Abstract
Learning, attention and action play a crucial role in determining how stimulus predictions are formed, stored, and updated. Years-long experience with the specific repertoires of sounds of one or more musical styles is what characterizes professional musicians. Here we contrasted active experience with sounds, namely long-lasting motor practice, theoretical study and engaged listening to the acoustic features characterizing a musical style of choice in professional musicians with mainly passive experience of sounds in laypersons. We hypothesized that long-term active experience of sounds would influence the neural predictions of the stylistic features in professional musicians in a distinct way from the mainly passive experience of sounds in laypersons. Participants with different musical backgrounds were recruited: professional jazz and classical musicians, amateur musicians and non-musicians. They were presented with a musical multi-feature paradigm eliciting mismatch negativity (MMN), a prediction error signal to changes in six sound features for only 12 minutes of electroencephalography (EEG) and magnetoencephalography (MEG) recordings. We observed a generally larger MMN amplitudes–indicative of stronger automatic neural signals to violated priors–in jazz musicians (but not in classical musicians) as compared to non-musicians and amateurs. The specific MMN enhancements were found for spectral features (timbre, pitch, slide) and sound intensity. In participants who were not musicians, the higher preference for jazz music was associated with reduced MMN to pitch slide (a feature common in jazz music style). Our results suggest that long-lasting, active experience of a musical style is associated with accurate neural priors for the sound features of the preferred style, in contrast to passive listening.
Highlights
Our ability to learn relies on sustained, active engagement with the sensory stimulation utilized for predicting future events and reducing errors
The shortest latency was found to the location deviant (p < 0.0001) and the mismatch negativity (MMN) with the longest latency was elicited by pitch deviant (p < 0.0001) (Table 3)
This result was confirmed even with only few years of musical experience since we found a positive correlation between years of training and slide MMN amplitudes in amateur musicians and nonmusicians
Summary
Our ability to learn relies on sustained, active engagement with the sensory stimulation utilized for predicting future events and reducing errors. Attenuated N1 cortical responses to repeated sounds have been linked to the online updating of neural predictions [3], whereas the change-related neural response called mismatch negativity (MMN) have been proposed to index an error signal in the predictive coding of the auditory environment (a mismatch between incoming sensory information and prediction) automatically elicited in the auditory cortex [1,4,5,6,7,8] This MMN error signal indexes the process of updating prior predictions according to sensory feedback, leading to auditory learning [9]
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