Welcome to the InfoSci Platform
Temporal Uncertainty During Overshadowing: A Temporal Difference Account

Temporal Uncertainty During Overshadowing: A Temporal Difference Account

Dómhnall J. Jennings, Eduardo Alonso, Esther Mondragón, Charlotte Bonardi
Copyright: © 2011 |Pages: 10
ISBN13: 9781609600211|ISBN10: 1609600215|EISBN13: 9781609600235
DOI: 10.4018/978-1-60960-021-1.ch003
Cite Chapter Cite Chapter

MLA

Jennings, Dómhnall J., et al. "Temporal Uncertainty During Overshadowing: A Temporal Difference Account." Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications, edited by Eduardo Alonso and Esther Mondragón, IGI Global, 2011, pp. 46-55. https://doi.org/10.4018/978-1-60960-021-1.ch003

APA

Jennings, D. J., Alonso, E., Mondragón, E., & Bonardi, C. (2011). Temporal Uncertainty During Overshadowing: A Temporal Difference Account. In E. Alonso & E. Mondragón (Eds.), Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications (pp. 46-55). IGI Global. https://doi.org/10.4018/978-1-60960-021-1.ch003

Chicago

Jennings, Dómhnall J., et al. "Temporal Uncertainty During Overshadowing: A Temporal Difference Account." In Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications, edited by Eduardo Alonso and Esther Mondragón, 46-55. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-021-1.ch003

Export Reference

Mendeley
Favorite

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.