Connectionism is an alternate computational paradigm to that provided by the von Neumann architecture that has inspired classical cognitive science (Bechtel & Abrahamsen, 2002; Dawson, 1998). Originally taking its inspiration from the biological neuron and neurological organization, it emphasizes collections of simple processing elements in place of the centrally-controlled manipulation of symbols by rules that is typical in classical cognitive science. The simple processing elements in connectionism are typically only capable of rudimentary calculations (such as summation),.
A connectionist network is a particular organization of processing units into a whole network. In most connectionist networks, the systems are trained using a learning rule to adjust the weights of all connections between processors in order to obtain a network that performs some desired input-output mapping.
Connectionist networks offer many advantages as models in cognitive science (Dawson, 2004). However, in spite of the fact that connectionism arose as a reaction against the assumptions of classical cognitive science, the two approaches have many similarities when examined from the perspective of Marr's tri-level hypothesis (Dawson, 1998).
- Bechtel, W., & Abrahamsen, A. A. (2002). Connectionism And The Mind : Parallel Processing, Dynamics, And Evolution In Networks (2nd ed.). Malden, MA: Blackwell.
- Dawson, M. R. W. (1998). Understanding Cognitive Science. Oxford, UK: Blackwell.
- Dawson, M. R. W. (2004). Minds And Machines : Connectionism And Psychological Modeling. Malden, MA: Blackwell Pub.