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Poster E78, Thursday, August 22, 2019, 3:45 – 5:30 pm, Restaurant Hall

Behavioral and Neural Correlates of Phonetic Plasticity

Christopher Heffner1, Charles Davis1, Emily Myers1;1University of Connecticut

Introduction. Speech understanding requires constant adjustment. This can be seen most radically when encountering new speech sound tokens in a second language, where categorizing non-native sounds requires learning. Yet adjustment to phonetic categories can be observed even within a native language, where tolerating variation between talkers in speech rate or in accent requires adaptation. Though non-native and native speech perception share similarities, it is unclear whether they rely on a common cognitive and neural substrate. In the present study, we explore individual differences in each process in behavior and brain structure. If learning and adaptation share common mechanisms, good non-native phonetic learners should also be good phonetic adapters to native-language speech, with a shared reliance on similar neural architecture. This should include auditory regions, frontal (Myers, 2014) language areas, and the basal ganglia (Lim et al., 2014), as well as connections among these three regions. Methods. We recruited 80 healthy English native-speaking young adults for a battery of tests of phonetic plasticity as well as general cognitive function. Learning Tasks. Participants learned to categorize new phonetic categories either incidentally (Gabay et al., 2015) or explicitly (Heffner et al., 2019). In incidental learning, participants were told to attend to a primary task of hunting zombies, which, unbeknownst to the learner, showed up in locations that could be predicted by sound categories; in explicit learning, they were given explicit instruction and feedback in the categories they were learning. Adaptation Tasks. Participants heard English sentences that were either rate-compressed (rate adaptation) or spoken by a native speaker of Italian with noise in the background (accent adaptation). After the sentence finished, they had to choose between three pairs of keywords that they heard within the sentence. The amount of rate compression or background noise the participants were able to tolerate was used as an index of adaptation. Other Cognitive Measures. Participants also completed a variety of tasks that were not speech-specific, including tests of executive function, inhibitory control, working memory, attention, episodic memory, vocabulary, and speech perception in noise. MRI Measures. All participants underwent MRI scanning, giving us structural, resting-state, and diffusion-weighted scans for each participant. Results. There were interrelationships in performance on three of our measures of phonetic plasticity—explicit learning, accent adaptation, and rate adaptation. Better learners of non-native tokens in explicit tasks were also better adapters. The fourth task, incidental learning, did not correlate as cleanly with the other three tasks. Correlations with the other cognitive measures were generally weak apart from positive relationships with vocabulary size and speech-in-noise ability. ROI-based analysis of brain structure showed relationships between the speech learning and adaptation measures and the properties of regions including inferior frontal cortex, Heschl’s gyrus, and insula. For example, participants showing better learning and adaptation had more gyrification in bilateral Heschl’s gyrus. Planned analyses include a whole-brain structural analysis as well as connectivity measures for both resting-state functional and diffusion-weighted measures, focusing on connections between frontal and temporal language areas and the basal ganglia.

Themes: Speech Perception, Perception: Auditory
Method: Other

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