Parameterization of connectionist models

Parameterization of connectionist models.

We present a method for estimating parameters of connectionist models that allows the model's output to fit as closely as possible to empirical data. The method minimizes a cost function that measures the difference between statistics computed from the model's output and statistics computed from the subjects' performance. An optimization algorithm finds the values of the parameters that minimiz...

Connectionist Models

Connectionist models, also known as Parallel Distributed Processing (PDP) models, are a class of computational models often used to model aspects of human perception, cognition, and behaviour, the learning processes underlying such behaviour, and the storage and retrieval of information from memory. The approach embodies a particular perspective in cognitive science, one that is based on the id...

Connectionist Models of Reading

Connectionist Models of Cognition

The chapters in Part II introduce the reader to broadly influential and foundational approaches to computational cognitive modeling. Each of these chapters describes in detail one particular approach and provides examples of its use in computational cognitive modeling. In this chapter, computer models of cogni-tion that have focused on the use of neural networks are reviewed. These architecture...

Connectionist Models of Development:

The symbolic paradigm of cognitive modeling, championed by Minsky and Papert, Newell and Simon, and other pioneers of the 1950’s and 1960’s, remains very much alive and well today. Yet an alternative paradigm, first championed in the 1950’s by Rosenblatt, in which cognitive processes are viewed as emergent functions of a complex stochastic dynamical system, has continued to have adherents. A co...