A Computational Model for Emotion-Regulation

  • 255 views
Uploaded on

Wai presentatie oktober 2007

Wai presentatie oktober 2007

More in: Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
255
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
3
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 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”
      • Response Hiding your embarrassment after bad result
  • 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
      • Adaptive emotion regulation
      • Effects of events like therapy or trauma
    • Many improvements can still be made
      • Variable ability to recognize emotional state
      • Modify response using social desirability etc.
      • Etc.
  • 22. Questions?