Functional Plasticity Coupled With Structural Predispositions in Auditory Cortex Shape Successful Music Category Learning

Abstract

Categorizing sounds into meaningful groups helps listeners more efficiently process the auditory scene and is a foundational skill for speech perception and language development. Yet, how auditory categories develop in the brain through learning, particularly for non-speech sounds (e.g., music), is not well understood. Here, we asked musically naïve listeners to complete a brief (∼20 min) training session where they learned to identify sounds from a musical interval continuum (minor-major 3rds). We used multichannel EEG to track behaviorally relevant neuroplastic changes in the auditory event-related potentials (ERPs) pre- to post-training. To rule out mere exposure-induced changes, neural effects were evaluated against a control group of 14 non-musicians who did not undergo training. We also compared individual categorization performance with structural volumetrics of bilateral Heschl’s gyrus (HG) from MRI to evaluate neuroanatomical substrates of learning. Behavioral performance revealed steeper (i.e., more categorical) identification functions in the posttest that correlated with better training accuracy. At the neural level, improvement in learners’ behavioral identification was characterized by smaller P2 amplitudes at posttest, particularly over right hemisphere. Critically, learning-related changes in the ERPs were not observed in control listeners, ruling out mere exposure effects. Learners also showed smaller and thinner HG bilaterally, indicating superior categorization was associated with structural differences in primary auditory brain regions. Collectively, our data suggest successful auditory categorical learning of music sounds is characterized by short-term functional changes (i.e., greater post-training efficiency) in sensory coding processes superimposed on preexisting structural differences in bilateral auditory cortex.

Short- and long-term experience-dependent neuroplasticity interact during the perceptual learning of concurrent speech

“…A rightward biased mechanism in musicians is additionally supported by their decreased RH P2 magnitude with training. Declines in P2 strength with learning is consistent with other singlesession, short-term learning experiments in which sensory-evoked neural responses become more efficient during active task engagement (Guenther et al 2004;Alain et al 2010;Ben-David et al 2011;Pérez-Gay Juárez et al 2019;Mankel et al 2022). Stronger engagement of RH may also indicate increased attention to frequency-related information (Crowley and Colrain 2004).…”

“…This suggests that the long-term auditory experience of musicianship might act as a catalyst for novel sound learning that would be highly relevant in other domains (e.g., second language learning; Slevc and Miyake 2006;Chobert and Besson 2013;Picciotti et al 2018). We argue the early nature of these effects in waves that localize to auditory-perceptual areas (and well before motor responses) suggests the rapid plasticity we observe with auditory learning can be attributed to changes in sensory encoding rather than later procedural learning (Alain et al 2007;Mankel et al 2022). Indeed, neither task familiarity (i.e., procedural learning) nor stimulus repetition alone are sufficient to produce changes in the early cortical ERPs (Alain et al 2007;Mankel et al 2022).…”

“…Given musicians' faster overall behavioral speed in our task, the double-dissociation in hemispheric latencies between groups may point to superior "cue-weighting" by musicians towards pitchrelated cues (Zatorre et al 1992). Relationships between behavioral decision speeds and right hemispheric learning patterns for musicians could reflect their focus on pitch interval features (i.e., "musical" content) between the two vowels, whereas nonmusicians' left hemispheric pattern may reflect heavier reliance on linguistic information (Mankel et al 2022). Though our task relies on segregation and identification of speech tokens, musicians' stronger learningrelated changes in right hemisphere may indicate a focus on pitch rather than linguistic information of the speech stimuli (Alain et al 2005), indicating distinct task strategies as a result of long-term experience in music.…”

“…The analysis window was guided by visual inspection of the grand averaged data. We focus on the P2 as we have previously shown this neural index is sensitive to long-term plasticity of musicianship (Bidelman et al 2014;Mankel et al 2020), success in double-vowel identification (Bidelman and Yellamsetty 2017), and tracks with perceptual learning during auditory categorization tasks (Mankel et al 2022; see also Alain et al, 2007).…”

Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed using Convolutional Neural Network

“…Although speech categorization is dominant in left hemisphere, studies have also shown involvement of right hemispheric regions during CP tasks [15]. For instance, right hemisphere is engaged in processing lexical tones [16, 17, 18], acoustic information such as pitch levels [19] and processing of non-speech stimuli such as musical tones [20, 21]. So, it is important to decode the association of right hemispheric regions and RT to fully understand the neural basis of RT variation in CP.…”