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Summary A Level Psychology 'Nicotine Addiction learning theory explanation' (AQA)

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Psychology notes that gained me an A* at A Level. I created this using textbooks, mark schemes, class notes and my own further research to provide a comprehensive set of notes. This is formatted according to the AQA psychology specification, where each point is covered, but regardless of the exam board, it provides a detailed set of notes including AO1, AO2 and AO3 points!

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Explanations for nicotine addiction: learning theory
learning theory suggests that smoking is learnt through conditioning (classical
and operant).
Operant conditioning – smoking behaviour is reinforced.
1 positive reinforcement: if the consequence of a behaviour is rewarding to an
individual (pleasure) the behaviour is more likely to reoccur. Nicotine is a
powerful reinforcer because of its physiological effects on the dopamine
reward system on the mesolimbic pathway. The drug stimulates release of
dopamine in the nucleus accumbens producing a feeling of euphoria which the
smoker finds rewarding which positively reinforces their smoking behaviour.
2 negative reinforcement: a smoker’s continuing dependence on nicotine
explained by this. Cessation of nicotine leads to withdrawal syndrome which
can include behavioural effects (agitation, disturbed sleep), cognitive effects
(Poor concentration), mood disturbances (anxiety and depression). These
symptoms make it hard to abstain so long so they reduce or eliminate these
effects by smoking again. Smoking another cig is negatively reinforcing because
it stops an unpleasant stimulus.
Role of cue reactivity (CC): pleasurable effect of smoking is a primary
reinforcer because its intrinsically rewarding (not learned) due to its effects
on the brains dopamine reward system meaning likely to smoke again. Any
stimuli present at the same time or before become associated with this
pleasurable effect – secondary reinforcers as they take on the properties of the
primary reinforcer and become rewarding in their own right. If smoking in

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