To solve these problems, we created a Bayesian model and recorded electroencephalography (EEG) to both study trial-by-trial responses and account for inter-individual differences. Being general by nature, they do not incorporate individual parameters, and are thus unable to account for inter-individual differences 17, 18. Furthermore, computational models used to study trial-by-trial responses 13– 16 were developed to uncover the general principles of SL. Unfortunately, these alternative explanations make similar predictions on the performances averaged over multiple trials. However, it is not clear whether these differences arise from an improved ability to learn sequence statistics - and if so, at which level of complexity - or from an improved ability to process sensory information. For example, musicians have a greater neural sensitivity to the frequency of occurrence of items than non-musicians 7, they are better at segmenting words from an artificial language stream 8, they have a greater neural sensitivity to 1 st order Markov probability 9 and to more complex statistics 10– 12. Musicians have better performances than non-musicians in tasks that implicate different levels of SL complexity. The concept of Markov chain order defines an ordering of SL, from simple to complex as K increases. P(⚪|⚫⚪), is known as K th order Markov probability. Similarly, the probability of an item given the preceding K items, e.g. P(⚪|⚫), is known as 1 st order Markov probability. The probability of occurrence of an item given the preceding one, e.g. In reference to discrete Markov chain analysis, those statistics reflect different orders of Markov chains. ![]() the frequency of ⚫, P(⚫), in ⚫⚫⚪⚫⚫⚫⚪) or to the transition probability between items ( e.g. In this definition, statistics is employed in a broad sense, and can refer for example to the frequency of occurrence of individual items ( e.g. Having improved SL abilities would allow musicians to make more accurate inferences about future events, thus improving perception 2, decision-making 3, and language acquisition 4– 6. The ability to learn sequence statistics is referred to as “statistical learning” (SL). Although temporal regularities can be represented at different levels of details 1, the simplest representation of the sequence is the knowledge about its summary statistics. Musical training provides a rich temporal structure, which is thought to improve the ability to detect and use temporal regularities. Overall, our results prove that musical expertise is associated with improved neural SL, and support music-based intervention to fine tune general cognitive abilities. Finally, early EEG components correlate with the Bayesian model surprise elicited by simple statistics, as opposed to late EEG components that correlate with Bayesian model surprise elicited by complex statistics surprise, and so more strongly for musicians than non-musicians. EEG recordings reveal a neural underpinning of the musician’s advantage: the P300 amplitude correlates with the Bayesian model surprise elicited by each item, and so, more strongly for musicians than non-musicians. This higher performance is explained in the Bayesian model by parameters governing SL, as opposed to parameters governing sensory information processing. Our results confirm that musicians perform ~15% better than non-musicians at predicting items in auditory sequences that embed either simple or complex statistics. ![]() ![]() To solve this controversy, we developed a Bayesian model and recorded electroencephalography (EEG) to study trial-by-trial responses. Unfortunately, these very different explanations make similar predictions on the performances averaged over multiple trials. However, these better performances could be due to an improved ability to process sensory information, as opposed to an improved ability to learn sequence statistics. ![]() In standard SL paradigms, musicians have better performances than non-musicians. It is poorly known whether musical training leads to improvements in general cognitive abilities, such as statistical learning (SL).
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