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BrainAGE as a predictor of phonetic perception
Poster Session C, Saturday, September 13, 11:00 am - 12:30 pm, Field House
This poster is part of the Sandbox Series.
Portia Washington1, Emily Myers1,2; 1Department of Psychological Sciences, University of Connecticut, 2Department of Speech, Language, and Hearing Sciences, University of Connecticut
Behaviorally, individuals differ in the extent to which they learn, encode, and represent speech sounds. In recent evidence, differences in fine-grained phonetic perception predict individual differences in other language outcomes. Several studies report no effect of age on tasks that assess sensitivity to phonetic category structure suggesting that aspects of phonetic perception are resilient to the effects of aging (Myers et al., 2024). However, a number of biological and environmental factors also shape the development and trajectory of changes in brain structure in adulthood, and these changes do not track smoothly with chronological age. Considering that differences in brain structure and measures associated with brain aging predict phonetic expertise, we asked whether age-related changes in brain structure reflect differences in phonetic perception. We collected a structural MRI from 60 adults ages 18-78, along with phonetic decision data, and a battery of cognitive and linguistic assessments. Participants in this sample were recruited in five equal age-bands. This sample allows us to analyze differences in the behavior of adults across the lifespan as opposed to a dichotomous view of younger and older adults. The Visual Analog Scale (VAS) phonetic decision task provides insight into individual differences in phonetic skill. Listeners rated tokens on vowel and VOT continua spanning canonical endpoints and ambiguous tokens. Several measures of phonetic perception were derived from this task using a four-parameter logistic regression to extract slope and consistency estimates for each participant. These measures reflect the extent to which individuals retain within-category representations, and how consistent their representations are of specific tokens. We quantified individual differences in brain structure using the BrainAGE algorithm. BrainAGE is a supervised learning algorithm that uses T1 images to calculate an estimate of “global” brain health by comparing grey and white matter volume, and cortical thickness to T1 images of other individuals across the lifespan. A positive “BrainAGE gap”, or the difference between an individual’s estimated BrainAGE and chronological age, suggests that the brain is aging faster than anticipated and has been associated with both subtle and clinical levels of cognitive decline and exposure to environmental stressors (Kalc et al., 2024, Nemati et al., 2024, Gotlib et al., 2023). In this project we discuss a) how VAS outcomes relate to other measures of cognitive-linguistic fitness across the lifespan, and b) if participants’ overall brain “health” predicts individual differences in phonetic perception. We hypothesize that there will be few differences between the behavior of younger and older adults, but that BrainAGE may relate to other aspects of an individual’s development that predict subtle differences in their phonetic acuity. Preliminary evidence shows a weak negative relationship between overall VAS consistency and BrainAGE gap suggesting that individuals that show evidence of decelerated aging have more consistent phonetic representations. These results, together with further analyses quantifying regional differences in BrainAGE, contribute to a better understanding of the ways that differences in signatures of neural aging impact phonetic perception.
Topic Areas: Speech Perception,