From learning to creativity: Identifying the behavioural and neural correlates of learning to predict human judgements of musical creativity

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  • Ioanna Zioga, Queen Mary University of London
  • ,
  • Peter M.C. Harrison, Queen Mary University of London
  • ,
  • Marcus T. Pearce
  • Joydeep Bhattacharya, University of London
  • ,
  • Caroline Di Bernardi Luft, Queen Mary University of London

Human creativity is intricately linked to acquired knowledge. However, to date learning a new musical style and subsequent musical creativity have largely been studied in isolation. We introduced a novel experimental paradigm combining behavioural, electrophysiological, and computational methods, to examine the neural correlates of unfamiliar music learning, and to investigate how neural and computational measures can predict human creativity. We investigated music learning by training non-musicians (N = 40) on an artificial music grammar. Participants’ knowledge of the grammar was tested before and after three training sessions on separate days by assessing explicit recognition of the notes of the grammar, while additionally recording their EEG. After each training session, participants created their own musical compositions, which were later evaluated by human experts. A computational model of auditory expectation was used to quantify the statistical properties of both the grammar and the compositions. Results showed that participants successfully learned the new grammar. This was also reflected in the N100, P200, and P3a components, which were higher in response to incorrect than correct notes. The delta band (2.5–4.5 Hz) power in response to grammatical notes during first exposure to the grammar positively correlated with learning, suggesting a potential neural mechanism of encoding. On the other hand, better learning was associated with lower alpha and higher beta band power after training, potentially reflecting neural mechanisms of retrieval. Importantly, learning was a significant predictor of creativity, as judged by experts. There was also an inverted U-shaped relationship between percentage of correct intervals and creativity, as compositions with an intermediate proportion of correct intervals were associated with the highest creativity. Finally, the P200 in response to incorrect notes was predictive of creativity, suggesting a link between the neural correlates of learning, and creativity. Overall, our findings shed light on the neural mechanisms of learning an unfamiliar music grammar, and offer novel contributions to the associations between learning measures and creative compositions based on learned materials.

Original languageEnglish
Article number116311
Number of pages19
Publication statusPublished - Feb 2020

    Research areas

  • Artificial music grammar, Creativity, EEG, IDyOM, Statistical learning, Training

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