Scientific Psychology Series – serie

Invariances in Human Information Processing examines and identifies processing universals and how they are implemented in elementary judgemental processes. This edited collection offers evidence that these universals can be extracted and identified from observing law-like principles in perception, cognition, and action. Addressing memory operations, development, and conceptual learning, this book considers basic and complex meso- and makro-stages of information processing. Chapter authors provide theoretical accounts of cognitive processing that may offer tools for identification of functional components in brain activity in cognitive neuroscience

With an emphasis on developments taking place in Germany during the nineteenth century, this book provides in-depth examinations of the key contributions made by the pioneers of scientific psychology. Their works brought measurement and mathematics into the study of the mind.Through unique analysis of measurement theory by Whewell, mathematical developments by Gauss, and theories of mental processes developed by Herbart, Weber, Fechner, Helmholtz, Müller, Delboeuf and others, this volume maps the beliefs, discoveries, and interactions that constitute the very origins of psychophysics and its offspring Experimental Psychology. Murray and Link expertly combine nuanced understanding of linguistic and historic factors to identify theoretical approaches to relating physicalintensities and psychological magnitudes. With an eye to interactions and influences on future work in the field, the volume illustrates the important legacy that mathematical developments in the nineteenth century have for twentieth and twenty-first century psychologists.This detailed and engaging account fills a deep gap in the history of psychology. The Creation of Scientific Psychology will appeal to researchers, academics, and students in the fields of history of psychology, psychophysics, scientific, and mathematical psychology.

Within the last three decades, interest in the psychological experience of human faces has drawn together cognitive science researchers from diverse backgrounds. Computer scientists talk to neural scientists who draw on the work of mathematicians who explicitly influence those conducting behavioral experiments.The chapters in this volume illustrate the breadth of the research on facial perception and memory, with the emphasis being on mathematical and computational approaches. In pulling together these chapters, the editors sought to do much more than illustrate breadth. They endeavored as well to illustrate the synergies and tensions that inevitably result from adopting a broad view, one consistent with the emerging discipline of cognitive science.

This book is designed to be an introduction to the theories of measurement and meaningfulness, and not a comprehensive study of those topics. A major theme of this book is the psychophysical measurement of subjective intensity. This has been a subject of intense interest in psychology from the very beginning of experimental psychology. And from that beginning to the present day, it has continuously generated major controversies involving measurement and meaningfulness.

Measurement and Representation of Sensations offers a glimpse into the most sophisticated current mathematical approaches to psychophysical problems. In this book, editors Hans Colonius and Ehtibar N. Dzhafarov, top scholars in the field, present a broad spectrum of innovative approaches and techniques to classical problems in psychophysics at different levels of stimulus complexity. The chapters emphasize rigorous mathematical constructions to define psychophysical concepts and relate them to observable phenomena. The techniques presented, both deterministic and probabilistic, are all original and recent.Subjects addressed throughout the six chapters of this volume include:*computing subjective distances from discriminability;*a new psychophysical theory of intensity judgments;*computing subjective distances from two discriminability functions;*an alternative to the model-building approach based on observable probabilities; and*possible forms of perceptual separability developed within a generalization of General Recognition Theory.Measurement and Representation of Sensations is a valuable text for both behavioral scientists and applied mathematicians.

This volume presents a series of studies that expand laws, invariants, and principles of psychophysics beyond its classical domain of sensation. This book's goal is to demonstrate the extent of the domain of psychophysics, ranging from sensory processes, through sensory memory and short-term memory issues, to the interaction between sensation and action. The dynamics and timing of human performance are a further important issue within this extended framework of psychophysics: Given the similarity of the various cortical areas in terms of their neuroanatomical structure, it is an important question whether this similarity is paralleled by a similarity of processes. These issues are addressed by the contributions in the present volume using state-of-the-art research methods in behavioral research, psychophysiology, and mathematical modeling. The book is divided into four sections. Part I presents contributions concerning the classical domain of psychophysical judgment. The next two parts are concerned with elementary and higher-order processes and the concluding section deals with psychophysical models. The sections are introduced by guest editorials contributed by independent authors. These editorials present the authors' personals view on the respective section, providing an integrated account of the various contributions or highlighting their focus of interest among them. While also voicing their own and sometimes different point of view, they contribute to the process of discussion that makes science so exciting. This volume should be of great interest to advanced students in neuroscience, cognitive science, psychology, neuropsychology, and related areas who seek to evaluate the range and power of psychological work today. Established scientists in those fields will also appreciate the variety of issues addressed within the same methodological framework and their multiple interconnections and stimulating "cross-talk."

