Recent Progress in Mathematical Psychology
Psychophysics, Knowledge Representation, Cognition, and Measurement
Scientific Psychology Series

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.

Contents: Part I:Measurement, Decision and Choice.P. Fishburn, Utility of Wealth in Nonlinear Utility Theory. R. Janssens, Structuring Complex Concepts. F.S. Roberts, Role Assignments and Indifference Graphs. Part II:Psychophysics and Psychometrics.T. Indow, Parallel Shift of Judgment Characteristic Curve According to the Context in Cutaneous and Color Discriminations. G. Storms, A Maximum Likelihood Model for Psychophysical Data and Response Times. A.H.G.S. Van der Ven, Inhibition Theory and the Concept of Perseveration. M. D'Zmura, G. Iverson, A Formal Approach to Color Constancy: The Recovery of Surface and Light Source Spectral Properties Using Bilinear Models. Part III:Knowledge Representation.C.E. Dowling, C. Hockemeyer, Computing the Intersection of Knowledge Spaces Using Only Their Bases. J-C. Falmagne, J-P. Doignon, Meshing Knowledge Structures. K. Lakshminarayan, F. Gilson, An Application of a Stochastic Knowledge Structure Model. P. Theuns, Building a Knowledge Space Via Boolean Analysis of Co-occurence Data. Part IV:Learning Models.J.N. Rouder, W.H. Batchelder, Multinomial Models for Measuring Storage and Retrieval Processes in Paired Associate Learning. P. Suppes, L. Liang, Concept Learning Rates and Transfer Performance of Several Multivariate Neural Network Models. Part V:Cognitive Modeling.V. Brown, Comparing Parallel and Sequential Multinomial Models of Letter Identification. H. Feger, U. von Hecker, Reciprocity as an Interaction Principle. R. Schweikert, J. Mounts, Additive Effects of Factors on Reaction Time and Evoked Potentials in Continuous Flow Models.