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An engineering approach to aversive learning.

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Humans and animals learn to predict and escape from or avoid potential threats by a combination of Pavlovian and instrumental learning. In recent years, reinforcement learning theory has provided a computational framework that has allowed mechanistic, quantitative models of the underlying learning processes, and this can be used to understand the underlying brain systems that implement them (for example, using model-based fMRI). This has been used to identify, amongst other things, a key roe for the striatum in the representation of Pavlovian and instrumental prediction errors for punishments. More recently, we have been trying to understand how such accounts of motivational learning control not only responses and actions towards punishments, but also the subjective experience of them: for instance in the case of pain, asking whether endogenous analgesia comes under the control aversive motivational systems. This sort of account should yield innovation of novel interventional strategies to treat disorders of pain and fear.

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