This eagerly awaited volume presents Anderson's cumulative progress in unified social psychology. The research is grounded in the three fundamental laws of information integration theory. Research shows these laws to apply to topics in social and personality psychology such as person cognition, attitudes, moral cognition, social development, group dynamics and self-cognition. This definitive work will broaden the appreciation of Anderson's unique treatment of psychological processes.

Written by one of the masters of the foundation of measurement, Louis Narens' new book thoroughly examines the basis for the measurement-theoretic concept of meaningfulness and presents a new theory about the role of numbers and invariance in science. The book associates with each portion of mathematical science a subject matter that the portion of science is intended to investigate or describe. It considers those quantitative or empirical assertions and relationships that belong to the subject matter to be meaningful (for that portion of science) and those that do not belong to be meaningless.The first two chapters of the Theories of Meaningfulness introduce meaningfulness concepts, their place in the history of science, and some of their traditional applications. The idea that meaningfulness will have different, but interrelated uses is then introduced. To provide formal descriptions of these, the author employs a powerful framework that incorporates pure mathematics, provides for qualitative objects and relations, and addresses the relationships between qualitative objects and pure mathematics. The framework is then applied to produce axiomatic theories of meaningfulness, including generalizations and a new foundation for the famous Erlanger Program of mathematics. The meaningfulness concept is further specialized with the introduction of intrinsicness, which deals with meaningful concepts and relations that are lawful and qualitativeness, which is concerned with qualitative concepts. The concept of empiricalness is then introduced to distinguish it from meaningfulness and qualitativeness.The failure to distinguish empiricalness from meaningfulness and qualitativeness has produced much confusion in the foundations of science literature and has generated many pseudo-controversies. This book suggests that many of these disappear when empiricalness is intersected with the other concepts to produce "meaningful and empirical relations," "empirical laws," and "qualitative and empirical concepts."A primary goal of this book is to show that the new theories of meaningfulness and intrinsicness developed in this book are not only descriptive but are also potent. Asserting that they do more than codify already existing concepts the book:*works out logical relationships between meaningfulness concepts that were previously unrecognized;*clarifies certain well-known and important debates by providing rich languages with new concepts and technical results (theorems) that yield insights into the debated issues and positions taken on them; and*provides new techniques and results in substantive scientific areas of inquiry. This book is about the role of mathematics in science. It will be useful to those concerned with the foundations of science in their respective fields. Various substantive examples from the behavioral sciences are presented.

The ability to learn concepts lies at the very core of human cognition, enabling us to efficiently classify, organize, identify, and store complex information. In view of the basic role that concepts play in our everyday physical and mental lives, the fields of cognitive science and psychology face three long standing challenges: discovering the laws that govern concept learning and categorization behavior in organisms, showing how they inform other areas of cognitive research, and describing them with the mathematical systematicity and precision found in the physical sciences. In light of these theoretical and methodological shortcomings, this volume will introduce a set of general mathematical principles for predicting and explaining conceptual behavior. The author’s theory is based on seven fundamental constructs of universal science: invariance, complexity, information, similarity, dissimilarity, pattern, and representation. These constructs are joined by a novel mathematical framework that does not depend on probability theory, and derives key results from conceptual behavior research with other key areas of cognitive research such as pattern perception, similarity assessment, and contextual choice. The result is a unique and systematic unifying foundation for cognitive science in the tradition of classical physics.

The mental representations of perceptual and cognitive stimuli vary on many dimensions. In addition, because of quantal fluctuations in the stimulus, spontaneous neural activity, and fluctuations in arousal and attentiveness, mental events are characterized by an inherent variability. During the last several years, a number of models and theories have been developed that explicitly assume the appropriate mental representation is both multidimensional and probabilistic. This new approach has the potential to revolutionize the study of perception and cognition in the same way that signal detection theory revolutionized the study of psychophysics. This unique volume is the first to critically survey this important new area of research.

Two experimental procedures have prompted the empirical development of psychophysical models: those that measure response frequency, often referred to as response probability; and those that measure response time, sometimes referred to as reaction time. The history of psychophysics is filled with theories that predict one or the other of these two responses. Yet the persistent reappearance of empirical relationships between these two measures of performance makes clear the need for a theory that both predicts and relates these two measures. Most likely, both response measures are the result of a single process that generates empirical laws relating response time and response probability. It is this process — its theory, description, and application — that is the topic of The Wave Theory of Difference and Similarity. The author of this book has set out to provide a theoretical foundation for formulating new theories that systematize earlier results and to stimulate new concepts and introduce new tools for exploring mental phenomena and improving mental measurement.

