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Chaos / Volume 19 / Issue 1 / FOCUS ISSUE: NONLINEAR DYNAMICS IN COGNITIVE AND NEURAL SYSTEMS

Chaos 19, 015102 (2009); http://dx.doi.org/10.1063/1.3072788 (12 pages)

Mental states as macrostates emerging from brain electrical dynamics

Carsten Allefeld, Harald Atmanspacher, and Jiří Wackermann

Department of Empirical and Analytical Psychophysics, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, 79100 Germany

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(Received 23 October 2008; accepted 29 December 2008; published online 31 March 2009)

Psychophysiological correlations form the basis for different medical and scientific disciplines, but the nature of this relation has not yet been fully understood. One conceptual option is to understand the mental as “emerging” from neural processes in the specific sense that psychology and physiology provide two different descriptions of the same system. Stating these descriptions in terms of coarser- and finer-grained system states (macro- and microstates), the two descriptions may be equally adequate if the coarse-graining preserves the possibility to obtain a dynamical rule for the system. To test the empirical viability of our approach, we describe an algorithm to obtain a specific form of such a coarse-graining from data, and illustrate its operation using a simulated dynamical system. We then apply the method to an electroencephalographic recording, where we are able to identify macrostates from the physiological data that correspond to mental states of the subject.

© 2009 American Institute of Physics

Lead Paragraph

The question of how it is to be understood that physiological processes can give rise to psychological phenomena—moods, cognitive modes, sleep stages, being conscious/unconscious—has already been the subject of much philosophical and scientific debate, but has still not been satisfactorily settled. In this paper, we follow the line of thought that conceives of the domain of the mental as something that “emerges” from the physical, but argue that this only becomes a proper answer if the term emergence can be given a precise meaning. Following concepts that arose in theoretical physics, in particular statistical mechanics and nonlinear dynamics, we propose that the mental and the physical should be understood as different descriptions of the same system. A description is given in terms of the states the system can assume, and it becomes especially useful if it is possible to formulate a dynamical law, that is a rule that determines the change of the system state over time. In this case, the relation between the lower-level description in the form of (physiologically characterized) microstates and the higher-level description in the form of (psychologically characterized) macrostates is given by a so-called Markov coarse-graining. In order to give empirical support for the viability of these ideas, we turn to a special case in which the macrostates are metastable states, defined by areas in the state space within which the system stays for prolonged periods of time. We describe an algorithm to identify metastable states from empirical data, and we illustrate its operation using data from a simulated system. The method is then applied to a recording of brain electric (electroencephalographic, EEG) data from a patient suffering from a form of epilepsy characterized by frequent brief seizures during which the subject becomes mentally absent. We show that our algorithm is able to identify the segments of the recording belonging to normal and paroxysmal EEG, respectively. The method is therefore capable of identifying metastable macrostates from the physiological data which closely correspond to mental states of the subject, providing in this first test case support for the viability of our theoretical approach to the nature of the relation between physiology and psychology.

Article Outline

  1. INTRODUCTION
  2. EMERGENCE IN DYNAMICAL SYSTEMS
  3. IDENTIFYING METASTABLE MACROSTATES FROM DATA
    1. Discretization of the microstate space
    2. Microstate dynamics
    3. Almost invariant sets
    4. Macrostates and time scales
  4. Example: A system with four metastable macrostates
  5. APPLICATION TO EEG DATA
    1. Original data state space
    2. Amplitude vector state space
  6. CONCLUSION

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KEYWORDS and PACS

Keywords

electroencephalography, neurophysiology, physiological models, psychology

PACS

ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1063/1.3072788

PUBLICATION DATA

ISSN

1054-1500 (print)  
1089-7682 (online)

Publisher

$abstract.society.value is a Member of CrossRef American Institute of Physics

For access to fully linked references, you need to log in.
    Allefeld, C. and Bialonski, S., “Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains,” Phys. Rev. E 76, 066207 (2007).


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