A computational model for emotion-regulation <ul><ul><li>Matthijs Pontier </li></ul></ul>
Overview of this presentation <ul><li>Model of emotion regulation by Gross </li></ul><ul><li>Explanation of the computatio...
Goal of this study <ul><li>Gross has described a model of emotion-regulation </li></ul><ul><li>This model is described inf...
Model of emotion regulation by Gross <ul><li>The experienced level of emotion can be changed by choosing a different: </li...
Model of emotion-regulation by Gross
The computational model <ul><li>Emotional Values of elements that are chosen are expressed in real numbers [0, 2] </li></u...
Updating the Emotion-Response-Level <ul><li>New_ERL = (1-  (w n  * v n ) +   Old_ERL </li></ul><ul><li> =   Pr...
Updating the Emotion-Response-Level <ul><li>Old_ERL = 1 </li></ul><ul><li> = 0.5 </li></ul><ul><li> (w n  * v n ) = x-a...
Updating the Emotional Values Vn <ul><li> v n  = -   n  * d / d max   </li></ul><ul><li>New_v n  = old_v n  +   v n </l...
Updating the Emotional Values Vn <ul><li> n  = 0.1 </li></ul><ul><li>d max  = 2 </li></ul><ul><li>d = x-axis </li></ul><u...
Model in layers <ul><ul><li>Emotion-Response-Level </li></ul></ul><ul><ul><li>Emotional Values V n </li></ul></ul><ul><ul>...
LeadsTo simulation of the model <ul><li>Initially high emotion response level </li></ul><ul><li>Low ERL norm  (excitement)...
Updating Modification Factors   n <ul><li>Eval(d) = abs.avg.(d) t t/m t+5 </li></ul><ul><li> n  =   n  *    n   / (1...
Updating Modification Factors   n   <ul><li> n  = 0.3 </li></ul><ul><li> n  = 0.3 </li></ul><ul><li>Eval(old_d) = 1 </l...
Model in layers <ul><ul><li>Emotion-Response-Level </li></ul></ul><ul><ul><li>Emotional Values V n </li></ul></ul><ul><ul>...
LeadsTo simulation of the model <ul><li>Initially low   n ’s </li></ul><ul><li> set to value for good adaptive behaviou...
Updating   n 's <ul><li> n   =    * Event / (1 + (  n  -   basic ) * Event) </li></ul><ul><li>New_  n   = Old_  n ...
Updating   n 's <ul><li> = 0.3 </li></ul><ul><li> n  = 0.1 </li></ul><ul><li> basic  = 0.5 </li></ul><ul><li>Event = ...
Model in layers <ul><ul><li>Emotion-Response-Level </li></ul></ul><ul><ul><li>Emotional Values V n </li></ul></ul><ul><ul>...
LeadsTo simulation of the model <ul><li>Initial low   n ’s and   </li></ul><ul><li>Successful therapy at timepoint 40 </...
Discussion <ul><li>Emotion regulation model was able to simulate: </li></ul><ul><ul><li>Simple emotion regulation process ...
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A Computational Model for Emotion-Regulation

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Wai presentatie oktober 2007

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A Computational Model for Emotion-Regulation

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

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