a computational model for emotion-regulation
DESCRIPTION
Wai presentatie oktober 2007TRANSCRIPT
A computational model for emotion-regulation
Matthijs Pontier
Overview of this presentation
● Model of emotion regulation by Gross● Explanation of the computational model● Results of the computational model● Discussion
Goal of this study
● Gross has described a model of emotion-regulation● This model is described informally● Goal: Make a computational model
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
Model of emotion-regulation by Gross
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
Updating the Emotion-Response-Level
● New_ERL = (1-(wn * vn) + Old_ERL
● = Proportion of Old ERL which is taken to the new ERL
● wn = Weight of an element
● Vn = Emotional Value of an element
Updating the Emotion-Response-Level
• Old_ERL = 1
• = 0.5
• (wn * vn) = x-axis
• New_ERL = y-axis
Updating the Emotional Values Vn
● vn = -
n * d / d
max
● New_vn = old_v
n + vn
● d = ERL – ERLnorm
● ERLnorm = optimal level ERL
● n = 'willingness' to adjust behaviour
Updating the Emotional Values Vn
● n = 0.1
● dmax
= 2
● d = x-axis
● vn = y-axis
Model in layers
Emotion-Response-Level
Emotional Values Vn
Modification Factors n
LeadsTo simulation of the model
● Initially high emotion response level● Low ERLnorm (excitement)● n’s set to values for optimal regulation● Smaller n’s result in under regulation● Bigger n’s result in over regulation
Updating Modification Factors n
● Eval(d) = abs.avg.(d)t t/m t+5
● n = n* n / (1n) * (Eval(new_d) / Eval(old_d) – Cn)
● New_n = old_
n +
n
● n = (personal) tendency to adjust behaviour much or little
● Cn =
constant that describes costs to adjust behaviour
Updating Modification Factors n
• n = 0.3
• n = 0.3
• Eval(old_d) = 1
• Cn = 0.5
• Eval(new_d) = x-axis
• n = y-axis
Model in layers
Emotion-Response-Level
Emotional Values Vn
Modification Factors n
Personal Tendency n
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
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)
Updating n's
• = 0.3
• n = 0.1
• basic
= 0.5
• Event = x-axis
• n = y-axis
Model in layers
Emotion-Response-Level
Emotional Values Vn
Modification Factors n
Personal Tendency n
Experiences (e.g. Therapy / Trauma)
LeadsTo simulation of the model
● Initial low n’s and ● Successful therapy at timepoint 40
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.
Questions?