Learning Theory as an Explanation for Gambling Addiction (AQA A Level Psychology): Revision Note

The role of partial reinforcement in gambling addiction

• As with nicotine addiction, social learning theory can be used to explain why people start to gamble

• Individuals observe role models gambling and see the rewarding consequences of the behaviour, and this does not have to be direct observation of other people's behaviour

  • Newspapers, magazines and other media report positively on big lottery winners, or broadcast the glamour and excitement of horse-racing, whichmay be enough to trigger a desire for the same reinforcement in someone who has never gambled before

• This vicarious reinforcement leads them to imitate the gambling behaviour to get the same reinforcement

• Gambling continues through the process of operant conditioning, which suggests that behaviour that is positively reinforced (rewarded) is likely to be repeated

  • Winning money is an obvious positive reinforcer of continued betting and wagering, and the 'buzz' that accompanies a gamble is also reinforcing because it is exciting

  • Gambling can also be an escape for many, albeit a temporary one, and this is negative reinforcement to the extent that it offers a distraction from aversive stimuli such as the anxieties of everyday life

• Under a continuous reinforcement schedule, which rewards every desired response from the animal, behaviour does not lead to the most persistent behaviour; once the rewards stop, the desired behaviour quickly disappears, a process known as extinction

  • Partial reinforcement, on the other hand, does create the kind of persistent behaviour seen in gambling, as the gambler is unpredictably rewarded and this keeps the behaviour going even when rewards are hard to come by

• There are four different schedules of partial reinforcement:

  • Fixed-interval – after regular intervals of time

  • Variable interval – after varying and unpredictable intervals 

  • Fixed ratio – after a set number of attempts

  • Variable ratio – after a varying and unpredictable number of attempts

The role of variable reinforcement in gambling addiction

• Skinner's research with rats and pigeons demonstrated that of all the ways in which behaviour can be reinforced intermittently, the variable schedule produces the most persistent behaviour

  • Variable reinforcement is provided after an always-changing proportion of wagers are placed

    • E.g. a slot machine might pay out after an average of 25 spins, but not on every 25th spin; the first payout might come after the 11th spin, the second after the 21st, the third after the 38th

• In the case of gambling, the individual has no way to predict when they will win or lose and therefore, gambling is an example of a behaviour subject to partial reinforcement on a variable-ratio schedule

• Slot machines are manufactured to follow a variable-ratio schedule to ensure gamblers will continue to play

  • This is a highly unpredictable pattern of reinforcement, which takes longer for learning to be established but once established is much more resistant to extinction

    • It is possible to go for many spins with no payout at all, but the gambler continues to place wagers for a long time even when the behaviour is no longer reinforced, which helps to explain why some people continue to gamble despite experiencing big losses

Research which investigates the learning theory of gambling addiction

• Parke & Griffiths (2004) investigated the theory of operant conditioning as an explanation for gambling addiction and found that the sensation of a ‘near miss’ (which is really a loss) often experienced by gamblers reinforced gambling behaviour as much as winning did, making gambling highly addictive

• Wardle et al. (2007) conducted the British Gambling Prevalence Survey and found the prevalence of gambling addiction was 6.57 times higher in individuals who also had parents who were addicted to gambling, supporting social learning theory as an explanation for starting gambling 

Evaluation of the learning theory of gambling addiction

Strengths

• Learning theory explains a common phenomenon; how gamblers persistently fail to stop gambling whilst at the same time being absolutely determined to do so

  • Gambling is maintained through conditioning processes that require no active effort to learn, so the gambler's conscious desire to give up conflicts with motivational forces they are not aware of

• Learning theory has practical applications, as treatments such as aversion therapy and covert sensitisation are derived directly from conditioning principles

  • If gambling is maintained by reinforcement and cues, then treatment can work by breaking those associations rather than relying on willpower alone

Limitations

• Griffiths & Delfabbro (2001) argue that conditioning does not work the same way in everyone, as responses to identical cues differ, motivations differ, e.g. some gamble to relax, others for arousal and some abstainers relapse while others exposed to the same cues do not

  • These individual differences are difficult for learning theory to explain without invoking cognitive features such as distorted thinking

• Learning theory explains some types of gambling better than others, as games vary in the balance of skill and chance, with fruit machines and scratchcards down entirely to chance whereas poker involves real skill

  • Conditioning requires the bet and the outcome to occur close together, so games with a significant delay between the two are harder to explain in these terms

Issues & Debates

• Learning theory suggests that gambling behaviour is entirely determined by environmental cues and reinforcement, not free choice

  • This implies gamblers are passive responders to external triggers (e.g., slot machine sounds or wins), undermining the idea of free will

• The explanation is environmentally reductionist as it focuses only on stimulus-response mechanisms and ignores cognitive factors (e.g., irrational beliefs, distorted thinking) or biological predispositions

  • Gambling addiction is complex, and learning theory oversimplifies it