Introduction

Connectionism has recently become a very popular framework for modelling cognition and the brain. Its use in the study of basic language processing tasks (such as reading and speech recognition) is particularly widespread. Many effects (such as regularity, frequency and consistency effects) arise naturally, as a simple consequence of the gradual acquisition of the appropriate conditional probabilities, for virtually any mapping for virtually any neural network trained by virtually any gradient descent procedure. Other effects (such as cohort, morphological and priming effects) can arise as a simple consequence of information or representation overlap. More effects (such as robustness) follow easily from information redundancy. These effects show themselves during learning, after learning and after simulated brain damage. There is thus much scope for the connectionist modelling of developmental, normal and patient data, and the literature reflects this.

The problem is that many of these effects are essentially “free gifts” that come with virtually any neural network model, and yet we often see them being quoted in the literature as being “evidence” for the correctness of particular models of the brain. This can be very misleading, particularly for researchers that have no direct modelling experience themselves. In this chapter I shall review the main effects that we can expect to arise naturally in connectionist models and attempt to show, in simple terms, how these effects are a natural consequence of the underlying information theory and how the details of the network models do not make any real difference to these results.