Connectionism: Difference between revisions
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Connectionism (edit)
Revision as of 01:53, 14 November 2019
Removed comma from first sentence in introduction. Expanded introduction to include a broad summary of theory, advantages and disadvantages of the approach, the effects of the more recent developments of deep learning, and description of the debate with classical theory. Added citations/links. Biological realism: Added that neural networks are derived from biological neurons; included link/citation. Added example of integrative architecture in debate section, as well as emergentist explanation.
{{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|pages=589-613|via=}}</ref> Some advantages of the connectionist approach include is 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)|last=Marcus|first=Gary F.|publisher=MIT Press|year=2001|isbn=978-0262632683|location=Cambridge, Massachusetts|pages=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]]
===Biological realism===
Connectionist work in general does not need to be biologically realistic and therefore suffers from a lack of neuroscientific plausibility.<ref>{{Cite web|url=http://www.encephalos.gr/48-1-01e.htm|title=Encephalos Journal|website=www.encephalos.gr|access-date=2018-02-20}}</ref><ref>{{Cite book|url=https://books.google.com/books?id=s-OCCwAAQBAJ&pg=PT18&lpg=PT18&dq=%22accurate%22&f=false#v=onepage&q=%22accurate%22&f=false|title=Neural Geographies: Feminism and the Microstructure of Cognition|last=Wilson|first=Elizabeth A.|date=2016-02-04|publisher=Routledge|isbn=9781317958765|language=en}}</ref><ref>{{Cite web|url=https://pdfs.semanticscholar.org/e953/59bc80e624a963a3d8c943e3b2898a397ef7.pdf|title=Organismically-inspired robotics: homeostatic adaptation and teleology beyond the closed sensorimotor loop|last=|first=|date=|website=|access-date=}}</ref><ref>{{Cite journal|last=Zorzi|first=Marco|last2=Testolin|first2=Alberto|last3=Stoianov|first3=Ivilin P.|date=2013-08-20|title=Modeling language and cognition with deep unsupervised learning: a tutorial overview|journal=Frontiers in Psychology|volume=4|doi=10.3389/fpsyg.2013.00515|issn=1664-1078|pmc=3747356|pmid=23970869}}</ref><ref>{{Cite web|url=http://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1015&context=comparativephilosophy|title=ANALYTIC AND CONTINENTAL PHILOSOPHY|last=|first=|date=|website=|access-date=}}</ref><ref>{{Cite book|url=https://books.google.com/books?id=uV9TZzOITMwC&pg=PA17&lpg=PA17&dq=%22biological%20plausibility%22&f=false#v=onepage&q=%22biological%20plausibility%22&f=false|title=Neural Network Perspectives on Cognition and Adaptive Robotics|last=Browne|first=A.|date=1997-01-01|publisher=CRC Press|isbn=9780750304559|language=en}}</ref><ref>{{Cite book|url=https://books.google.com/books?id=7pPv0STSos8C&pg=PA63&lpg=PA63&dq=%22biological+realism%22#v=onepage&q=%22biological%20realism%22&f=false|title=Connectionism in Perspective|last=Pfeifer|first=R.|last2=Schreter|first2=Z.|last3=Fogelman-Soulié|first3=F.|last4=Steels|first4=L.|date=1989-08-23|publisher=Elsevier|year=|isbn=9780444598769|location=|pages=|language=en}}</ref​> However, the structure of neural networks are derived from that of biological [[Neuron|neurons]], and this parallel in low-level structure is often argued to be an advantage of connectionism in modeling cognitive structures compared with other approaches.<ref name=":2" />
* Computationalists often posit [[domain specificity|domain specific]] symbolic sub-systems designed to support learning in specific areas of cognition (e.g., language, intentionality, number), whereas connectionists posit one or a small set of very general learning-mechanisms.
Despite these differences, some theorists have proposed that the connectionist architecture is simply the manner in which organic brains happen to implement the symbol-manipulation system. This is logically possible, as it is well known that connectionist models can implement symbol-manipulation systems of the kind used in computationalist models,{{Citation needed|date=February 2011}} as indeed they must be able if they are to explain the human ability to perform symbol-manipulation tasks. Several cognitive models combining both symbol-manipulative and connectionist architectures have been proposed, notably among them [[Paul Smolensky]]'s Integrated Connectionist/Symbolic Cognitive Architecture (ICS).<ref name=":0" /><ref>{{Cite journal|last=Smolensky|first=Paul|date=1990|title=Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems|url=http://www.lscp.net/persons/dupoux/teaching/AT1_2012/papers/Smolensky_1990_TensorProductVariableBinding.AI.pdf|journal=Artificial Intelligence|volume=46|pages=159-216|via=}}</ref> But the debate rests on whether this symbol manipulation forms the foundation of cognition in general, so this is not a potential vindication of computationalism. Nonetheless, computational descriptions may be helpful high-level descriptions of cognition of logic, for example.
The debate was largely centred on logical arguments about whether connectionist networks could produce the syntactic structure observed in this sort of reasoning. This was later achieved,{{Citation needed|date=February 2011}} although using processes unlikely to be possible in the brain,{{Citation needed|date=February 2011}} thus the debate persisted. {{As of | 2016}}, progress in neurophysiology and general advances in the understanding of neural networks have led to the successful modelling of a great many of these early problems, and the debate about fundamental cognition has, thus, largely been decided among neuroscientists in favour of connectionism.{{citation needed|date=March 2012}} However, these fairly recent developments have yet to reach consensus acceptance among those working in other fields, such as psychology or philosophy of mind.
Part of the appeal of computational descriptions is that they are relatively easy to interpret, and thus may be seen as contributing to our understanding of particular mental processes, whereas connectionist models are in general more opaque, to the extent that they may be describable only in very general terms (such as specifying the learning algorithm, the number of units, etc.), or in unhelpfully low-level terms. In this sense connectionist models may instantiate, and thereby provide evidence for, a broad theory of cognition (i.e., connectionism), without representing a helpful theory of the particular process that is being modelled. In this sense the debate might be considered as to some extent reflecting a mere difference in the level of analysis in which particular theories are framed. Some researchers suggest that the analysis gap is the consequence of connectionist mechanisms giving rise to [[Emergence|emergent phenomena]] that may be describable in computational terms.<ref>{{Cite journal|last=Ellis|first=Nick C.|date=1998|title=Emergentism, Connectionism and Language Learning|url=http://www-personal.umich.edu/~ncellis/NickEllis/Publications_files/Emergentism.pdf|journal=Language Learning|volume=48:4|pages=631-664|via=}}</ref>
The recent{{when|date=February 2016}} popularity of [[Cognitive Model#Dynamical systems|dynamical systems]] in [[philosophy of mind]] have added a new perspective on the debate; some authors{{which|date=February 2016}} now argue that any split between connectionism and computationalism is more conclusively characterized as a split between computationalism and [[Cognitive Model#Dynamical systems|dynamical systems]].
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