The plan for this volume emerged during the international Leipzig conference commemorating the centenary of the death of Gustav Fechner. The contributors suggested that while many features of modern psychological theory were anticipated by Fechner, many new theoretical approaches owe much more to him than often is realized. As such, they decided to honor Fechner by evaluating his own contribution to the founding of psychology and psychoanalysis, by deepening the foundations of psychological theories of consciousness, perception, and choice, and by using the analysis of time to create a new appreciation of constraints that bind mental processes together. Thus, this volume spans an extraordinary range of psychological topics, from hermeneutics to the time-quantum basis for mental processes, in a way that would both amaze and delight Fechner. Moreover, the international reach of his pioneering ideas can be seen from the current locations of the contributors. The span from Japan to the United States to Holland to Germany and to Israel provides a global measure of Fechner's scientific legacy.

This research monograph describes a large programming project in which an underwater organism, capable of perceiving, learning, deciding, and navigating, is computationally simulated. The developed computational model serves as a contemporary theory of perceptual-motor performance, embodying much of what is known about human vision and some of what is known about other cognitive processes. This artificial intelligence project has substantial contributions to make to the development of autonomous underwater vehicles. It also makes a specific theoretical statement about the organization and nature of organic perceptual motor systems that may be useful to psychologists, neuroscientists, and theoreticians in a number of other fields.

Psychophysical theory exists in two distinct forms -- one ascribes the explanation of phenomena and empirical laws to sensory processes. Context effects arising through the use of particular methods are an unwanted nuisance whose influence must be eliminated so that one isolates the "true" sensory scale. The other considers psychophysics only in terms of cognitive variables such as the judgment strategies induced by instructions and response biases. Sensory factors play a minor role in cognitive approaches. This work admits the validity of both forms of theory by arguing that the same empirical phenomena should be conceptualized in two alternative, apparently contradictory, ways. This acceptance of opposites is necessary because some empirical phenomena are best explained in terms of sensory processes, while others are best ascribed to central causes.The complementarity theory stresses the "mutually completing" nature of two distinct models. The first assigns importance to populations of sensory neurons acting in the aggregate and is formulated to deal with sensory effects. The second assigns importance to judgment uncertainty and to the subject strategies induced by experimental procedures. This model is formulated to explain context effects. Throughout the text, the exposition is interlaced with mathematics, graphs, and computer simulations designed to reveal the complementary nature of psychophysical explanations.

Signal detection theory--as developed in electrical engineering and based on statistical decision theory--was first applied to human sensory discrimination 40 years ago. The theoretical intent was to provide a valid model of the discrimination process; the methodological intent was to provide reliable measures of discrimination acuity in specific sensory tasks. An analytic method of detection theory, called the relative operating characteristic (ROC), can isolate the effect of the placement of the decision criterion, which may be variable and idiosyncratic, so that a pure measure of intrinsic discrimination acuity is obtained. For the past 20 years, ROC analysis has also been used to measure the discrimination acuity or inherent accuracy of a broad range of practical diagnostic systems. It was widely adopted by methodologists in the field of information retrieval, is increasingly used in weather forecasting, and is the generally preferred method in clinical medicine, primarily in radiology. This book attends to both themes, ROC analysis in the psychology laboratory and in practical diagnostic settings, and to their essential unity. The focus of this book is on detection and recognition as fundamental tasks that underlie most complex behaviors. As defined here, they serve to distinguish between two alternative, confusable stimulus categories, which may be perceptual or cognitive categories in the psychology laboratory, or different states of the world in practical diagnostic tasks.This book on signal detection theory in psychology was written by one of the developers of the theory, who co-authored with D.M. Green the classic work published in this area in 1966 (reprinted in 1974 and 1988). This volume reviews the history of the theory in engineering, statistics, and psychology, leading to the separate measurement of the two independent factors in all discrimination tasks, discrimination acuity and decision criterion. It extends the previous book to show how in several areas of psychology--in vigilance and memory--what had been thought to be discrimination effects were, in reality, effects of a changing criterion. The book shows that data plotted in terms of the relative operating characteristic have essentially the same form across the wide range of discrimination tasks in psychology. It develops the implications of this ROC form for measures of discrimination acuity, pointing up the valid ones and identifying several common, but invalid, ones. The area under the binormal ROC is seen to be supported by the data; the popular measures d' and percent correct are not. An appendix describes the best, current programs for fitting ROCs and estimating their parameters, indices, and standard errors. The application of ROC analysis to diagnostic tasks is also described. Diagnostic accuracy in a wide range of tasks can be expressed in terms of the ROC area index. Choosing the appropriate decision criterion for a given diagnostic setting--rather than considering some single criterion to be natural and fixed--has a major impact on the efficacy of a diagnostic process or system. Illustrated here by separate chapters are diagnostic systems in radiology, information retrieval, aptitude testing, survey research, and environments in which imminent dangerous conditions must be detected. Data from weather forecasting, blood testing, and polygraph lie detection are also reported. One of these chapters describes a general approach to enhancing the accuracy of diagnostic systems.

