Faster sound stream segmentation in musicians than in nonmusicians

Abstract

The musician's brain is considered as a good model of brain plasticity as musical training is known to modify auditory perception and related cortical organization. Here, we show that music-related modifications can also extend beyond motor and auditory processing and generalize (transfer) to speech processing. Previous studies have shown that adults and newborns can segment a continuous stream of linguistic and non-linguistic stimuli based only on probabilities of occurrence between adjacent syllables, tones or timbres. The paradigm classically used in these studies consists of a passive exposure phase followed by a testing phase. By using both behavioural and electrophysiological measures, we recently showed that adult musicians and musically trained children outperform nonmusicians in the test following brief exposure to an artificial sung language. However, the behavioural test does not allow for studying the learning process per se but rather the result of the learning. In the present study, we analyze the electrophysiological learning curves that are the ongoing brain dynamics recorded as the learning is taking place. While musicians show an inverted U shaped learning curve, nonmusicians show a linear learning curve. Analyses of Event-Related Potentials (ERPs) allow for a greater understanding of how and when musical training can improve speech segmentation. These results bring evidence of enhanced neural sensitivity to statistical regularities in musicians and support the hypothesis of positive transfer of training effect from music to sound stream segmentation in general.

Figure 1. Illustration of the experimental design used in the present experiment.

Stimuli were presented auditorily via loudspeakers. The learning phase lasted 5.5 minutes and the order of the tests was counter balanced across participants.

Figure 2. Percentage of correct responses.

Group performance in the linguistic (green) and musical tests (blue) for musicians (left) and nonmusicians (right). The error bars represents +/− Standard Error.

Figure 3. Grand average (Fronto-Central region) across musicians (A, left) and nonmusicians (B, right) recorded during each time bin of the exposure phase.

(black = 1st time bin, red = 2nd time bin, green  = 3rd time bin, blue  = 4th time bin). (C) Map showing the distribution of the N400 component (350–550 ms latency band, averaged across time bins and groups).

Figure 4. N400 mean amplitude (350–550 ms) averaged across 6 fronto-central electrodes in both groups of participants (musicians in blue, nonmusicians in red) and in the four time bins (1'20'') from the exposure phase.

Negativity is up. Error bars refer to confidence intervals computed as described in and take into account inter-subject variability, separately for each group.

Figure 5. Scatter plot of accuracy in the behavioural test versus the time bin showing the maximum N400 amplitude.

Regression index and the p value are provided on the plot. Musicians are represented in blue and nonmusicians in red.