1. Do you like me? The role of rose tinted glasses in mental health
Katherine S. Button

2. Imagine you are entering a cocktail party
Imagine you are entering a cocktail party. You are excited, you enjoy socialising, and you believe you are well liked. You’ve had a lifetime of loving, supportive relationships and are self-assured and confident. You arrive expecting things to go well, the evidence you encounter supports this, and you later reflect on the most enjoyable moments.
Now suppose you are socially anxious. You desperately want people to like you but you fear that they’ll think you’re a fool. Perhaps you’ve had an excruciatingly embarrassing experience at a recent dinner party, or a childhood of negative social experiences, being bullied and undermined by peers and parents. Deep down you believe yourself to be unlikable. You arrive anxious and expecting things to be awkward, and can’t help looking for evidence that confirms your belief. When when get home afterward you conduct a post-mortem, ruminating on all the worst bits.

3. Social anxiety
Negative self-schema,
“I am unlikeable, socially incompetent’
Fear of negative evaluation (FNE) and persistent negative self-beliefs defines social anxiety yet the process of inferring social evaluation, and it’s role in reinforcing negative self-beliefs / self-esteem are poorly understood. According to the cognitive model, negative self-beliefs act as a prism through which incoming sensory information is processed, leading to biases in perception, attention, and and potentially learning. Social interactions are dynamic and socially reinforced, therefore biased social evaluation learning may be an important mechanism in reinforcing negative self beliefs and maintaining symptoms. Instrumental learning task to model social evaluation learning
Reinforces negative self beliefs

4. Social evaluation learning task
I think you are…
Witty
Dull
Like = 0.8 positive correct
Dislike = 0.8 negative correct
× 32
Incorrect!
I developed a task to assess social evaluation learning. The participant is instructed to work out how much the computer likes them based on trial and error learning, by selecting the word which corresponds with what the compuert thinks. to work out whether computer likes / dislikes participant
64 trials, 2 rules, rule switch halfway
Like = 0.8 positive words correct
Dislike = 0.8 negative words correct
Outcome: errors to criterion (8 consecutive rule-contingent answers)
Button et al. (2012) JBTEP, 43(4):

5. Social Evaluation Learning Task
Self
‘I think you are…’
Like
Dislike
Other
‘I think George is…’
Learning Phase
32 trials
Global Rating
Dislike 0 – 100 Like
Button et al (2015) PLoS One. 10(4): p. e

6. Results Datasets = 7, n = 337 Response Rate Errors to Criterion Bias β
95% CI p
Social anxiety
0.25
0.001
Condition
5.3
0.006
Social anxiety × Condition
-0.12
0.020
We now have data from 7 data-sets (2 published, one in development and the other from student project) on 337 individuals. We can see clear evidence of a strong positive self bias at low anxiety individuals on average make 7 fewer errors learning negative relative to self-evaluation, and that this positive self-bias reduces as anxiety incresaes. We can also see that this effect is specific to the self and that evaluative learning about others is unrelated to social anxiety. social evaluation learning is only biased in relation to the self – what drives this effect?
Hobbs et al (in prep)

7. Mechanism driving biased learning
Initial expectations
learning from positive evaluation
learning from negative evaluation
We aimed to create a series of models based on ideas we had about the data but all of the models follow a similar structure, all following a reinforcement learning rule based the the rescorla-wagner update equation. So here what you have the action value for each state, so here we have two actions, choosing the positive word or choosing the negative word and there are values attached to each of these. So if the value attached to choosing the positive word is greater than for the negative word, the model predicts that you will choose the positive word. Every trial these are updated by taking the action value from the previous trial, plus the difference between actual outcome (or the reward, in this case the feedback) minus the predicted outcome, so the reward prediction error. The predicted outcome is simply the value for that chosen outcome.
and this is multiplied by this learning rate parameter, which basically tells you how much attention you are going to pay to the information. So a higher learning rate means this information is going to be more valuable to you and will have a greater impact on the action values.
AS AN EXAMPLE IF I HAVE HIGH ACTION VALUES FOR THE POSITIVE WORD, AND I CHOOSE THAT WORD, BUT THE COMPUTER ACTUALLY CHOSE THE NEGATIVE WORD, THIS REWARD PREDICTION ERROR TERM WOULD BE BIG. HOW MUCH I TAKE THIS INTO ACCOUNT DEPENDS ON THE LEARNING RATE, SO IF THE LEARNING RATE IS HIGH THEN THE VALUE WILL BE CHANGED MORE, IF IT IS LOW, EVEN THOUGH THE RPE WAS BIG, I WONT LEARN THAT MUCH, so my action values wont change that much
TALK ABOUT THE IMPORTANT DIFFERENCES IN THE MODELS
Make the predictions that’s
Learning rate determines how much the information is taked on board, so a higher learning rate would mean that the information

