Computational And Clinical Approaches To Pattern Recognition And Concept Formation

The ninth volume in this highly acclaimed series discusses the computational and clinical approaches to pattern recognition and concept formation regarding: visual and spatial processing models; computational models, templates and hierarchical models. An ideal reference for students and professionals in experimental psychology and behavioral analysis.

The ninth volume in this highly acclaimed series discusses the computational and clinical approaches to pattern recognition and concept formation regarding: visual and spatial processing models; computational models, templates and hierarchical models. An ideal reference for students and professionals in experimental psychology and behavioral analysis.

Volume eight in this highly acclaimed series discusses the behavioral approaches to pattern recognition and concept formation in two sections: categories and concepts in birds, and shape and form. An ideal reference for students and professionals in experimental psychology and behavioral analysis.

First multi-year cumulation covers six years: 1965-70.

This volume is based on the 10th annual Harvard Symposium for the Quantitative Analysis of Behavior. The first Harvard Symposium was devoted to signal-detection analyses of reinforcement and choice behavior. The present volume reprises the original signal- detection theme, incorporating additional insights based on experimental and theoretical analyses undertaken during the years separating the two conferences. This collection illustrates how signal-detection theory, first advanced to account for performance in threshold-level sensory discrimination, has broadened to encompass a variety of psychological problems involving discriminations between confusable stimuli. The approach is quantitative in its emphasis on estimation of independent parameters of the discrimination process, and analytical in its efforts to separate the determiners of discriminability and bias and to identify the mechanisms of their operation. Above all, the book is broadly integrative in its approach to diverse problems. This volume is based on the 10th annual Harvard Symposium for the Quantitative Analysis of Behavior. The first Harvard Symposium was devoted to signal-detection analyses of reinforcement and choice behavior. The present volume reprises the original signal- detection theme, incorporating additional insights based on experimental and theoretical analyses undertaken during the years separating the two conferences.

Originally published in 1991, this title was the result of a symposium held at Harvard University. It presents some of the exciting interdisciplinary developments of the time that clarify how animals and people learn to behave adaptively in a rapidly changing environment. The contributors focus on aspects of how recognition learning, reinforcement learning, and motor learning interact to generate adaptive goal-oriented behaviours that can satisfy internal needs – an area of inquiry as important for understanding brain function as it is for designing new types of freely moving autonomous robots. Since the authors agree that a dynamic analysis of system interactions is needed to understand these challenging phenomena – and neural network models provide a natural framework for representing and analysing such interactions – all the articles either develop neural network models or provide biological constraints for guiding and testing their design.

The articles gathered in this volume represent examples of a unique approach to the study of mental phenomena: a blend of theory and experiment, informed not just by easily measurable laboratory data but also by human introspection. Subjects such as approach and avoidance, desire and fear, and novelty and habit are studied as natural events that may not exactly correspond to, but at least correlate with, some (known or unknown) electrical and chemical events in the brain.