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![Page 1: A formal model of new reinforcement sensitivity theory (RST) Alan Pickering Department of Psychology a.pickering@gold.ac.uk.](https://reader036.fdocuments.in/reader036/viewer/2022062511/5515c7f2550346c6278b4678/html5/thumbnails/1.jpg)
A formal model of “new” reinforcement sensitivity
theory (RST)
Alan PickeringDepartment of Psychology
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Overview• Review:
Old RSTNew RSTPast theoretical models of system interactions
• Present outline of a formal model of interactions in “new” RST
• Conclusions
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OLD RSTBehavioural Activation System = BASBehavioural Inhibition System = BIS
SYSTEM RESPONDS TO
OUTPUTS TRAIT
BAS ConditionedReward
Approach + Arousal
Imp (Ext)
BIS ConditionedPunishment
Inhibition + Arousal
Anxiety(N)
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NEW RSTFlight/Fight/Freeze System = FFFS
SYSTEM RESPONDS TO
OUTPUTS TRAIT
BAS Reward Approach + Arousal
Imp (Ext)
FFFS Punishment Flight/Fight/Freezing
???
BIS Goal Conflict Inhibition + Arousal
Anxiety(N)
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Interactions
• Dynamic interactions between activated systems
• E.g., mutually inhibitory above• But NOT necessarily statistical
interactions
System 1 System 2
Input Input
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Interactions in Old RST 1Gray & Smith (1969). In Gilbert and Sutherland
(Eds) Animal Discrimination Learning. London: Academic Press.
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Interactions in Old RST 2Pickering (1997). European Psychologist, 2,
139-163.
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Interactions in New RST 1McNaughton & Corr (2004). Neuro-
science and Biobehavioural Reviews, 28, 285-305.
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Corr (2004). Neuroscience and Biobehavioural Reviews, 28, 317-332.
Interactions in New RST 2
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System Interactions and Joint Subsystems RST
• SimilaritiesBoth emphasise joint actions of systems with independent sensitivities
• DifferencesJoint subsystems is an additive account whereas system interactions are typically nonlinear and may cause statistical interactions
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Separable Subsystems• Response to
Reward (S+) solely controlled by BAS/IMP etc
• A single main effect
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Joint Subsystems• Response to
reward (S+) reflects both BAS/IMP and BIS/ANX
• Two main effects (but no interaction)
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A Simple Model of New RST• Has dynamically interacting systems• Has 3 key sensitivity parameters
wA BAS sensitivity
wF FFFS sensitivity
wI BIS sensitivity
• Has two key parameters concerning strengths of input stimuliSR reward stimulus strength
SF fear stimulus strength
independent
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Two System ModelSF
SR
FFFS BAS
System Outputs
wF wA
inhibitory
excitatory
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Three System ModelSF
SR
FFFS BAS
FFFS Output
wF wA
BIS
BAS Output
wI
AND
inhibitory
excitatory
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Simulation 1: No BISwA = 0.5; SR=0.5; SF=0.5/0.9
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 0.2 0.4 0.6 0.8 1
FFFS Sensitivity
Act
ivat
ion
FFFSActivation0.5
BASActivation0.5
FFFSActivation0.9
BASActivation0.9
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Simulation 2: With BISwA = 0.5; SR=0.5; SF=0.5/0.9; plus wI = 0.5
-0.2
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
FFFS Sensitivity
Act
ivat
ion
FFFSActivation0.5
BASActivation0.5
FFFSActivation0.9
BASActivation0.9
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Simulation 2: BIS ActivationwA = 0.5; SR=0.5; SF=0.5/0.9; plus wI = 0.5
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 0.2 0.4 0.6 0.8 1
FFFS Sensitivity
Act
ivat
ion
BISActivation0.5
BISActivation0.9
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Simulation 3: Varying SF & SR
SF + SR = 1; wA = wF = 0.5; plus no BIS / wI = 0.5
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Strength of Fear Stimulus
Ac
tiv
ati
on
FFFSActivation
BASActivation
FFFSActivationwith BISBASActivationwith BISBISActivation
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Simulation 4: Simulating self-reported trait values
• How might self-report trait values map onto the 3 underlying sensitivities in the model?
• Assume trait (e.g., anxiety) is a reflection of one system (e.g., BIS)
• Assume people do not have direct awareness of their sensitivity values
• Start with simplest possible model
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Simulation 4: Further Assumptions
Assume …• for a given situation, that each system
output level corresponds to the level of an emotional state
• that a self-reported trait reflects the average memory of a specific emotional state across a large no. of situations
• that the situations for each simulated person differ randomly in SR and SF
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Simulation 4: Simplifications
• Only relevant features of situation are SR and SF
• 200 random situations for each person• Perfect recall of mean system outputs
across al 200 situations• 100 simulated subjects with
sensitivitites drawn independently from normal distribution (m=0.5; s.d.=0.15)
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Simulation 4: ExperiencesFor simulated subject #1
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Simulation 4: SensitivitieswI for 100 simulated subjects
m=0.49, sd=0.14
BIS Sensitivity
.85
.80
.75
.70
.65
.60
.55
.50
.45
.40
.35
.30
.25
.20
Fre
qu
en
cy
20
15
10
5
0
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Simulation 4: Results“Trait” Correlations (N=100)
BAS FFFS BIS
FFFS -0.53
BIS 0.40 0.33
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Simulation 4: ResultsRegression predicting self-reported BAS from 3 sensitivitiesR2 = 0.89
Coefficientsa
.179 .015 11.631 .000
.431 .018 .835 24.049 .000
-.206 .016 -.442 -12.755 .000
-.077 .017 -.157 -4.515 .000
(Constant)
WA
WF
WI
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: MBASOUTa.
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Simulation 4: ResultsRegression predicting self-reported FFFS from 3 sensitivitiesR2 = 0.82
Coefficientsa
.486 .021 23.280 .000
-.036 .024 -.064 -1.466 .146
.455 .022 .908 20.761 .000
.021 .023 .039 .888 .377
(Constant)
WA
WF
WI
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: MFFFSOUTa.
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Simulation 4: ResultsRegression predicting self-reported BIS from 3 sensitivitiesR2 = 0.85
Coefficientsa
-.053 .020 -2.605 .011
.464 .024 .772 19.677 .000
.148 .021 .274 7.005 .000
.278 .022 .487 12.396 .000
(Constant)
WA
WF
WI
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: MBISOUTa.
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Conclusions & ChallengesCONCLUSIONS1. New RST produces at least as
complex a pattern of possible effects as old RST
2. Current models seem to predict that the “BIS-related” personality trait may be strongly influenced by sensitivities of all 3 systems
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Conclusions & ChallengesCHALLENGES1. To see if the conclusions generalise to
all model variants, including ones with more realistic assumptions
2. Are there any variants which produce a radically different pattern of predictions?
3. To apply the model to task data to see if it can predict patterns of results