8. Mechanism driving biased learning
Examined correct-repeat behaviour for negative and positive words
p = 0.29
p < 0.001
which corresponds to a 1% increase in negative-correct-repeat behaviour for each unit increase in BFNE score. Increased sensitivity to correct feedback for negative self-referential words
Better updating of belief about negative evaluation (similar to positive)
Explains loss of positive self-referential bias
Enhanced learning from negative evaluation in social anxiety
Button et al (2015) PLoS One. 10(4): p. e

9. Computational modelling of mechanism
Initial expectations
learning rate from positive evaluation
learning rate from negative evaluation
We aimed to create a series of models based on ideas we had about the data but all of the models follow a similar structure, all following a reinforcement learning rule based the the rescorla-wagner update equation. So here what you have the action value for each state, so here we have two actions, choosing the positive word or choosing the negative word and there are values attached to each of these. So if the value attached to choosing the positive word is greater than for the negative word, the model predicts that you will choose the positive word. Every trial these are updated by taking the action value from the previous trial, plus the difference between actual outcome (or the reward, in this case the feedback) minus the predicted outcome, so the reward prediction error. The predicted outcome is simply the value for that chosen outcome.
and this is multiplied by this learning rate parameter, which basically tells you how much attention you are going to pay to the information. So a higher learning rate means this information is going to be more valuable to you and will have a greater impact on the action values.
AS AN EXAMPLE IF I HAVE HIGH ACTION VALUES FOR THE POSITIVE WORD, AND I CHOOSE THAT WORD, BUT THE COMPUTER ACTUALLY CHOSE THE NEGATIVE WORD, THIS REWARD PREDICTION ERROR TERM WOULD BE BIG. HOW MUCH I TAKE THIS INTO ACCOUNT DEPENDS ON THE LEARNING RATE, SO IF THE LEARNING RATE IS HIGH THEN THE VALUE WILL BE CHANGED MORE, IF IT IS LOW, EVEN THOUGH THE RPE WAS BIG, I WONT LEARN THAT MUCH, so my action values wont change that much
TALK ABOUT THE IMPORTANT DIFFERENCES IN THE MODELS
Make the predictions that’s
Learning rate determines how much the information is taked on board, so a higher learning rate would mean that the information
Hopkins et al (in prep)

10. Model 1 - two learning rates
- αpositive, αnegative,
Model 2 – three learning rates
αself positive, αself negative, αother general
Model 3 – two learning rates
- αself positive, αgeneral
We compared a number of models, the main differences were in the learning rates and we found that the model that captured the data best had 3 learning rates, one for self positive information, one for self negative information and general one for learning about others. So what this model is saying is that there is an asymmetry in learning for positive and negative information about the self, but not for the other. So this seems to make sense with respect to the behavioural findings.
Asymmetrical learning rates for positive and negative information about the self describe positive self-bias

11. Implications: healthy self-esteem
Enhanced learning from positive evaluation and discounting negative evaluation may be an important mechanism which maintains positive self-esteem Down-weighting evidence of negative evaluation may be an adaptive function, reducing the risk of escalating social awkwardness or confrontation, and promoting positive social encounters

12. Implications: social anxiety
Enhanced learning of negative evaluation leads to more accurate, but perhaps less adaptive, social learning, consistent with literature on ’depressive realism’ Negative-self beliefs and symptoms reinforced by promoting recall of negative evaluation during ‘post-mortem’ interpretations Interventions to ‘reinstate’ the rose tinted glasses, perhaps by boosting self-esteem, improving self-image, or training self-compassion, may be effective

13. Acknowledgements Marcus Munafò Bristol Glyn Lewis UCL
Michael Browning Oxford
Catherine Hobbs (PhD Student) Bath
Alex Hopkins (PhD Student) UCL
Michael Moutoussis UCL
@ButtonKate