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A formal model of new reinforcement sensitivity theory (RST) Alan Pickering Department of Psychology

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Presentation on theme: "A formal model of new reinforcement sensitivity theory (RST) Alan Pickering Department of Psychology"— Presentation transcript:

1 A formal model of new reinforcement sensitivity theory (RST) Alan Pickering Department of Psychology

2 Overview Review: Old RST New RST Past theoretical models of system interactions Present outline of a formal model of interactions in new RST Conclusions

3 OLD RST Behavioural Activation System = BAS Behavioural Inhibition System = BIS SYSTEMRESPONDS TO OUTPUTSTRAIT BASConditioned Reward Approach + Arousal Imp (Ext) BISConditioned Punishment Inhibition + Arousal Anxiety (N)

4 NEW RST Flight/Fight/Freeze System = FFFS SYSTE M RESPONDS TO OUTPUTSTRAIT BASRewardApproach + Arousal Imp (Ext) FFFSPunishmentFlight/Fight/ Freezing ??? BISGoal Conflict Inhibition + Arousal Anxiety (N)

5 Interactions Dynamic interactions between activated systems E.g., mutually inhibitory above But NOT necessarily statistical interactions System 1System 2 Input

6 Interactions in Old RST 1 Gray & Smith (1969). In Gilbert and Sutherland (Eds) Animal Discrimination Learning. London: Academic Press.

7 Interactions in Old RST 2 Pickering (1997). European Psychologist, 2,

8 Interactions in New RST 1 McNaughton & Corr (2004). Neuro- science and Biobehavioural Reviews, 28,

9 Corr (2004). Neuroscience and Biobehavioural Reviews, 28, Interactions in New RST 2

10 System Interactions and Joint Subsystems RST Similarities Both emphasise joint actions of systems with independent sensitivities Differences Joint subsystems is an additive account whereas system interactions are typically nonlinear and may cause statistical interactions

11 Separable Subsystems Response to Reward (S + ) solely controlled by BAS/IMP etc A single main effect

12 Joint Subsystems Response to reward (S + ) reflects both BAS/IMP and BIS/ANX Two main effects (but no interaction)

13 A Simple Model of New RST Has dynamically interacting systems Has 3 key sensitivity parameters w A BAS sensitivity w F FFFS sensitivity w I BIS sensitivity Has two key parameters concerning strengths of input stimuli S R reward stimulus strength S F fear stimulus strength independent

14 Two System Model SFSF SRSR FFFSBAS System Outputs wFwF wAwA inhibitory excitatory

15 Three System Model SFSF SRSR FFFSBAS FFFS Output wFwF wAwA BIS BAS Output wIwI AND inhibitory excitatory

16 Simulation 1: No BIS w A = 0.5; S R =0.5; S F =0.5/0.9

17 Simulation 2: With BIS w A = 0.5; S R =0.5; S F =0.5/0.9; plus w I = 0.5

18 Simulation 2: BIS Activation w A = 0.5; S R =0.5; S F =0.5/0.9; plus w I = 0.5

19 Simulation 3: Varying S F & S R S F + S R = 1; w A = w F = 0.5; plus no BIS / w I = 0.5

20 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

21 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 S R and S F

22 Simulation 4: Simplifications Only relevant features of situation are S R and S F 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)

23 Simulation 4: Experiences For simulated subject #1

24 Simulation 4: Sensitivities w I for 100 simulated subjects m=0.49, sd=0.14

25 Simulation 4: Results Trait Correlations (N=100) BASFFFSBIS FFFS-0.53 BIS

26 Simulation 4: Results Regression predicting self-reported BAS from 3 sensitivities R 2 = 0.89

27 Simulation 4: Results Regression predicting self-reported FFFS from 3 sensitivities R 2 = 0.82

28 Simulation 4: Results Regression predicting self-reported BIS from 3 sensitivities R 2 = 0.85

29 Conclusions & Challenges CONCLUSIONS 1.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

30 Conclusions & Challenges CHALLENGES 1.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


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