A Neural Network Model for Reaction Times

点击翻译按钮获取中文摘要

Computers of any kind take time to produce an answer to a problem. Mental operations also take time to produce answers. Psychologists have studied reaction time data for over a century in an effort to infer from the observed patterns of reaction time the details of the mental computation being performed. The past decade has seen a resurgence of interest in "brain-like computation" which attempts to capture in artificial systems some of the computational power of the nervous system. Such "neural network" or "connectionist" models are potentially very powerful and very fast, though it is not yet clear for what problems they are best suited. This project will study the behavior of neural networks with respect to the time they take to perform simple computations, with the primary objective of understanding the operations of the human brain, comparing neural network simulations to human experimental data on the same tasks. Different neural network architectures differ a great deal in the time it takes for a particular operation to be performed. Parallel computers, for instance, generally exhibit reaction time patterns completely different from more traditional von Neumann machines, because they can perform many operations at the same time. For example, the most popular class of neural networks, multilayer feed forward networks, use completely parallel operations operating in synchrony. All computations take essentially the same time, although it is possible to build time dependencies into these models with additional assumptions. However, another class of parallel neural network models, non- linear dynamical system models, show very strong intrinsic time dependencies. Examples of such neural network models are Hopfield networks, the ART models, and the model to be used in this research, the BSB model. Most of the interest in neural networks has been in their computational abilities, i.e., getting interesting answers to problems. But ideally, a model of a human mental operation should do two things, compute the answer and take the same relative time to do it as humans. Preliminary simulations using the BSB model for a simple task have shown a reasonable match to experimental reaction time patterns in the well-studied, simple problem of deciding whether two stimuli are the same or different and responding appropriately. Study of the reaction time patterns produced by neural network models for human cognition will begin with simple tasks and then progress to more complex problems. One example is application of this model to the reaction time required to obtain the answers to problems in simple arithmetic.