an adaptive affective social decision making model alexei sharpanskykh jan treur vrije universiteit...
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An Adaptive Affective Social Decision Making Model
Alexei SharpanskykhJan Treur
vrije Universiteit amsterdam
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Motivation
Traditionally, human decision making has been modelled as the problem of rational choice from a number of options using economic utility-based theories
Research Aim: To create a more biologically plausible model of human decision making based on theoretical principles from Neuroscience and Social Science
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Decision Making Aspects
Predicted effects of the options
Valuing of these effects
Emotions felt in relation to this valuing
Social influence
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Predicted effects of the options
Simulated behavior and perception chains by Hesslow
s1 r1
s2
r3s3
r2
...
s1, s2, s3 ... are sensory statesr1, r2, r3 ... are preparation statesw1, w2, ... are link strengthsV1, V2, ... are state values
w1
w2
V1 V2
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Predicted effects of the options
Simulated behavior and perception chains by Hesslow
s(evacuation_required) r(goto(staircaseA))
s(at(staircaseA)) r(goto(crossingA))
s(at(crossingA)) r(goto(exit))
s(at(exit))staircaseA
crossingA
exit
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Emotion generation
“As if” body loop (by A.Damasio)
sensory state
preparation for the induced bodily response
sensory representation of the bodily response
induced feeling
s1 r1
s2
r(bem)s(bem)
feeling(bem)
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Emotions and Valuing
Decision making involves emotional valuing of predicted consequences of decision options
A notion of value, involving emotions is represented in the amygdala
s1 r1
s2
r(bem)s(bem)
feeling(bem)
Damasio’s Somatic Marker HypothesisEach represented decision option induces (via an emotional response) a feeling which is used to mark the option
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Social contagion
A’s emotion state for option O
A’s intention state
for option O
emotion states of
other group members
for option O
intention states of
other group members
for option O
A’s somatic marking
for option O
A’s mirroring of emotion
for option O
A’s mirroring of intention
for option O
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Learning
Hebbian learning principle: connections between neurons that are activated simultaneously are strengthened
s1 r1
s2
r(bem)s(bem)
feeling(bem)
s(G(r1))
s1 r1
s2
r(bem)s(bem)
feeling(bem)
s(G(r1))
s1 r1
s2
r(bem)s(bem)
feeling(bem)
s(G(r1))
action-effect prediction links emotion-related valuation links social influence links
d (r1, s2)/dt = r1s2 (1 – (r1, s2)) – (r1, s2)
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Learning
s1 r1
s2
r(bem)s(bem)
feeling(bem)
s(G(r1))
s1 r1
s2
r(bem)s(bem)
feeling(bem)
s(G(r1))
s1 r1
s2
r(bem)s(bem)
feeling(bem)
s(G(r1))
(I) action-effect prediction links (II) emotion-related valuation links (III) social influence links
Learning of links (II) has the greatest impact on decision making Learning of links (III) has a negligible effect on decision making, when agents are similar A combination of learning of all links (I), (II) and (III) results in the strongest discrimination
between the options
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Combined model
s(evacuation_required)
r(move_to(E))
s(goal)
s(bfear)
s(is_at(E))
s(eval_for(is_at(E),bfear))
r(bfear)
s(eval_for(is_at(E),bhope))
hoper(bhope)
s(G(move_to(E)))
s(G(bfear))
s(G(bhope))
fear
s(bhope)
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Simulation Results
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910
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The path is short, but becomes dangerous
The path is average, but dangerous
The path is long, but safe
timetimetime
# action # action# action
Preparation for the execution of the options
The developed model could be used to evaluate and predict emotional decisions of individuals in groups under stressful conditions
Learning of the emotion-related links has the strongest effect on discrimination of decision making options (cf. the role of the Amygdala in valuing)
In the future emotion regulation mechanisms (e.g., to cope with fear and stress) will be investigated
Conclusions