Here, I examine the music-induced optimal state of flow using two algorithms for causal structure learning: Greedy-Equivalent-Search (GES) and the PC algorithm. Background knowledge on the preconditions of flow is incorporated to optimize the search process. For causal inference, I make use of Pearl’s do-calculus and the Intervention calculus when DAG is Absent algorithm (IDA) from the pcalg R package.
Flow is a pleasurable, optimal state of mind going together with a total immersion in an activity (Chicksentmihalji, 1990). Flow has also drawn attention in music research, recognizing it as being important for optimal musical performance. In this context, Wrigley and Emmerson (2011) examined the subjective experience of flow during music performance by administering the self-report Flow State Scale-2 (FSS-2) to 236 students immediately after their musical performance. This research has led to many more studies in the music domain (Tan & Sin, 2019) but, despite this increase, it is still unclear how the dimensions of flow interact, mutually reinforce each other, and together constitute and maintain the experiential state of flow.
What is the internal structure of flow during music performance?
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