Inherent auditory skills rather than formal music training shape the neural encoding of speech

Abstract

Musical training is associated with a myriad of neuroplastic changes in the brain, including more robust and efficient neural processing of clean and degraded speech signals at brainstem and cortical levels. These assumptions stem largely from cross-sectional studies between musicians and nonmusicians which cannot address whether training itself is sufficient to induce physiological changes or whether preexisting superiority in auditory function before training predisposes individuals to pursue musical interests and appear to have similar neuroplastic benefits as musicians. Here, we recorded neuroelectric brain activity to clear and noise-degraded speech sounds in individuals without formal music training but who differed in their receptive musical perceptual abilities as assessed objectively via the Profile of Music Perception Skills. We found that listeners with naturally more adept listening skills ("musical sleepers") had enhanced frequency-following responses to speech that were also more resilient to the detrimental effects of noise, consistent with the increased fidelity of speech encoding and speech-in-noise benefits observed previously in highly trained musicians. Further comparisons between these musical sleepers and actual trained musicians suggested that experience provides an additional boost to the neural encoding and perception of speech. Collectively, our findings suggest that the auditory neuroplasticity of music engagement likely involves a layering of both preexisting (nature) and experience-driven (nurture) factors in complex sound processing. In the absence of formal training, individuals with intrinsically proficient auditory systems can exhibit musician-like auditory function that can be further shaped in an experience-dependent manner.

Keywords: EEG; auditory event-related brain potentials; experience-dependent plasticity; frequency-following responses; nature vs. nurture.

Fig. 1.

PROMS scores reveal that some listeners have highly adept (musician-like) auditory skills despite having no formal music training. Listeners (all nonmusicians) were divided into high- and low-musicality groups based on a median split of scores on the full PROMS battery (dashed vertical line). (Inset) Mean PROMS scores across groups. Error bars = ±1 SEM; ***P < 0.001.

Fig. 2.

Speech-evoked FFRs reveal neural enhancements in musical ears. FFR waveforms and spectra in the clean (A and B) and noisy (C and D) speech conditions reflecting phase-locked neural activity to the spectrotemporal characteristics of speech. (E) FFR F0 amplitudes. Data from actual trained musicians (40) are shown for comparison. Highly musical listeners exhibited stronger encoding of speech at F0 (voice pitch) and its integer multiple harmonics (timbre) for degraded speech than less musical individuals. Formally trained musicians (40) still exhibit larger FFRs than nonmusicians, regardless of the nonmusicians’ inherent musicality (musician data were not available for noise). (F) FFR latency is earlier in high vs. low PROMS scorers. (G) GLME regression relating brain and behavioral measures (aggregating all clean/noise responses; n = 56). Individuals with higher levels of intrinsic neural noise are less musical (i.e., have lower PROMS scores). The solid line shows the regression fit; dotted lines indicate the 95% CI interval. Error bars = ±1 SEM; *P < 0.05, **P < 0.01, n.s. = nonsignificant.

Fig. 3.

Cortical speech-evoked responses are modulated by noise but not listeners’ inherent auditory skills (musicality). (A and B) ERP waveforms for clean (A) and noise-degraded (B) speech. (C) N1–P2 amplitudes and latencies (SI Appendix, Fig. S1) indicate noise-related changes in neural activity but no differences between musicality groups. Musician data shown for comparison are from ref. . Trained musicians’ N1–P2 amplitudes differ from low PROMS scorers but are similar to those of musical sleepers (high-scoring PROMS group). (D and E) Relationships between brainstem (FFR) and cortical (ERP) measures for noise-degraded speech (n = 28 responses). (D) Larger P1 responses at the cortical level are associated with larger FFR F0 amplitudes. (E) Faster brainstem FFRs are associated with smaller N1–P2 responses, as seen in high PROMS scorers and trained musicians (compare with C). Error bars = ±1 SEM; *P < 0.05, ***P < 0.001.