University of Cambridge > Talks.cam > Zangwill Club > An engineering approach to aversive learning.

An engineering approach to aversive learning.

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Louise White.

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.

This talk is part of the Zangwill Club series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity