State-Trace Analysis of Sequence Learning by Recurrent Networks

Fayme Yeates, Andy Wills, Fergal Jones, Ian McLaren

Research output: Chapter in Book/Report/Conference proceedingConference proceedings published in a bookpeer-review

Abstract

This study investigated the use of state-trace analysis (Bamber, 1979) when applied to computational models of human learning. We aimed to investigate the performance of simple recurrent networks (SRNs) on a sequence learning task. Elman’s (1990) SRN and Cleeremans & McClelland’s (1991) Augmented SRN are both benchmark models of human sequence learning. The differences between these models, comprising of an additional learning parameter and the use of response units activated by output units constituted our main manipulation. The results are presented as a state-trace analysis, which demonstrates that the addition of an additional type of weight component, and response units to a SRN produces multi-dimensional state-trace plots. However, varying the learning rate parameter of the SRN also produced two functions on a state-trace plot, suggesting that state-trace analysis may be sensitive to variation within a single process.

Original languageEnglish
Title of host publicationBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012
EditorsNaomi Miyake, David Peebles, Richard P. Cooper
PublisherThe Cognitive Science Society
Pages2581-2586
Number of pages6
ISBN (Electronic)9780976831884
Publication statusPublished - 2012
Event34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012 - Sapporo, Japan
Duration: 1 Aug 20124 Aug 2012

Publication series

NameBuilding Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012

Conference

Conference34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012
Country/TerritoryJapan
CitySapporo
Period1/08/124/08/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • Augmented SRN
  • Learning
  • sequence learning
  • SRN
  • state-trace analysis

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