abstract Eliano Pessa

Eliano Pessa (University of Pavia, Italy)

Emergent phenomena in neural network models of cognitive processing

A basic hypothesis of the connectionist approach asserts that the observed macroscopic consequences of cognitive processing are nothing but collective effects emergent from the interactions between suitable microscopic units. The implementation of the above assertion is based on mathematical models making use of artificial neural networks. In this talk we discuss whether: a) these models concretely exhibit emergent collective effects; b) these collective effects are characterized by the same features which we observe in behaviors produced by human mental processes. Our conclusion is that only particular models of this kind (including recurrent networks) can give rise to emergent collective effects, often exhibiting top-down causation, multistability and roaming behaviors. Moreover, only the use of specific strategies and techniques of data analysis allows to use the models themselves in a way useful to experimental psychologists. We present the application of our proposals to a specific case study in order to illustrate the nature of the difficulties encountered when dealing with a concrete implementation.