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Abstract Music performance is a complex act requiring a highly coordinated interplay of cognitions, decisions, and movements. To fully understand such performances requires multimodal approaches to capturing, analysing, interpreting, and applying these phenomena, each of which introduces further complexity. Introducing measurement techniques to a performance can interfere with the musicians’ attention, freedom of movement, and artistic process and therefore affect the ecological validity of the experiment, especially in live and pressured settings. Desired performance outcomes are highly individual and subjective, complicating attempts at analysis and generalisation. And the complexity of the data can make it difficult to translate findings back into knowledge that is genuinely useful for the musician. This session will survey research seeking to optimise performance by decoding performance where it happens. It will consider musicians’ attitudes towards and use of technologies in their practice, simulations that allow live performance to be studied in controlled settings, and prototype data visualisations which allow musicians to inform their performance and preparation in real time. Waddell G, Bland R, and Williamon A (forthcoming), Simulating and stimulating performance: developing a next-generation music performance simulator, Frontiers in Psychology. DOI» Waddell G and Williamon A (2024), Enhanced performance training, in C Johnson and A King (eds), Music, Technology, Innovation: Industry and Educational Perspectives (pp. 59-74), Routledge. Ramirez R and Waddell G (2022), Technology-enhanced learning of performance, in GE McPherson (ed), Oxford Handbook of Music Performance, Vol 2 (pp. 528-552), Oxford University Press. Dalmazzo DC, Waddell G, and Ramirez R (2021), Applying deep learning techniques to estimate patterns of musical gesture, Frontiers in Psychology, 11(575971), 1-15. DOI» Waddell G and Williamon A (2019), Technology use and attitudes in music learning, Frontiers in ICT, 6 (11), 1-14.