-density EEG and magnetoencephalographic (MEG) recordings to compare the Significance Experience-dependent changes in sensory processing are critical for successful perception in dynamic and noisy environments. However, the neural and computational mechanisms supporting such changes have remained elusive.…”
mentioning
confidence: 99%
“…In an fMRI study of Brought to you by | New York University Authenticated Download Date | 4/23/17 2:54 AM perceptual learning for speech, neural sensitivity to the shift in category boundary conditioned by the perceptual learning for speech paradigm was observed in right frontal regions throughout the learning trajectory (Myers & Mesite, 2014). However, both anterior and posterior left temporal regions showed sensitivity to the category shift that only emerged after several blocks of exposure.…”
Section: Perceptual Adaptation To Idiolectmentioning
confidence: 98%