Do reward cues push us to approach rather than just to act?
Cues that predicted money made people more likely to approach but less likely to withdraw, so Pavlovian cues bias specific kinds of action rather than simply energising behaviour.
Source
Disentangling the roles of approach, activation and valence in instrumental and pavlovian responding
Study at a glance
- Design
- Human experiment — Within-subject go/nogo task in approach and withdrawal blocks, with instrumental training, Pavlovian conditioning and a transfer test; choices fit by RL models compared with group-level Bayesian model selection.
- N
- N=46 · 46 healthy adults analysed after exclusions from 54 recruited; 24 did the 'throwaway' and 22 the 'release' withdrawal version.
- Population
- Healthy adult volunteers from the Berlin area
- Outcome
- Probability of go versus nogo responses under Pavlovian background cues; fitted RL model parameters (learning rate, go biases, reward and punishment sensitivity, Pavlovian weights)
Structured fields used in claim comparison tables when every cited study has a complete layer.
What they did
Adults learned by trial and error whether to act (go) or hold back (nogo) for mushroom pictures, in one block where acting meant collecting the mushroom and one where acting meant getting rid of it. They were separately taught that fractal images with tones predicted monetary gains or losses. In a final test those images appeared behind the mushrooms without feedback. The authors fitted a family of reinforcement-learning models to every choice and picked the most parsimonious one by group-level Bayesian model comparison.
What they found
Reward-predicting backgrounds increased go responses when go meant approach but reduced them when go meant withdrawal, and loss-predicting backgrounds did the opposite; the approach effect appeared in 45/46 people. People had a fixed bias against active withdrawal, but the model with separate learning parameters for approach and withdrawal was rejected, so learning itself did not differ. Rewards moved learning much more than punishments, whose average effect was indistinguishable from zero. The best model predicted choices with an overall predictive probability of 0.7544.
The limits
What it doesn't show
The task cannot tell whether the effect comes from the verbal label of the action ('collect' versus 'throw away') or from the physical movement toward or away from the stimulus, and the authors did not measure participants' insight. The money at stake was small and the stimuli artificial, so it is unclear how far the result generalises to real-world approach and avoidance. Neural and genetic mechanisms (dopamine, serotonin) are only discussed as predictions; nothing biological was measured, and questionnaire measures of anxiety and depression showed no link to the model parameters.
Key terms
- Pavlovian-instrumental transfer (PIT)
- When a cue that predicts reward or punishment, learned without any action, changes how often a separately learned action is performed.
- Instrumental learning
- Learning which action to take from the outcomes that action produces.
- Go/nogo task
- A task in which the correct response is either to make an action or to withhold it, which lets researchers separate what an action does from simply being active.
- Bayesian model comparison
- Choosing between candidate models by weighing how well each fits the data against how many free parameters it uses.
- Softmax choice rule
- A function that turns the values of options into choice probabilities, so higher-valued options are chosen more often but not always.
Flashcards
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Quiz yourself
What effect did a loss-predicting background cue have on go responses when go meant approaching the mushroom?
Common questions
Why did the design reward both go and nogo responses?
So that acting and not acting, and approach and withdrawal, were linked to rewards equally often; any Pavlovian effect then could not be explained by one action simply having been more rewarded.
Does 'punishment insensitivity' mean people ignored losses entirely?
No. They learned the Pavlovian loss cues well; it was only in the instrumental learning that punishments had much less effect than rewards, and a zero outcome still acts somewhat like a punishment relative to expectations.
How do the Pavlovian and instrumental parts combine in the model?
Their influences are added together before the softmax, like two experts each voting for the action they prefer.
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