Impulsivity and Cognitive Distortions in Pathological Gambling Dr - - PowerPoint PPT Presentation

Impulsivity and Cognitive Distortions in Pathological Gambling Dr Luke Clark Department of Experimental Psychology University of Cambridge, U.K.

The Psychology of Gambling

• 1. How do we explain the prevalence of gambling if

people understand that ‘the house always wins’?

• 2. How does gamble become dysfunctional (addictive?)

in a minority?

Cognitive distortions during gambling Brain mechanisms of decision-making and reward processing Emotional / physiological responses in the body

The Cognitive Approach to Gambling

• Gamblers experience distorted processing of

probability and randomness, such that they over- estimate their chances of winning

• Distortions elevated in problem gamblers

10 20 30 40 50 60 70 80

Gamblers Controls

Total Score

Gambling-Related Cognitions Scale

• Two basic types:

1) Sequential predictions based

• n independence of turns

2) Mistaken appraisals of skill due to perceived personal control

Clark (2010 Proc Roy Soc B), Michalczuk et al (2011)

The ‘Gambler’s Fallacy’ in Simulated Roulette

Simple task:

• Guess RED or BLACK
• Then, rate your

confidence Black, Black, Black, Black  “RED!” (i.e. negative recency)

Studer & Clark (in prep)

Choose red after Choose red after

The ‘Gambler’s Fallacy’ in Simulated Roulette

20 25 30 35 40 45 50 1 2 3 4 5 % same as previous outcome

Consecutive Reds / Blacks

Confidence after Loss Loss Confidence after Loss Loss Loss Loss

25 30 35 40 45 50

Short (1,2) Long (4,5) % Choice of Previous Outcome Run Length

• 0.1

0.1 0.2 0.3 0.4 0.5

Short (1,2) Long (4,5) Z(Confidence Rating) Losing Streak

Near-Misses

2 4 6 8 10 12 14 15% 30% 45% Near-Miss Frequency Trials in Extinction

“A special kind of failure to reach a goal,

• ne that comes close to being successful”

(Reid 1986) Kassinove & Schare 2001

Near-Misses in a Simulated Slot Machine

Selection - Anticipation - Outcome

• 0.6
• 0.5
• 0.4
• 0.3
• 0.2
• 0.1

0.1 NearMiss FullMiss

Z score of rating

"Continue to play?" "Pleased with

• utcome?"

Subjective Differences between Near- Misses and Full-Misses

Clark et al (2009 Neuron)

Arousal Responses to Wins and Near-Misses

• 0.005

0.005 0.01 0.015 0.02 0.025 0.03 0.035

1 2 3 4 5 6

SCR Change from Baseline (log + 1) Time post-outcome (2s bins) Participant - WINS All Non-Wins

• 0.004
• 0.002

0.002 0.004 0.006 0.008 0.01 0.012

1 2 3 4 5 6

SCR Change from Baseline (log + 1) Time post-outcome (2s bins) Participant - NEAR Participant - FULL

Clark et al (2011 Journal of Gambling Studies)

fMRI Responses to Wins and Near-Misses

P<.05 FWE Dopaminergic Midbrain Anterior Insula Ventral Striatum mPFC WINNING OUTCOMES minus ALL NON-WIN OUTCOMES NEAR-MISS OUTCOMES minus FULL-MISS OUTCOMES

A B

P<.001 uncorr

Clark et al (2009 Neuron)

Gambling Severity predicts Near-Miss Activity in Midbrain

• 1
• 0.5

0.5 1 1.5

5 10 15 20

Percent Signal Change

SOGS

re-smoothed at 4mm

Chase & Clark (2010 J Neurosci)

‘Close only counts in horseshoes and hand grenades’

Horseshoes Game of skill Near-misses provide indication of skill acquisition, and thus likelihood

• f future success

Should be valued by brain reward system Fruit machine Game of chance Near-misses provide no indication of future success Should be ignored by brain

Griffiths (1993), Reid (1986)

• Gambling distortions can be elicited in healthy individuals in a

laboratory environment (Gambler’s Fallacy, effects of near- misses)

• Near-miss outcomes are experienced as unpleasant but invigorate

gambling behaviour

• Wins and near-misses are associated with phasic changes in

peripheral arousal

• At a neural level, near-misses trigger anomalous activation in

components of the brain reward system: VS, insula, vmPFC.

• The size of these near-miss responses predicts susceptibility to

gambling distortions in healthy volunteers (insula) and severity of gambling involvement in regular gamblers (midbrain)

• No evidence for changes in (baseline) dopamine D2 receptors in

PG, but correlations with impulsivity

Conclusions

Acknowledgements

University of Cambridge Andrew Lawrence Rosanna Michalczuk Henry Chase Mike Aitken Barbara Sahakian Trevor Robbins Barney Dunn (MRC CBU) Funding support: Medical Research Council MRC – Wellcome Trust Behavioural and Clinical Neuroscience Institute Economic and Social Research Council Responsibility in Gambling Trust (now RGF) Imperial College, London Henrietta Bowden-Jones Paul Stokes Anne Lingford-Hughes Kit Wu Robert Rogers (Oxford) Antonio Verdejo (U Granada)