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{{short description|Approach in cognitive science that hopes to explain mental phenomena using artificial neural networks}}
{{more footnotes|date=April 2014}}
'''Connectionism''' is an approach in the fields of [[cognitive science]] that hopes to explain [[mind|mental]] phenomena using [[artificial neural networks]] (ANN).<ref name=":0">{{cite book|url=https://plato.stanford.edu/archives/fall2018/entries/connectionism/|title=The Stanford Encyclopedia of Philosophy|first=James|last=Garson|editor-first=Edward N.|editor-last=Zalta|date=27 November 2018|publisher=Metaphysics Research Lab, Stanford University|via=Stanford Encyclopedia of Philosophy}}</ref> Connectionism presents a cognitive theory based on simultaneously occurring, distributed signal activity via connections that can be represented numerically, where learning occurs by modifying connection strengths based on experience.<ref name=":1">{{Cite journal|last=Smolensky|first=Paul|date=1999|title=Grammar-based Connectionist Approaches to Language|url=http://csjarchive.cogsci.rpi.edu/1999v23/i04/p0589p0613/MAIN.PDF|journal=Cognitive Science|volume=23|issue=4|pages=589–613|via=|doi=10.1207/s15516709cog2304_9}}</ref> Some advantages of the connectionist approach include its applicability to a broad array of functions, structural approximation to biological neurons, low requirements for innate structure, and capacity for [[graceful degradation]].<ref name=":2">{{Cite book|title=The Algebraic Mind: Integrating Connectionism and Cognitive Science (Learning, Development, and Conceptual Change)​|url=https://archive.org/details/algebraicmindint00marc_403|url-access=limited​|last=Marcus|first=Gary F.|publisher=MIT Press|year=2001|isbn=978-0262632683|location=Cambridge, Massachusetts|pages=​27–28​[https://archive.org/details/algebraicmindint00marc_403/page/n43 27]–28}}</ref> Some disadvantages include the difficulty in deciphering how ANNs process information and a resultant difficulty explaining phenomena at a higher level.<ref name=":1" /> The success of [[deep learning]] networks in the past decade has greatly increased the popularity of this approach, but the complexity and scale of such networks has brought with them increased [[Explainable artificial intelligence|interpretability problems]].<ref name=":0" /> Connectionism is seen by many to offer an alternative to classical theories of mind based on symbolic computation, but the extent to which the two approaches are compatible has been the subject of much debate since their inception.<ref name=":0" />
[[File:Artificial_neural_network.svg|thumb|Connectionist (ANN) model with a hidden layer]]
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