The problem of multiple instantiation is the ability to handle different instances of a unique object at the same time. For connectionist models that do not use a working area containing copies of items from a long-term knowledge base, the problem of multiple instantiation is a particularly difficult one. While people are able to deal with multiple instances, their performance when doing so is nonetheless poorer, which is not the case for symbolic models. A cognitive model should reflect competence, as well as its limits. Some connectionist solutions to the problem of multiple instantiation are mentioned in this paper. An new solution which makes use of semi-distributed representations is presented. This model does not separate the long term knowledge base from a working area and has no recourse to copies. This solution limits the process of multiple instantiation in a way that should better reflect human data.

This paper presents a connectionist model of human reasoning that uses temporal relations between node firing. Temporal synchrony is used for representing variable binding and concepts. Temporal succession serves to represent rules by linking antecedent to consequent parts of the rule. The number of successive synchronies is affected by two well-known neurobiological parameters, the frequency of neural rythmic activity and the precision of neural synchronization. Reasoning is predicted to be constrained by these variables. An experiment manipulating the amount of successive synchronies is presented. Experimental results would seem to confirm the predictions.