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Title: Shared emotions in music
Author: Cochrane, Thomas
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2007
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In this thesis I show that groups can share token emotional states by performing music together. First I argue that emotions are perceptions, representing the self's dynamic relation with the world. This representation is achieved by patterns of bodily changes, functioning independently of conscious feeling. Moreover, emotional expressions should be included in this analysis because they contribute to the pattern of bodily changes. This entails that we can 'think through' our emotions by manipulating our behavioural expressions. I then argue that empathy relies on our tendency to neurally mirror the expressive behaviours of other people, resulting in a simulation of emotional arousal. Turning to music, I argue that music hijacks our simulative capacities and thus that recognising emotions in music is like recognising emotions in people. The fact that the brain processes patterns of sound, vision and touch intermodally as patterns of movement underlines this claim. All this allows me to argue that musicians can use music to physically extend the cognition of their emotions. Here the music may not just influence their bodily changes, but may be processed alongside those changes as an elaboration of the overall pattern. On some occasions, the music may even take the dominant role in this respect. Thus emotional representations are best described more neutrally, though bodily patterns remain the central case of emotions. I then analyse joint listening to music, arguing that our perceptual activities may be interdependently structured, mutually fixing the character of the object, as well as encouraging similar emotional responses. In order to show that the intrinsic content of mental states can be shared, I then look at the theory of collective intentions. This provides a model for embodying the content of a mental state in the agreement between individuals. I apply this model to ensemble musical performance. To listen to the music submitted with this thesis, go to;
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: ML Literature of music