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.
Table of Contents
Contents: F.G. Ashby, Multivariate Probability Distributions. Part I:Similarity, Preference, and Choice. J.L. Zinnes, D.B. MacKay, A Probabilistic Multidimensional Scaling Approach: Properties and Procedures. G. De Soete, J.D. Carroll, Probabilistic Multidimensional Models of Pairwise Choice Data. U. Bo"ckenholt, Multivariate Models of Preference and Choice. D.M. Ennis, K. Mullen, A General Probabilistic Model for Triad Discrimination, Preferential Choice, and Two-Alternative Identification. N.A. Perrin, Uniting Identification, Similarity and Preference: General Recognition Theory. Part II:Interactions Between Perceptual Dimensions. W.T. Maddox, Perceptual and Decisional Separability. H. Kadlec, J.T. Townsend, Signal Detection Analyses of Dimensional Interactions. T.D. Wickens, L.A. Olzak, Three Views of Association in Concurrent Detection Ratings. Part III:Detection, Identification, and Categorization. J.P. Thomas, L.A. Olzak, Simultaneous Detection and Identification. D.M. Ennis, Modeling Similarity and Identification When There Are Momentary Fluctuations in Psychological Magnitudes. A.A.J. Marley, Developing and Characterizing Multidimensional Thurstone and Luce Models for Identification and Preference. Y. Takane, T. Shibayama, Structures in Stimulus Identification Data. R.M. Nosofsky, Exemplar-Based Approach to Relating Categorization, Identification, and Recognition. M.M. Cohen, D.W. Massaro, On the Similarity of Categorization Models. F.G. Ashby, Multidimensional Models of Categorization.
"Upon reading it, the reader will be pleasantly surprised with the real progress that mathematical psychologists have made toward the development of multidimensional models of detection, identification, categorization, similarity, recognition, and preference....Ashby's book is a new addition to the Scientific Psychology series....This is a prestigious series of high-quality books, and Ashby's volume makes an excellent new contribution to the series."
"...a valuable overview of the development of probabilistic multidimensional models applied to specific areas of perception and cognition."
—Journal of Mathematical Psychology