Psychophysics is by definition mappings between events in the environment and levels of human sensory responses. In this text the methods of nonlinear dynamics, employing trajectories developed for simpler sensory modelling, are extended to classes of problems which lie at the interface between sensation and perception. A diversity of topics for which extensive empirical evidence exists are reformulated by writing their dynamics in terms of complex trajectories put into coupled lattices and into cascades of such lattices. Fundamental relationships between core processes of psychophysics in time and space, and recurrent quantitative or topological distortions of the physical world which arise in perception, are given a treatment which contrasts fundamentally with traditional linear equations in use since the 19th century.

Originally published in 1992, this work compliments and extends the theory and results of nonlinear psychophysics – an original approach created by the author. It breaks with the traditional mathematics used in the experimental psychology of sensation and draws on what is popularly known as chaos theory and its extension into neural networks. Topical and innovative in its approach, it integrates a diversity of topics previously treated separately into one framework. The properties of the mathematics used are illustrated in the context of substantive problems in psychophysics; thus, it builds strong new bridges between the dynamics of mass action in psychophysical processes and the broader phenomena of sensation. No other treatments of the topic take quite this approach; the use of systems theory, rather than traditional equations of psychophysics dating from the mid-nineteenth century, offers a striking contrast in both theory construction and data analysis.

Nonlinear Psychophysical Dynamics utilizes new results in systems theory as a foundation for representing sensory channels as a form of recursive loop processes. It demonstrates that a range of phenomena, previously treated as diverse or anomalous, are more readily seen as related and as the natural consequence of self-regulation and nonlinearity. Some cases with appropriate data analysis are reviewed.

Originally published in 1992, this work compliments and extends the theory and results of nonlinear psychophysics - an original approach created by the author. It breaks with the traditional mathematics used in the experimental psychology of sensation and draws on what is popularly known as chaos theory and its extension into neural networks. Topical and innovative in its approach, it integrates a diversity of topics previously treated separately into one framework. The properties of the mathematics used are illustrated in the context of substantive problems in psychophysics; thus, it builds strong new bridges between the dynamics of mass action in psychophysical processes and the broader phenomena of sensation. No other treatments of the topic take quite this approach; the use of systems theory, rather than traditional equations of psychophysics dating from the mid-nineteenth century, offers a striking contrast in both theory construction and data analysis.

Nonlinear Psychophysical Dynamics utilizes new results in systems theory as a foundation for representing sensory channels as a form of recursive loop processes. It demonstrates that a range of phenomena, previously treated as diverse or anomalous, are more readily seen as related and as the natural consequence of self-regulation and nonlinearity. Some cases with appropriate data analysis are reviewed.

Although its roots can be traced to the 19th century, progress in the study of nonlinear dynamical systems has taken off in the last 30 years. While pertinent source material exists, it is strewn about the literature in mathematics, physics, biology, economics, and psychology at varying levels of accessibility. A compendium research methods reflecting the expertise of major contributors to NDS psychology, Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data examines the techniques proven to be the most useful in the behavioral sciences. The editors have brought together constructive work on new practical examples of methods and application built on nonlinear dynamics. They cover dynamics such as attractors, bifurcations, chaos, fractals, catastrophes, self-organization, and related issues in time series analysis, stationarity, modeling and hypothesis testing, probability, and experimental design. The analytic techniques discussed include several variants of the fractal dimension, several types of entropy, phase-space and state-space diagrams, recurrence analysis, spatial fractal analysis, oscillation functions, polynomial and Marquardt nonlinear regression, Markov chains, and symbolic dynamics. The book outlines the analytic requirements faced by social scientists and how they differ from those of mathematicians and natural scientists. It includes chapters centered on theory and procedural explanations for running the analyses with pertinent examples and others that illustrate applications where a particular form of analysis is seen in the context of a research problem. This combination of approaches conveys theoretical and practical knowledge that helps you develop skill and expertise in framing hypotheses dynamically and building viable analytic models to test them.