Through neural stimulation to behavior manipulation: A novel method for analyzing dynamical cognitive models

Thomas Hope, Ivilin Stoianov, Marco Zorzi

Research output: Contribution to journalArticle

Abstract

The dynamical systems' approach to cognition (Dynamicism) promises computational models that effectively embed cognitive processing within its more natural behavioral context. Dynamical cognitive models also pose difficult, analytical challenges, which motivate the development of new analytical methodology. We start by illustrating the challenge by applying two conventional analytical methods to a well-known Dynamicist model of categorical perception. We then introduce our own analysis, which works by analogy with neural stimulation methods, and which yields some novel insights into the way the model works. We then extend and apply the method to a second Dynamicist model, which captures the key psychophysical trends that emerge when humans and animals compare two numbers. The results of the analysis-which reveals units with tuning functions that are monotonically related to the magnitudes of the numbers that the agents must compare-offer a clear contribution to the contentious debate concerning the way number information is encoded in the brain.

Original languageEnglish
Pages (from-to)406-433
Number of pages28
JournalCognitive Science
Volume34
Issue number3
DOIs
Publication statusPublished - Apr 2010

Keywords

  • Analysis
  • Dynamical systems
  • Evolution
  • Genetic algorithms
  • Recurrent neural networks

ASJC Scopus subject areas

  • Language and Linguistics
  • Artificial Intelligence
  • Cognitive Neuroscience
  • Experimental and Cognitive Psychology

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