A memory-based account of retrospective revaluation.

We adapt an instance model of human memory, Minerva 2, to simulate retrospective revaluation. In the account, memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and causes each to become active. The information retrieved from memory is the sum of the activated traces. Learning is modelled as a process of cued-recall; encoding is modelled as a process of differential encoding of unexpected features in the probe (i.e., expectancy-encoding). The model captures three examples of retrospective revaluation: backward blocking, recovery from blocking, and backward conditioned inhibition. The work integrates an understanding of human memory and complex associative learning.