Abstract/Summary

Emotions are complex, universal experiences, which provide richness to life events influencing physiological and cognitive responses. For researchers exploring affect, there are a range of subjective reporting tools which allow an individual to evaluate their affective experience, subsequently allowing their physiological response to be tagged. However, the reliability and accuracy of these reports suffer from factors such as emotional intelligence, past emotional experiences and cultural background. To overcome these problems, this thesis presents the development of a novel tool, 'aPPRAISE', for obtaining implicit reports of affect. This tool allows an individual, whilst stimulated by music, to implicitly, continuously report their affective state upon a number of dimensions. In addition, aPPRAISE is statistically more reliable in tagging physiological data, than current popular tools. Furthermore, there are a number of dimensions available for researchers to assess induced affect e.g. valence, arousal, energy, tension and GEMS. It is generally accepted that two dimensions are not sufficient to describe the nuanced affective experiences elicited. However, obtaining reports on a large set of dimensions impacts experimental duration making the experiment laborious for participants. As a solution to these problems, the work presented in this thesis extracts the hidden relationship between dimensions of affect. Reliable models have been found which allow for a post-hoc estimation of unknown dimensions, such as energy, from a smaller set of reported dimensions, such as valence and arousal. To further emphasise the need of high-dimensional reports, the discrimination power of the GEMS space and alternate dimensional models are evaluated. The results highlight that a high-dimensional representation of affect significantly increases the ability to discriminate between affective experiences. Therefore for future research in this field, this thesis suggests the need to explore affect upon a large number of dimensions, using aPPRAISE and the models established for estimating un-reported dimensions of affect.