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* 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<ref name=":3">{{Cite journal|last=Chang|first=Franklin|date=2002|title=Symbolically speaking: a connectionist model of sentence production|url=https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog2605_3|journal=Cognitive Science|language=en|volume=26|issue=5|pages=609–651|doi=10.1207/s15516709cog2605_3|issn=1551-6709}}</ref>, 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 although using fast-variable binding abilities outside of those standardly assumed in connectionist models<ref
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>
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