The current thesis identifies inconsistencies and contradictions within the literature regarding guilt. One approach considers guilt to be a prosocial emotion that motivates individuals to maintain social halmony. The other approach considers guilt to be egoistic by nature, and motivates individuals to make themselves feel better. This thesis identifies these approaches as the SoCial Guilt Model (SGM) and the Individual Guilt Model (IGM) respectively. The Cognitive-Affect Guilt Explanation (CAGE) proposed by this thesis is founded upon three core assumptions: (i) that guilty feelings differ f):om guilty thoughts (the "CAGE distinction"), (ii) that guilty feelings primarily motivate impulsiveness, and (iii) that guilty thoughts primarily motivate prosocial behaviours. Experiment 1 indicated that guilty feelings were a significant predictor of self-indulgence whereas guilty thoughts were not. Experiment 2 supported all three core assumptions of CAGE. Guilty thoughts predicted pro social behaviour, while guilty feelings predicted impulsiveness at the expense of long-telm gain. Experiment 3 replicated the association between guilty feelings and impulsiveness but failed to replicate the association between guilty thoughts and pro social behaviour. Experiment 4 showcased the ability of CAGE to predict behaviours in a dynamic and complex environment, involving multiple guilt behaviours (reparation, self-punishment). Experiment 5 was conducted to investigate the neural correlates of the CAGE distinction. The results supported the CAGE distinction based upon activity of limbic and social cognition structures. The theoretical and practical implications of the proposed model are discussed. The original contribution to lmowledge of the present research is how an understanding of the CAGE distinction can benefit psychologists in predicting the types of behaviour associated with guilt.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:651279 |
| Date | January 2014 |
| Creators | McLatchie, Neil |
| Publisher | University of Kent |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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