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Dynamical Cognitive Science

Dynamical Cognitive Science

by Lawrence M. Ward

ISBN-10: 0262232170

ISBN-13: 9780262232173

Pub. Date: 12/01/2001

Publisher: MIT Press

Dynamical Cognitive Science makes available to the cognitive science community the analytical tools and techniques of dynamical systems science, adding the variables of change and time to the study of human cognition. The unifying theme is that human behavior is an "unfolding in time" whose study should be augmented by the application of time-sensitive tools


Dynamical Cognitive Science makes available to the cognitive science community the analytical tools and techniques of dynamical systems science, adding the variables of change and time to the study of human cognition. The unifying theme is that human behavior is an "unfolding in time" whose study should be augmented by the application of time-sensitive tools from disciplines such as physics, mathematics, and economics, where change over time is of central importance.

The book provides a fast-paced, comprehensive introduction to the application of dynamical systems science to the cognitive sciences. Topics include linear and nonlinear time series analysis, chaos theory, complexity theory, relaxation oscillators, and metatheoretical issues of modeling and theory building. Tools and techniques are discussed in the context of their application to basic cognitive science problems, including perception, memory, psychophysics, judgment and decision making, and consciousness. The final chapter summarizes the contemporary study of consciousness and suggests how dynamical approaches to cognitive science can help to advance our understanding of this central concept.

Product Details

MIT Press
Publication date:
Bradford Books Series
Edition description:
New Edition
Product dimensions:
6.00(w) x 9.00(h) x 1.00(d)
Age Range:
18 Years

Table of Contents

Chapter 1Magic, Ritual, and Dynamics1
1.1Magic and Ritual1
Chapter 2Sequence9
2.1The Serial Universe9
2.2The Problem of Serial Order in Behavior11
2.3Markovian Analysis of Behavior13
Chapter 3Rhythms of Behavior17
3.1The Dance of Life17
3.2Music and Rhythm19
3.3Rhythms in the Brain20
Chapter 4Time25
4.2The Arrow of Time28
4.3Measuring Time30
Chapter 5Cognitive Processes and Time35
5.1Temporal Unfolding of Cognitive Behavior35
5.2Timing of Cognitive Behavior40
Chapter 6Systems and General Systems Theory45
6.2General Systems Theory48
6.3Dynamical Systems Theory50
Chapter 7Science and Theory53
7.1The Mandala of Science53
7.2Formal Theories56
7.3Principle of Complementarity59
Chapter 8Dynamical versus Statical Models61
8.1Theories, Models, and Data61
8.2Statical Models62
8.3Dynamical Models64
8.4Why We Need Both Statical and Dynamical Models68
Chapter 9Dynamical and Structural Models71
9.1Structural Models71
9.2Graph Theory72
9.3Interplay between Dynamical and Structural Models78
Chapter 10Deterministic versus Stochastic Dynamical Models81
10.1Deterministic Models81
10.2Stochastic Models84
10.3Do We Need Both?87
Chapter 11Linear Time Series Analysis89
11.1Time Series and Noise89
11.2ARIMA (p,d,q)90
11.3ARIMA Model of Time Estimation94
11.4Mixed Regression-ARIMA Model of Psychophysical Judgment96
Chapter 12Probability Theory and Stochastic Models99
12.1Dynamical Cognitive Science and Mathematics99
12.2Stochastic Processes: A Random Walk to Ruin100
12.3Critical Points in Stochastic Models103
12.4Ergodicity and the Markov Property105
Chapter 13Stochastic Models in Physics107
13.1The Master Equation107
13.2Quantum Physics110
13.3Complementarity Redux113
Chapter 14Noise115
14.1What Is Noise?115
14.2Probabilistic Description of Noise119
14.3Spectral Properties of Noise122
Chapter 15Colored Noise125
15.1The Ubiquity of Colored Noise125
15.2The Vicissitudes of the Exponent [alpha]129
15.3Colored Noise in Living Systems131
Chapter 161/f Noise in Human Cognition135
16.1Music and Time Perception135
16.2Reaction Time138
Chapter 171/f Noise in the Brain145
17.1Neural Activity145
17.2Magnetoencephalogram Recordings147
17.3Electroencephalogram and Event-Related Potential Recordings150
Chapter 18Models of 1/f Noise155
18.1The Simplest Case155
18.2Multiplicative Noise156
18.3Self-Organized Criticality157
18.4Center-Surround Neural Network160
Chapter 19Statistical Theory of 1/f Noise165
19.1What Must Be Explained165
19.2Queuing in a Wire166
19.3Arima (1, 0, 0)169
19.4Multifractals and Wild Self-Affinity171
Chapter 20Stochastic Resonance175
20.1What is Stochastic Resonance?175
20.2Stochastic Resonance in a Threshold Detector179
Chapter 21Stochastic Resonance and Perception183
21.1Detection of Weak Signals by Animals183
21.2Stochastic Resonance in Human Perception188
Chapter 22Stochastic Resonance in the Brain193
22.1Stochastic Resonance in Neurons193
22.2Neural Networks196
Chapter 23Chaos201
23.1Chaos Is Not What You Think It Is201
23.2What Chaos Really Is203
23.3Phase Space Drawings and Strange Attractors207
Chapter 24Chaos and Randomness209
24.1A Random Walk through the Logistic Difference Equation209
24.2Dimensionality of an Attractor213
24.3Chaos and Noise215
Chapter 25Nonlinear Time Series Analysis219
25.1State Space Reconstruction219
25.2Out-of-Sample Forecasting221
25.3Surrogate Data226
Chapter 26Chaos in Human Behavior?229
26.1Could Unexplained Variance Be Chaos?229
26.2Nonlinear Forecasting Analysis of Time Estimation230
26.3Nonlinear Analysis of Mental Illness233
26.4Memory and the Logistic Difference Equation235
Chapter 27Chaos in the Brain?237
27.1The Smell of Chaos237
27.2Dimensionality of the Electroencephalogram240
27.3Chaotic Event-Related Potentials?242
Chapter 28Perception of Sequence245
28.1The Gambler's Fallacy245
28.2Estimation of Short-Run Probabilities247
28.3Evolution of Contingency Perception251
Chapter 29Can People Behave Randomly?255
29.3Sequential Dependencies and Extrasensory Perception261
Chapter 30Can People Behave Chaotically?263
30.2Not Really265
30.3Heuristics and Chaos268
Chapter 31Relaxation Oscillators: A Foundation for Dynamical Modeling273
31.1A Brief Taxonomy of Oscillators273
31.2The van der Pol Relaxation Oscillator277
31.3Noisy Oscillators in the Brain280
Chapter 32Evolution and Ecology of Cognition285
32.1Evolution of Cognition285
32.2Ecology of Cognition291
Chapter 33Dynamical Cognitive Neuroscience295
33.1Brain Imaging296
33.2Brain Dynamics299
33.3Hybrid Models303
Chapter 34Dynamical Computation305
34.1Numerical Methods305
34.2Neural Network Models308
34.3Exotic Computers312
Chapter 35Dynamical Consciousness315
35.2Unity of Science324

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