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A Computational Model for Emotion-Regulation

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Wai presentatie oktober 2007

Wai presentatie oktober 2007

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• 1. A computational model for emotion-regulation
• Matthijs Pontier
• 2. Overview of this presentation
• Model of emotion regulation by Gross
• Explanation of the computational model
• Results of the computational model
• Discussion
• 3. Goal of this study
• Gross has described a model of emotion-regulation
• This model is described informally
• Goal: Make a computational model
• 4. Model of emotion regulation by Gross
• The experienced level of emotion can be changed by choosing a different:
• Situation Last-minute study vs Dinner
• Sub-situation Talk about exam vs Something else
• Aspect Distract vs Pay attention
• Meaning “ It’s only a test” vs “It’s really important”
• 5. Model of emotion-regulation by Gross
• 6. The computational model
• Emotional Values of elements that are chosen are expressed in real numbers [0, 2]
• Situation Selection = 1.12 
• The chosen situation has an emotion-level of 1.12
• The Emotion-Response-Level is also expressed in a real number [0, 2]
• The Emotion-Response-Level is influenced by the Emotional Values
• The chosen Emotional Values are influenced by the Emotion-Response-Level
• 7. Updating the Emotion-Response-Level
• New_ERL = (1-  (w n * v n ) +  Old_ERL
•  = Proportion of Old ERL which is taken to the new ERL
• w n = Weight of an element
• V n = Emotional Value of an element
• 8. Updating the Emotion-Response-Level
• Old_ERL = 1
•  = 0.5
•  (w n * v n ) = x-axis
• New_ERL = y-axis
• 9. Updating the Emotional Values Vn
•  v n = -  n * d / d max
• New_v n = old_v n +  v n
• d = ERL – ERL norm
• ERL norm = optimal level ERL
•  n = 'willingness' to adjust behaviour
• 10. Updating the Emotional Values Vn
•  n = 0.1
• d max = 2
• d = x-axis
•  v n = y-axis
• 11. Model in layers
• Emotion-Response-Level
• Emotional Values V n
• Modification Factors  n
• 12. LeadsTo simulation of the model
• Initially high emotion response level
• Low ERL norm (excitement)
•  n ’s set to values for optimal regulation
• Smaller  n ’s result in under regulation
• Bigger  n ’s result in over regulation
• 13. Updating Modification Factors  n
• Eval(d) = abs.avg.(d) t t/m t+5
•  n =  n  *  n / (1  n ) * (Eval(new_d) / Eval(old_d) – C n )
• New_  n = old_  n +  n
•  n = (personal) tendency to adjust behaviour much or little
• C n = constant that describes costs to adjust behaviour
• 14. Updating Modification Factors  n
•  n = 0.3
•  n = 0.3
• Eval(old_d) = 1
• C n = 0.5
• Eval(new_d) = x-axis
•  n = y-axis
• 15. Model in layers
• Emotion-Response-Level
• Emotional Values V n
• Modification Factors  n
• Personal Tendency  n
• 16. LeadsTo simulation of the model
• Initially low  n ’s
•  set to value for good adaptive behaviour
•  n ’s rise during simulation, which leads to adaptive behaviour
• Small  results in under adaptation
• Big  results in over adaptation
• 17. Updating  n 's
•  n =  * Event / (1 + (  n -  basic ) * Event)
• New_  n = Old_  n +  n
•  = variable which represents influencability of  n 
• Event = Certain event which influences  n
• e.g. Therapy (positive) or Trauma (negative)
• 18. Updating  n 's
•  = 0.3
•  n = 0.1
•  basic = 0.5
• Event = x-axis
•  n = y-axis
• 19. Model in layers
• Emotion-Response-Level
• Emotional Values V n
• Modification Factors  n
• Personal Tendency  n
• Experiences (e.g. Therapy / Trauma)
• 20. LeadsTo simulation of the model
• Initial low  n ’s and 
• Successful therapy at timepoint 40
• 21. Discussion
• Emotion regulation model was able to simulate:
• Simple emotion regulation process