- Garson, James. Connectionism. The Stanford Encyclopedia of Philosophy. stanford:connectionism.
- Gary F. Marcus (2001). The Algebraic Mind : Integrating Connectionism and Cognitive Science MIT Press. isbn:0262133792
- David Chalmers. (1990). Syntactic Transformations on Distributed Representations. Connection Science, 2, Nos 1 & 2, 53-62.
- van Gelder, T. J. (1999). Distributed versus local representation. In R. Wilson & F. Keil (Eds.) The MIT Encyclopedia of Cognitive Sciences. Cambridge MA: MIT Press, 236-8.
- Ramsey, W., Stich, S. P., & Garon, J. (1991). Connectionism, eliminativism, and the future of folk psychology. In W. Ramsey, S. P. Stich, & D. E. Rumelhart, Eds. (1991). Philosophy and connectionist theory. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
Fodor and Pylyshyn
Is connectionism an alternative to LOT?
- Connectionism is a theory at the hardware level. It tells us how classical cognitive architectures are neurally implemented.
- Connectionism tells us how a classical architecture is implemented by connectionist networks at a lower level of algorithm and representation.
- Connectionism denies the existence of a classical architecture. The architecture at the level of algorithm and representation is a connectionist one.
- Connectionism is a theory at the level of algorithm and representation. But the correct cognitive architecture is a hybrid one that includes both classical and connectionist architectures.
Regarding scenario #1
See the discussion in Crick, F (1989). The recent excitement about neural networks Nature, 337, 129-132. doi:10.1038/337129a0
- Most connectionist networks are biologically unrealistic in many ways.
- Neural connections are either excitory or inhibitory, but not both.
- Many training rules are biologically unrealistic. For example, back propagation does not scale well, and cannot deal with one-shot learning.
- Real neural networks may have lots of recurrent connections, unlike feed-forward networks.
Regarding scenario #2
- Connectionist representations: localist vs distributed.
- Localist representation - one node for one meaning. Can it deal with systematicity and productivity?
van der Veldea & de Kamp. Neural blackboard architectures of combinatorial structures in cognition. Behavioral and Brain Sciences
Distributed memory in Ramsey, W., Stich, S. P., & Garon, J. (1991).
Connectionism: friend or foe?
Regarding scenario #3
Distributed representations are powerful and useful. But can they explain cognition without LOT?
- Objection #1: Unstructured distributed representations cannot explain systematicity.
- See Chalmers (1990) for a reply.
- Objection #2: Where do distributed representations come from? (e.g. RSG model, RAAM)
- Objection #3: LOT needed to explain free transformation in central cognitive processes (e.g. conscious thoughts).
Compare: "Where do zip files come from?", "How can you change one file in a zip archive without changing others?"
Regarding scenario #4
- An example of a hybrid approach - LOT in working memory vs unstructured representations in long-term memory.
- Is this an example of LOT?