Preface | p. i |
Dynamic Psychology: An Introduction to Time Series Analysis | p. 1 |
Temporal Sequences of Behavioral Events | p. 3 |
The Time Series Approach | p. 5 |
Time-Series Analysis of the Anxiety Treatment Data | p. 8 |
Higher Order Autoregression Models | p. 14 |
The Effect of Averaging Autocorrelated Data across Subjects | p. 15 |
Time-Series Analysis with Long Data Sequences | p. 18 |
Time-Series Analysis Procedures using MINITAB | p. 26 |
Summary | p. 27 |
The Analysis of Nonstationary Time Series | p. 29 |
An Adaptive Kalman Filter Model for Estimating Timeseries Parameters | p. 30 |
Detecting a Change in Autoregression Parameters using Adaptive Kalman Filtering | p. 37 |
An Application of the Change Detection Procedure | p. 40 |
Application of Adaptive Kalman Filtering to the IKT Data | p. 42 |
Summary | p. 47 |
Linear and Nonlinear Systems in Psychological Research | p. 48 |
The Linear Polynomial Model | p. 49 |
Nonlinear System Identification: A Generalization of the Polynomial Model | p. 51 |
A Simplified Nonlinear System Identification Technique Based on Cross-correlation | p. 63 |
Nonlinear System Identification for Pulse Input and Output Sequences | p. 65 |
A Smeared Nonlinear System Identification Analysis of a Two-channel Tracking Task | p. 66 |
Nonlinear Systems Analysis of Differenced Input and Output Sequences | p. 76 |
Psychological Applications of Nonlinear System Identification | p. 79 |
Theoretical Interpretation of Linear and Nonlinear Transfer Functions | p. 88 |
Summary | p. 89 |
Nonlinear System Identification Using a Gradient-Descent Function Minimization Method | p. 91 |
A Tensor Calculus Representation of the Nonlinear System Identification Equations | p. 95 |
Fitting the Nonlinear Model to Multiple Times Series Data By Minimizing the Squared Error | p. 101 |
An Evaluation of the Error Minimization Nonlinear System Identification Procedure using Simulated Data | p. 103 |
Prediction of Handwriting Dynamics | p. 104 |
Analysis of the Two Channel Tracking Data using the Gradient-Descent Minimization Algorithm | p. 117 |
Some Artificial Examples of Second-Order Nonlinear Systems | p. 126 |
Nonlinear System Identification Analysis of Noisy Coupled Logistic Data | p. 128 |
Evaluating the Appropriate Number of Parameters in Nonlinear System Identification | p. 131 |
Summary | p. 133 |
Graphical Representation of Nonlinear Dynamics | p. 134 |
Phase Diagrams | p. 134 |
Phase Diagrams for the Coupled Logistic Model | p. 139 |
The Effect of Changes in Initial Values of a Time Series on its Qualitative Dynamics | p. 141 |
Recurrence Plots and their Application in the Analysis of Qualitative Dynamics | p. 145 |
Recurrence Plots for Empirical Data Sets | p. 148 |
Recurrence Plots for IKT Data | p. 152 |
Recurrence Plots for Handwriting Velocity Data | p. 153 |
Recurrence Plots for Heart Interbeat Interval Data | p. 155 |
Recurrence Plots for EEG Data | p. 156 |
Recurrence Plots for Sequential RT Data | p. 157 |
Poincare Plots | p. 162 |
Visualization of Data Obtained from Nonlinear Dynamical Systems | p. 163 |
Summary | p. 164 |
Quantitative Indices of Nonlinear Dynamics | p. 165 |
Evidence for Low Dimensional Attractors | p. 166 |
Computing the Correlation Dimension for Data Sets | p. 171 |
Dimensionality Estimates for the Coupled Logistic System | p. 174 |
Psychological Applications of the Correlation Dimension Measure | p. 176 |
Lyapunov Exponents For a Time Series | p. 180 |
Applications of Lyapunov Exponents in Psychology | p. 190 |
Comparing the Dynamics of Two or More Time Series | p. 192 |
Entropy | p. 192 |
General Comments on Computing Nonlinear Indices | p. 196 |
Pointwise Dimension Measures | p. 197 |
Nonlinearity Tests using Multivariate Time Series | p. 198 |
The BDS Test for Nonlinearity | p. 201 |
Alternative Techniques for Detecting Nonlinear Determinism in a Time Series | p. 203 |
Summary | p. 205 |
Noise Reduction Methods and Hypothesis Testing for Nonlinear Systems | p. 207 |
The SVD Algorithm | p. 207 |
Prediction Methods for Quantifying Nonlinear Dynamics | p. 211 |
Surrogate Data Sets: Testing Statistical Hypotheses for Nonlinear Dynamics | p. 216 |
Comparison of the Effects of Noise on Nonlinear Indices | p. 220 |
Analysis of Example Data Sets from Psychology | p. 222 |
Summary | p. 233 |
Control of Chaos and its Psychological Applications | p. 234 |
Control of Chaos using Unstable Orbits | p. 235 |
Applying Control of Chaos to Experimental Data | p. 238 |
A Nonlinear Neural Network Model of Human Cognition | p. 241 |
A Modular Neural Network Model | p. 248 |
Control of the Modular Neural Network | p. 252 |
Nonlinear Dynamics of the Modular Neural Network | p. 253 |
Application of Control of Chaos Ideas in Behavioral Science | p. 258 |
Summary | p. 259 |
Complexity Theory and Psychology | p. 261 |
A Complex Information Processing System | p. 262 |
Information Processing in a Randomly Connected Boolean Neural Network | p. 264 |
Evidence for Complex Behavior in Random Boolean Neural Networks | p. 267 |
Cognitive Phenomena and Complexity Theory | p. 270 |
Optimal Information Processing and Complexity at the 'Edge-of-Chaos' | p. 276 |
Edge-of-Chaos Behavior in an Associative Memory Model | p. 277 |
Complexity Theory and the Future of Cognitive Modeling | p. 287 |
Summary | p. 288 |
Applications of Nonlinear Techniques in Psychology | p. 290 |
Psychomotor Skill | p. 290 |
Behavioral Time Series | p. 292 |
Decision Making | p. 294 |
Developmental Processes | p. 297 |
Perceptual and Cognitive Processes | p. 301 |
Applications to Physiological Recordings of Psychological Significance | p. 302 |
Applications in the Social Sciences | p. 311 |
Human Prediction of Event Sequences | p. 312 |
Applications in Medical Psychology | p. 319 |
Summary | p. 324 |
Epilog | p. 326 |
Glossary | p. 329 |
References | p. 335 |
Appendix | p. 358 |
Author Index | p. 362 |
Subject Index | p. 370 |
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