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Through Neural Stimulation to Behavior Manipulation: A Novel Method for Analyzing Dynamical Cognitive Models
Author(s) -
Hope Thomas,
Stoianov Ivilin,
Zorzi Marco
Publication year - 2010
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2009.01079.x
Subject(s) - computer science , categorical variable , cognition , context (archaeology) , artificial intelligence , perception , analogy , dynamical systems theory , monotonic function , cognitive science , cognitive model , machine learning , psychology , mathematics , neuroscience , paleontology , mathematical analysis , linguistics , philosophy , physics , quantum mechanics , biology
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.