A Computational Model for Emotion-Regulation

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


Emotional Values Vn


Personal Tendency n


● 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


Emotional Values Vn


Personal Tendency n

Experiences (e.g. Therapy / Trauma)


● 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?

Technology

A Computational Model for Emotion-Regulation