Mathematical psychology is an interdisciplinary area of research in which methods of mathematics, operations research, and computer science in psychology are used. Now more than thirty years old, the field has continued to grow rapidly and has taken on a life of its own. This volume summarizes recent progress in mathematical psychology as seen by some of the leading figures in the field as well as some of its leading young researchers. The papers presented in this volume reflect the most important current directions of research in mathematical psychology. They cover topics in measurement, decision and choice, psychophysics and psychometrics, knowledge representation, neural nets and learning models, and cognitive modeling. Some of the major ideas included are new applications of concepts of measurement theory to social phenomena, new directions in the theory of probabilistic choice, surprising results in nonlinear utility theory, applications of boolean methods in the theory of knowledge spaces, applications of neural net ideas to concept learning, developments in the theory of parallel processing models of response time, new results in inhibition theory, and new concepts about paired associate learning.

This volume provides an overview of a relatively neglected branch of connectionism known as localist connectionism. The singling out of localist connectionism is motivated by the fact that some critical modeling strategies have been more readily applied in the development and testing of localist as opposed to distributed connectionist models (models using distributed hidden-unit representations and trained with a particular learning algorithm, typically back-propagation). One major theme emerging from this book is that localist connectionism currently provides an interesting means of evolving from verbal-boxological models of human cognition to computer-implemented algorithmic models. The other central messages conveyed are that the highly delicate issue of model testing, evaluation, and selection must be taken seriously, and that model-builders of the localist connectionist family have already shown exemplary steps in this direction.

This volume examines the scientific basis of reductionist approaches to understanding visual perception. The author makes the provocative argument that contemporary neuroscience and cognitive science have gone off on a wild-goose chase in the search for reductionist explanations of perceptual phenomena. This book considers some specific and general examples of this misdirection and suggests an alternative future course for science. It reviews the successes and failures of the sciences' efforts to explain perceptual and other mental functions in the terms of either internal cognitive mechanisms, formal models, or the neural structures from which the brain--the organ of the mind--is constructed.Although this is an iconoclastic and minority view, the book shows how many contemporary perceptual scientists have qualified their thinking with regard to what their data and theories mean even while generally accepting the empirical findings. It is, without question, an attempted refutation of some of the primary assumptions of contemporary theory. Summing up the author's convictions concerning some of the most important questions of human nature, this book is a statement of a point of view that has provided a framework for his personal answers to some of these important questions of human history.

Within the last three decades, interest in the psychological experience of human faces has drawn together cognitive science researchers from diverse backgrounds. Computer scientists talk to neural scientists who draw on the work of mathematicians who explicitly influence those conducting behavioral experiments.The chapters in this volume illustrate the breadth of the research on facial perception and memory, with the emphasis being on mathematical and computational approaches. In pulling together these chapters, the editors sought to do much more than illustrate breadth. They endeavored as well to illustrate the synergies and tensions that inevitably result from adopting a broad view, one consistent with the emerging discipline of cognitive science.

This book considers one of the most fundamental, but only infrequently considered, issues in psychology--Are mental processes accessible by means of verbal reports and/or experimental assays? It is argues that this is the main characteristic distinguishing between behaviorism and mentalistic cognitivism. The answer posed by the author is that, with few exceptions and for the most fundamental reasons, mental processes are not accessible and that any psychology, such as contemporary cognitivism, based on a putative analysis of mind into its mental components must be fallacious.Classic and modern arguments against both mentalism and behaviorism are reviewed. In general, it is concluded that most antibehaviorist arguments are based on second order humanistic considerations rather than those underlying the usual scientific standards. Behaviorism represents the best that can be done in a situation of fundamental immeasurability and uncertainty. A modern version is offered in the final chapter of this book.

