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RESEARCH PRODUCT

Acquiring music information : An incidental learning approach.

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This thesis contains my empirical works resulting from three years of studying contingency learning, that is the human ability to learn regularities between two or more events, applied to music. Learning music requires time and effort. However, many skills can be automatized in less time-consuming and effortful ways. Indeed, some research suggests that many elements of music knowledge are mostly implicitly acquired. In Chapter 1, the potential benefit of using an incidental learning procedure to automatize musical sub-skills useful for sight-reading and for pitch identification is discussed. In Chapter 2, the first set of experiments investigate whether an incidental contingency learning task can be used to easily and rapidly teach nonmusicians the associations between note positions on the musical staff and their note names (i.e., sight-reading). As hypothesized, robust improvements in sight-reading performance were observed. In Chapter 3, a second set of experiments first explored whether an incidental learning procedure can be used to help nonmusicians to acquire pitch-label associations. As hypothesized, robust learning was observed. Improvements in pitch identification performance were also observed for musicians. Finally, the experiments in Chapter 4 studied the relationship between incidental learning and time perception in acquiring pitch-label associations. In particular, the experiments investigated whether temporal predictability aids in the acquisition of contingencies relative to temporal unpredictability. Extending previous research, the evidence gathered from these studies suggested that contingency learning is not strongly influenced by time perception. Chapter 5 discusses the results of this thesis more broadly, with particular focus on how the present results open up new venues for practical implementations of incidental learning procedures in the music education domain. The relationship between time perception and contingency learning is further discussed.