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A formal model of “new” reinforcement sensitivity
theory (RST)
Alan PickeringDepartment of Psychology
Overview• Review:
Old RSTNew RSTPast theoretical models of system interactions
• Present outline of a formal model of interactions in “new” RST
• Conclusions
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)
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)
Interactions
• Dynamic interactions between activated systems
• E.g., mutually inhibitory above• But NOT necessarily statistical
interactions
System 1 System 2
Input Input
Interactions in Old RST 1Gray & Smith (1969). In Gilbert and Sutherland
(Eds) Animal Discrimination Learning. London: Academic Press.
Interactions in Old RST 2Pickering (1997). European Psychologist, 2,
139-163.
Interactions in New RST 1McNaughton & Corr (2004). Neuro-
science and Biobehavioural Reviews, 28, 285-305.
Corr (2004). Neuroscience and Biobehavioural Reviews, 28, 317-332.
Interactions in New RST 2
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
Separable Subsystems• Response to
Reward (S+) solely controlled by BAS/IMP etc
• A single main effect
Joint Subsystems• Response to
reward (S+) reflects both BAS/IMP and BIS/ANX
• Two main effects (but no interaction)
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
Two System ModelSF
SR
FFFS BAS
System Outputs
wF wA
inhibitory
excitatory
Three System ModelSF
SR
FFFS BAS
FFFS Output
wF wA
BIS
BAS Output
wI
AND
inhibitory
excitatory
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
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
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
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
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
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
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)
Simulation 4: ExperiencesFor simulated subject #1
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
Simulation 4: Results“Trait” Correlations (N=100)
BAS FFFS BIS
FFFS -0.53
BIS 0.40 0.33
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.
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.
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.
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
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