This new monograph presents Dr. Luce's current understanding of the behavioral properties people exhibit (or should exhibit) when they make selections among alternatives and how these properties lead to numerical representations of those preferences. It summarizes, and places in historical perspective, the research Dr. Luce has done on utility theory for over 10 years. Included are axiomatic theoretical formulations, experiments designed to test individual assumptions, and analyses of the fit to bodies of data of numerical representations derived from the theory.

The goal of Norman H. Anderson's new book is to help students develop skills of scientific inference. To accomplish this he organized the book around the "Experimental Pyramid"--six levels that represent a hierarchy of considerations in empirical investigation--conceptual framework, phenomena, behavior, measurement, design, and statistical inference. To facilitate conceptual and empirical understanding, Anderson de-emphasizes computational formulas and null hypothesis testing. Other features include:*emphasis on visual inspection as a basic skill in experimental analysis to help students develop an intuitive appreciation of data patterns;*exercises that emphasize development of conceptual and empirical application of methods of design and analysis and de-emphasize formulas and calculations; and*heavier emphasis on confidence intervals than significance tests.The book is intended for use in graduate-level experimental design/research methods or statistics courses in psychology, education, and other applied social sciences, as well as a professional resource for active researchers. The first 12 chapters present the core concepts graduate students must understand. The next nine chapters serve as a reference handbook by focusing on specialized topics with a minimum of technicalities.

Written by one of the masters of the foundation of measurement, Louis Narens' new book thoroughly examines the basis for the measurement-theoretic concept of meaningfulness and presents a new theory about the role of numbers and invariance in science. The book associates with each portion of mathematical science a subject matter that the portion of science is intended to investigate or describe. It considers those quantitative or empirical assertions and relationships that belong to the subject matter to be meaningful (for that portion of science) and those that do not belong to be meaningless.The first two chapters of the Theories of Meaningfulness introduce meaningfulness concepts, their place in the history of science, and some of their traditional applications. The idea that meaningfulness will have different, but interrelated uses is then introduced. To provide formal descriptions of these, the author employs a powerful framework that incorporates pure mathematics, provides for qualitative objects and relations, and addresses the relationships between qualitative objects and pure mathematics. The framework is then applied to produce axiomatic theories of meaningfulness, including generalizations and a new foundation for the famous Erlanger Program of mathematics. The meaningfulness concept is further specialized with the introduction of intrinsicness, which deals with meaningful concepts and relations that are lawful and qualitativeness, which is concerned with qualitative concepts. The concept of empiricalness is then introduced to distinguish it from meaningfulness and qualitativeness.The failure to distinguish empiricalness from meaningfulness and qualitativeness has produced much confusion in the foundations of science literature and has generated many pseudo-controversies. This book suggests that many of these disappear when empiricalness is intersected with the other concepts to produce "meaningful and empirical relations," "empirical laws," and "qualitative and empirical concepts."A primary goal of this book is to show that the new theories of meaningfulness and intrinsicness developed in this book are not only descriptive but are also potent. Asserting that they do more than codify already existing concepts the book:*works out logical relationships between meaningfulness concepts that were previously unrecognized;*clarifies certain well-known and important debates by providing rich languages with new concepts and technical results (theorems) that yield insights into the debated issues and positions taken on them; and*provides new techniques and results in substantive scientific areas of inquiry. This book is about the role of mathematics in science. It will be useful to those concerned with the foundations of science in their respective fields. Various substantive examples from the behavioral sciences are presented.

The goal of Norman H. Anderson's new book is to help students develop skills of scientific inference. To accomplish this he organized the book around the "Experimental Pyramid"--six levels that represent a hierarchy of considerations in empirical investigation--conceptual framework, phenomena, behavior, measurement, design, and statistical inference. To facilitate conceptual and empirical understanding, Anderson de-emphasizes computational formulas and null hypothesis testing. Other features include:*emphasis on visual inspection as a basic skill in experimental analysis to help students develop an intuitive appreciation of data patterns;*exercises that emphasize development of conceptual and empirical application of methods of design and analysis and de-emphasize formulas and calculations; and*heavier emphasis on confidence intervals than significance tests.The book is intended for use in graduate-level experimental design/research methods or statistics courses in psychology, education, and other applied social sciences, as well as a professional resource for active researchers. The first 12 chapters present the core concepts graduate students must understand. The next nine chapters serve as a reference handbook by focusing on specialized topics with a minimum of technicalities.

