The Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology Response-time Dynamics: Evidence for Linear and Low-dimensional Nonlinear Structure in Human Choice Sequences

Response time (RT) is a commonly used measure of cognitive performance, which is usually characterized as stochastic. However, useful information may be hidden in the apparently random fluctuations of RT. Dynamical systems analysis techniques allow an exploration of the alternative hypothesis that RT fluctuations are deterministic, albeit in a complex manner. We applied careful task construction and noise-reduction and surrogate series tests to show that RT series from a forced-pace serial response-time task have low-dimensional chaotic characteristics. In Experiment 1, 80% of subjects' filtered RT series had low dimensionality, sensitive dependence on initial conditions, spectra close to 1/f, and stable attractor geometry across sessions. In Experiment 2, we showed that the size of the inter-stimulus interval (ISI) determined the number of subjects with low-dimensional chaotic series. A small ISI caused 100% of subjects to respond in the chaotic regime, whereas only 25% had a low-dimensional chaotic RT component when the ISI was large. We argue that demanding task requirements cause a reduction in the dimensionality of the dynamics, producing RT fluctuations that may reflect a response strategy for controlling RT.

[1]  Jeff Pressing,et al.  Spectral properties of human cognition and skill , 1997, Biological Cybernetics.

[2]  S. Makeig,et al.  Lapses in alertness: coherence of fluctuations in performance and EEG spectrum. , 1993, Electroencephalography and clinical neurophysiology.

[3]  George Sugihara,et al.  Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series , 1990, Nature.

[4]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[5]  C. Chatfield,et al.  Fourier Analysis of Time Series: An Introduction , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Andrew Heathcote,et al.  Screen control and timing routines for the IBM microcomputer family using a high-level language , 1988 .

[7]  G. Smith,et al.  Slowness and age: speed-accuracy mechanisms. , 1995, Psychology and aging.

[8]  Pressing,et al.  Error Correction Processes in Temporal Pattern Production. , 1998, Journal of mathematical psychology.

[9]  K. Dooley,et al.  Explaining Complex Organizational Dynamics , 1999 .

[10]  Bryan Rodgers,et al.  What does a Man do after he Makes an Error? An Analysis of Response Programming , 1977 .

[11]  S. Barton,et al.  Chaos, self-organization, and psychology. , 1994, The American psychologist.

[12]  S J Schiff,et al.  Discriminating deterministic versus stochastic dynamics in neuronal activity , 1994, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[13]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[14]  John S. Nicolis.  Chaos and information processing - a heuristic outline , 1991 .

[15]  M. Pascual.  Understanding nonlinear dynamics , 1996 .

[16]  P. Rabbitt.  How old and young subjects monitor and control responses for accuracy and speed , 1979 .

[17]  Gavan Lintern,et al.  Self-organization in connectionist models: Associative memory, dissipative structures, and thermodynamic law , 1991 .

[18]  Scott D. Brown,et al.  The power law repealed: The case for an exponential law of practice , 2000, Psychonomic bulletin & review.

[19]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[20]  E. Large,et al.  The dynamics of attending: How people track time-varying events. , 1999 .

[21]  M. Casdagli.  Chaos and Deterministic Versus Stochastic Non‐Linear Modelling , 1992 .

[22]  Richard A. Heath,et al.  Nonlinear Dynamics: Techniques and Applications in Psychology , 2000 .

[23]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[24]  David L. Gilden,et al.  Fluctuations in the Time Required for Elementary Decisions , 1997 .

[25]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[26]  van Leeuwen C,et al.  Stability and Intermittency in Large-Scale Coupled Oscillator Models for Perceptual Segmentation , 1997, Journal of mathematical psychology.

[27]  J. Cooney,et al.  A dynamic model of reaction time in a short-term memory task. , 1994, Journal of experimental child psychology.

[28]  G. Ermentrout.  Dynamic patterns: The self-organization of brain and behavior , 1997 .

[29]  James B. Ramsey,et al.  The statistical properties of dimension calculations using small data sets , 1990 .

[30]  TheilerJames,et al.  Testing for nonlinearity in time series , 1992 .

[31]  Daniel R. Ilgen,et al.  Team performance on monitoring tasks: An examination of decision errors in contexts requiring sustained attention. , 1995 .

[32]  Andy Clark,et al.  The Dynamical Challenge , 1997, Cogn. Sci..

[33]  M. Loeb,et al.  The Psychology of Vigilance , 1982 .

[34]  Gottfried Mayer-Kress,et al.  Localized measures for nonstationary time-series of physiological data , 1994, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[35]  Keith N. Clayton,et al.  INTER- AND INTRA-TRIAL DYNAMICS IN MEMORY AND CHOICE , 1996 .

[36]  Michael Creutz,et al.  Fractals and Self-Organized Criticality , 1994 .

[37]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[38]  R. Mantegna,et al.  Fractals in biology and medicine. , 1995, Chaos, solitons, and fractals.

[39]  H. Stanley,et al.  Fractals in Biology and Medicine: From DNA to the Heartbeat , 1994 .

[40]  G. Mpitsos.  The Chaos User’s Tool Kit , 1994, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[41]  R. Duncan Luce,et al.  Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .

[42]  James W. Garson.  Cognition poised at the edge of chaos: A complex alternative to a symbolic mind , 1996 .

[43]  J. A. Stewart,et al.  Nonlinear Time Series Analysis , 2015 .

[44]  Neil Brewer,et al.  Developmental changes in processing speed: Influence of speed-accuracy regulation , 1989 .

[45]  Peter V. Coveney,et al.  Frontiers of Complexity: The Search for Order in a Chaotic World, Peter Coveney and Roger Highfield. 1995. Random House, Inc., New York, NY. 480 pages. ISBN: 0-449-90832-1. $27.50 , 1996 .

[46]  Donald Laming,et al.  Information theory of choice-reaction times , 1968 .

[47]  M. Wertheimer,et al.  An investigation of the randomness of threshold measurements. , 1953, Journal of experimental psychology.

[48]  P. Grassberger,et al.  On noise reduction methods for chaotic data. , 1993, Chaos.

[49]  P. Rabbitt,et al.  The Changing Pattern of Perceptual Analytic Strategies and Response Selection with Practice in a Two-Choice Reaction time task , 1978, The Quarterly journal of experimental psychology.

[50]  Mitchell G Longstaff,et al.  A nonlinear analysis of the temporal characteristics of handwriting , 1999 .

[51]  Chris Eliasmith,et al.  The third contender: A critical examination of the Dynamicist theory of cognition , 1996 .

[52]  D. Broadbent.  Noise, paced performance and vigilance tasks. , 1953, British journal of psychology.