To the overarching question in this special issue as to whether it is wise to pursue an inclusive pluralist approach to dynamical and symbolic theoretical frameworks in cognitive science, Rick Dale...

We introduce symbolic dynamics to cognitive scientists with the aim of furthering constructive debate on representation. Symbolic dynamics is a mathematical framework in which both continuous and discrete states of a system can be considered jointly. We discuss a number of theoretical implications this framework has for cognitive science, and offer some consideration of the way in which it might be employed for comparing or conciliating discrete and continuous representational theories. Symbolic dynamics may thus serve as a common, level playing field for debate in theories of cognitive representation.

Catastrophic interference is addressed as a problem that arises from pattern-based learning algorithms. As such, it is not limited to artificial neural networks but can be demonstrated in human subjects in so far as they use a pattern-based learning strategy. The experiment tests retroactive interference in humans learning lists of consonant-vowel-consonant nonsense syllable pairs. Results show significantly more interference for subjects learning patterned lists than subjects learning arbitrarily paired lists. To examine how different learning strategies depend on the structure of the learning task, a mixture-of-experts neural network model is presented. The results show how these strategies may interact to give rise to the results seen in the human data.

(2000). Turning the tables on the Turing test: The Spivey test. Connection Science: Vol. 12, No. 1, pp. 91-94.