Welcome to the InfoSci Platform
Reference Hub1
Application of Connectionist Models to Animal Learning: Interactions between Perceptual Organization and Associative Processes

Application of Connectionist Models to Animal Learning: Interactions between Perceptual Organization and Associative Processes

Robert C. Honey, Christopher S. Grand
Copyright: © 2011 |Pages: 14
ISBN13: 9781609600211|ISBN10: 1609600215|EISBN13: 9781609600235
DOI: 10.4018/978-1-60960-021-1.ch001
Cite Chapter Cite Chapter

MLA

Honey, Robert C., and Christopher S. Grand. "Application of Connectionist Models to Animal Learning: Interactions between Perceptual Organization and Associative Processes." Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications, edited by Eduardo Alonso and Esther Mondragón, IGI Global, 2011, pp. 1-14. https://doi.org/10.4018/978-1-60960-021-1.ch001

APA

Honey, R. C. & Grand, C. S. (2011). Application of Connectionist Models to Animal Learning: Interactions between Perceptual Organization and Associative Processes. In E. Alonso & E. Mondragón (Eds.), Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications (pp. 1-14). IGI Global. https://doi.org/10.4018/978-1-60960-021-1.ch001

Chicago

Honey, Robert C., and Christopher S. Grand. "Application of Connectionist Models to Animal Learning: Interactions between Perceptual Organization and Associative Processes." In Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications, edited by Eduardo Alonso and Esther Mondragón, 1-14. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-021-1.ch001

Export Reference

Mendeley
Favorite

Abstract

Here the authors examine the nature of the mnemonic structures that underlie the ability of animals to learn configural discriminations that are allied to the XOR problem. It has long been recognized that simple associative networks (e.g., perceptrons) fail to provide a coherent analysis for how animals learn this type of discrimination. Indeed “The inability of single layer perceptrons to solve XOR has a significance of mythical proportions in the history of connectionism.” (McLeod, Plunkett & Rolls, 1998; p. 106). In this historic context, the authors describe the results of recent experiments with animals that are inconsistent with the theoretical solution to XOR provided by some multi-layer connectionist models. The authors suggest a modification to these models that parallels the formal structure of XOR while maintaining two principles of perceptual organization and learning: contiguity and common fate.

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.