This volume presents a series of studies that expand laws, invariants, and principles of psychophysics beyond its classical domain of sensation. This book's goal is to demonstrate the extent of the domain of psychophysics, ranging from sensory processes, through sensory memory and short-term memory issues, to the interaction between sensation and action. The dynamics and timing of human performance are a further important issue within this extended framework of psychophysics: Given the similarity of the various cortical areas in terms of their neuroanatomical structure, it is an important question whether this similarity is paralleled by a similarity of processes. These issues are addressed by the contributions in the present volume using state-of-the-art research methods in behavioral research, psychophysiology, and mathematical modeling. The book is divided into four sections. Part I presents contributions concerning the classical domain of psychophysical judgment. The next two parts are concerned with elementary and higher-order processes and the concluding section deals with psychophysical models. The sections are introduced by guest editorials contributed by independent authors. These editorials present the authors' personals view on the respective section, providing an integrated account of the various contributions or highlighting their focus of interest among them. While also voicing their own and sometimes different point of view, they contribute to the process of discussion that makes science so exciting. This volume should be of great interest to advanced students in neuroscience, cognitive science, psychology, neuropsychology, and related areas who seek to evaluate the range and power of psychological work today. Established scientists in those fields will also appreciate the variety of issues addressed within the same methodological framework and their multiple interconnections and stimulating "cross-talk."

Measurement and Representation of Sensations offers a glimpse into the most sophisticated current mathematical approaches to psychophysical problems. In this book, editors Hans Colonius and Ehtibar N. Dzhafarov, top scholars in the field, present a broad spectrum of innovative approaches and techniques to classical problems in psychophysics at different levels of stimulus complexity. The chapters emphasize rigorous mathematical constructions to define psychophysical concepts and relate them to observable phenomena. The techniques presented, both deterministic and probabilistic, are all original and recent.Subjects addressed throughout the six chapters of this volume include:*computing subjective distances from discriminability;*a new psychophysical theory of intensity judgments;*computing subjective distances from two discriminability functions;*an alternative to the model-building approach based on observable probabilities; and*possible forms of perceptual separability developed within a generalization of General Recognition Theory.Measurement and Representation of Sensations is a valuable text for both behavioral scientists and applied mathematicians.

Mathematical psychology is an interdisciplinary area of research in which methods of mathematics, operations research, and computer science in psychology are used. Now more than thirty years old, the field has continued to grow rapidly and has taken on a life of its own. This volume summarizes recent progress in mathematical psychology as seen by some of the leading figures in the field as well as some of its leading young researchers. The papers presented in this volume reflect the most important current directions of research in mathematical psychology. They cover topics in measurement, decision and choice, psychophysics and psychometrics, knowledge representation, neural nets and learning models, and cognitive modeling. Some of the major ideas included are new applications of concepts of measurement theory to social phenomena, new directions in the theory of probabilistic choice, surprising results in nonlinear utility theory, applications of boolean methods in the theory of knowledge spaces, applications of neural net ideas to concept learning, developments in the theory of parallel processing models of response time, new results in inhibition theory, and new concepts about paired associate learning.

This volume provides an overview of a relatively neglected branch of connectionism known as localist connectionism. The singling out of localist connectionism is motivated by the fact that some critical modeling strategies have been more readily applied in the development and testing of localist as opposed to distributed connectionist models (models using distributed hidden-unit representations and trained with a particular learning algorithm, typically back-propagation). One major theme emerging from this book is that localist connectionism currently provides an interesting means of evolving from verbal-boxological models of human cognition to computer-implemented algorithmic models. The other central messages conveyed are that the highly delicate issue of model testing, evaluation, and selection must be taken seriously, and that model-builders of the localist connectionist family have already shown exemplary steps in this direction.

This book considers one of the most fundamental, but only infrequently considered, issues in psychology--Are mental processes accessible by means of verbal reports and/or experimental assays? It is argues that this is the main characteristic distinguishing between behaviorism and mentalistic cognitivism. The answer posed by the author is that, with few exceptions and for the most fundamental reasons, mental processes are not accessible and that any psychology, such as contemporary cognitivism, based on a putative analysis of mind into its mental components must be fallacious.Classic and modern arguments against both mentalism and behaviorism are reviewed. In general, it is concluded that most antibehaviorist arguments are based on second order humanistic considerations rather than those underlying the usual scientific standards. Behaviorism represents the best that can be done in a situation of fundamental immeasurability and uncertainty. A modern version is offered in the final chapter of this book.