Computational-representational understanding of mind: Difference between revisions

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'''Computational representational understanding of mind''' ('''CRUM''') is a [[hypothesis]] in [[cognitive science]] which proposes that thinking is performed by [[computations]] operating on representations. This hypothesis assumes that the mind has [[mental representation]]s analogous to [[data structures]] and computational procedures analogous to [[algorithms]], such that computer programs using algorithms applied to data structures can model the mind and its processes<ref>{{Cite book|last=Lieto|first=Antonio|title=Cognitive Design for Artificial Minds|year=2021|___location=London, UK | publisher=Routledge, Taylor & Francis | isbn=9781138207929}}</ref>..

CRUM takes into consideration several theoretical approaches of understanding human [[cognition]], including [[logic]], [[:wikt:rule|rule]], [[concept]], [[analogy]], image, and connection connectionist-based systems based on [[artificial neural networks]]. These serve as the representation aspects of CRUM theory which are then acted upon to simulate certain aspects of human cognition, such as the use of [[rule-based systems]] in [[neuroeconomics]].

There is much disagreement on this hypothesis, but CRUM has high regard among some researchers {{citation needed|reason=Need link to sources|date=April 2016}}. Philosopher [[Paul Thagard]] called it "the most theoretically and experimentally successful approach to mind ever developed".<ref>{{Cite book|title=Mind: Introduction to Cognitive Science|last=Thagard|first=Paul|publisher=The MIT Press|year=2005|isbn=9780262701099|pages=11}}</ref>