From symbols to icons the return of resemblance in the cognitive neuroscience revolution

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini (Synthese 193(5):1509–1534. doi:10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons...

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Bibliographic Details
Published inSynthese (Dordrecht) Vol. 195; no. 5; pp. 1941 - 1967
Main Authors Williams, Daniel, Colling, Lincoln
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Science + Business Media 01.05.2018
Springer Netherlands
Springer Nature B.V
Subjects
Bayesian analysis
Cognition
Cognition & reasoning
Cognitive psychology
Education
Epistemology
Information processing
Logic
Metaphysics
Neuropsychology
Neurosciences
Philosophy
Philosophy of Language
Philosophy of Science
Predictions
S.I. : Neuroscience and Its Philosophy
Predictive brain
Cognitive neuroscience revolution
Representational similarity analysis
Homeostasis
Iconic
Free energy principle
Bayesian brain
Mechanistic explanation
Cognitive representation
Predictive processing
Structural resemblance
Emulation
Information-processing
Bayesian networks
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Summary:We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini (Synthese 193(5):1509–1534. doi:10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves (“from below”), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction (“from above”). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research.
ISSN:0039-7857
1573-0964
DOI:10.1007/s11229-017-1578-6