Keynote 

Abstract: Music is a universal human capacity, yet musical creation and appreciation exhibit extraordinary cultural diversity. Despite this, many large-scale theories of music perception have emphasised universal principles that are relatively insensitive to experience, leading to cultural bias and little explanation for the cultural evolution of musical traditions. In this address I will present an alternative framework that foregrounds lifelong learning as central to how listeners come to perceive, interpret, and appreciate the music of their own cultures. Central to this approach is computational modelling of the psychological mechanisms that support music learning, with a particular focus on expectation. Expectation is an obligatory cognitive process through which the brain continuously generates predictions about what will happen next, based on patterns acquired from previous experience. During musical listening, listeners form expectations about the pitch and timing of upcoming events, as well as about more abstract structural features such as harmonic progression and thematic unity. These predictions reflect both long‑term learning accumulated over a lifetime of exposure to culturally specific musical systems, and short‑term learning of local patterns within the current listening context. Expectation also shapes perceived musical complexity: music that is more unpredictable is typically judged more complex. In turn, increased complexity heightens physiological and subjective arousal, whereas musical pleasure follows an inverted‑U relationship with complexity, with intermediate levels eliciting the greatest enjoyment. I will argue that this pattern reflects a deeper mechanism tied to learning progress: the brain derives pleasure from the process of discovering structure. Highly predictable music offers little new information to learn from, whereas highly unpredictable music impedes effective learning while intermediate levels provide an optimal basis for learning and pleasure. In closing, I will discuss how this learning-based perspective fits with cultural universals in music perception, how it can help explain individual differences in musical experience, and its role in the broader cultural evolution of musical traditions.

Bio: Marcus Pearce is Reader in Cognitive Science at Queen Mary University of London and Honorary Professor of Neuroscience at Aarhus University, Denmark. He has published extensively on auditory perception and cognition including the entry on Music Perception in the Oxford Research Encyclopedia of Psychology and the research monograph Learning to listen, listening to learn (Oxford University Press, 2025). His machine learning model of auditory expectation, IDyOM, is widely used in both music psychology and auditory cognitive neuroscience. He has given presentations at the Wellcome Collection and Royal Institution, run a Live Science residency at the Science Museum, delivered the IEE Faraday Lecture, and collaborated with the London Sinfonietta to produce a free iOS app for developing rhythm skills described by the New York Times as "maddeningly addictive". He was educated in Experimental Psychology at the University of Oxford and Artificial Intelligence at the University of Edinburgh.