Table of Contents

Volume I: From Learning Theory to Connectionist Theory. Contents: P. Suppes, Estes' Statistical Learning Theory: Past, Present, and Future. G. Bower, E. Heit, Choosing Between Uncertain Options: A Reprise to the Estes Scanning Model. R.D. Luce, A Path Taken: Aspects of Modern Measurement Theory. J.T. Townsend, Chaos Theory: A Brief Tutorial and Discussion. S. Link, Imitatio Estes: Stimulus Sampling Origins of Weber's Law. D. LaBerge, A Mathematical Theory of Attention in a Distractor Task. J.I. Yellot, Jr., Triple Correlation and Texture Discrimination. R.M. Nosofsky, Exemplars, Prototypes, and Similarity Rules. M.A. Gluck, Stimulus Sampling and Distributed Representations in Adaptive Network Theories of Learning. B.B. Murdock, Serial Organization in a Distributed Memory Model. S.A. Sloman, D.E. Rumelhart, Reducing Interference in Distributed Memories Through Episodic Gating. J.G. Rueckl, S.M. Kosslyn, What Good is Connectionist Modeling? A Dialogue.

Reviews

"...these volumes are excellent collections of articles....two fine volumes edited and written by first-rate cognitive psychologists."
—Contemporary Psychology