Control Theory of Emotion Regulation: A Dynamic Model of Emotion

1. Control Theory of Emotion Regulation: A Dynamic Model of Emotion David Cameron, Peter Totterdell, David Holman, Stuart Bennett The University of Sheffield d.s.cameron@shef.ac.uk

2. Outline • MARDY - Model of Affect Regulation Dynamics • Simulates fluctuations in internal and expressed emotional states • Based upon theoretical control model by Diefendorff & Gosserand (2003) • Incorporates an established model of alertness by Akerstedt, Folkard & Portin (2004) d.s.cameron@shef.ac.uk

3. Modelling Emotion • Perhaps best understood as dynamic phenomena • Intensity profiles (E.g. Verduyn et al., 2009) • Statistical models (E.g. Oravecz et al., In press) • Computational models (E.g. Gratch & Marsella – EMA) • Can be represented as key dimensions • E.g. Bipolar scale of positive to negative valence • Differentiation between felt and expressed emotional states d.s.cameron@shef.ac.uk

4. EmotionalLabour • Expression of a required set of display emotions in exchange for a wage (Hochschild ‘83) • Emotion regulation methods • Deep Acting and Surface Acting • Control theory as a model of understanding regulation • Tiered, competing goals • Physically Demanding • Can lead to employee burnout d.s.cameron@shef.ac.uk

5. Resource & Ego Depletion • Cognitive tasks are demanding • Shared ‘resource’ of cognitive energy (Baumeister et al. ‘07) • Depletion of this resource  exhaustion • Cognitive and physical energy associated (Gailliot et al. ‘07) • Energy (and resource) dynamically fluctuate • E.g. Self regulation ability sharply drops at the end of day d.s.cameron@shef.ac.uk

6. Compared to Desired Goal Compared to Desired State Compared to Desired State Compared to Desired Goal Resource Available Internal Emotion State Displayed Emotion State Inputs (from others) Outputs (to others) d.s.cameron@shef.ac.uk http://www.erosresearch.org/

7. Compared to Desired Goal Compared to Desired State Compared to Desired State Compared to Desired Goal Resource Available Internal Emotion State Displayed Emotion State Inputs (from others) Outputs (to others) d.s.cameron@shef.ac.uk

8. Compared to Desired Goal Compared to Desired State Compared to Desired State Compared to Desired Goal Resource Available Internal Emotion State Displayed Emotion State Inputs (from others) Outputs (to others) d.s.cameron@shef.ac.uk

9. Compared to Desired Goal Compared to Desired State Compared to Desired State Compared to Desired Goal Resource Available Internal Emotion State Displayed Emotion State Inputs (from others) Outputs (to others) d.s.cameron@shef.ac.uk

10. Compared to Desired State Compared to Desired State Compared to Desired Goal Resource Available Internal Emotion State Displayed Emotion State Inputs (from others) Outputs (to others) d.s.cameron@shef.ac.uk

11. Modelling diary data Hourly mood recordings across two days Afternoon lull & recovery in both diary data and model data. Less variability in second day d.s.cameron@shef.ac.uk

12. Effects Seen • Internal state and physical energy linked • Diary data r = .679 Model data r = .655 • Fluctuating emotional states follow cyclical patterns • Fixed display rules result in greater employee exhaustion d.s.cameron@shef.ac.uk

13. Future Work • Further validation against collected data • Diary data from couples • Development of model predictions • Contrary to expectations greater variation in expression with strict display rules. • Preliminary testing for sleep studies • Network of interacting models • Emotion contagion • Programmed Interventions? d.s.cameron@shef.ac.uk

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15. Example data Experimental model is repeatedly woken between day 1 and 2 All other parameters are unchanged Recordings taken every hour (as in diary studies) d.s.cameron@shef.ac.uk