Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. Here, we tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus-stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across t transition prevents normal stimulus-stimulus learning. These results establish that the acquisition of model-based information about transitions between non-rewarding events is also driven by prediction errors, and that contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts.
Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning.
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