Temporal uncertainty during overshadowing: A temporal difference account

Dómhnall J. Jennings, Eduardo Alonso, Esther Mondragón, Charlotte Bonardi

Research output: Chapter in Book/Report/Conference proceedingChapter

9 Citations (Scopus)


Standard associative learning theories typically fail to conceptualise the temporal properties of a stimulus, and hence cannot easily make predictions about the effects such properties might have on the magnitude of conditioning phenomena. Despite this, in intuitive terms we might expect that the temporal properties of a stimulus that is paired with some outcome to be important. In particular, there is no previous research addressing the way that fixed or variable duration stimuli can affect overshadowing. In this chapter we report results which show that the degree of overshadowing depends on the distribution form - fixed or variable - of the overshadowing stimulus, and argue that conditioning is weaker under conditions of temporal uncertainty. These results are discussed in terms of models of conditioning and timing. We conclude that the temporal difference model, which has been extensively applied to the reinforcement learning problem in machine learning, accounts for the key findings of our study.

Original languageEnglish
Title of host publicationComputational Neuroscience for Advancing Artificial Intelligence
Subtitle of host publicationModels, Methods and Applications
PublisherIGI Global
Number of pages10
ISBN (Print)9781609600211
Publication statusPublished - 2011
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)


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