title: Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences.
abstract: Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects' self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.
authors.affiliation: Max Planck Institute for Biological Cybernetics Tübingen, Germany ; Max Planck Institute for Intelligent Systems Tübingen, Germany ; Graduate Training Centre of Neuroscience Tübingen, Germany.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Peng
authors.affiliation: Max Planck Institute for Biological Cybernetics Tübingen, Germany ; Max Planck Institute for Intelligent Systems Tübingen, Germany ; Graduate Training Centre of Neuroscience Tübingen, Germany.
authors.forename: Tim
authors.initials: T
authors.lastname: Genewein
authors.affiliation: Max Planck Institute for Biological Cybernetics Tübingen, Germany ; Max Planck Institute for Intelligent Systems Tübingen, Germany.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Braun
keywords: Lempel-Ziv complexity; approximate entropy; conditional entropy; effective measure complexity; excess entropy; motion complexity; motion randomness
references.citation: IEEE Trans Biomed Eng. 2006 Feb;53(2):210-8
references.pmid: 16485749
references.citation: Chaos. 1995 Mar;5(1):110-117
references.pmid: 12780163
references.citation: Chaos. 2003 Mar;13(1):25-54
references.pmid: 12675408
references.citation: Proc Natl Acad Sci U S A. 1991 Mar 15;88(6):2297-301
references.pmid: 11607165
references.citation: IEEE Trans Biomed Eng. 2006 Dec;53(12 Pt 2):2606-9
references.pmid: 17152441
references.citation: Neural Comput. 2004 Apr;16(4):717-36
references.pmid: 15025827
references.citation: Anesthesiology. 2000 Mar;92(3):715-26
references.pmid: 10719951
references.citation: PLoS One. 2010 Jan 29;5(1):e8973
references.pmid: 20126409
references.citation: PLoS One. 2013 May 27;8(5):e63400
references.pmid: 23723979
references.citation: Science. 2009 May 29;324(5931):1165
references.pmid: 19389998
references.citation: Chaos. 2011 Sep;21(3):037105
references.pmid: 21974668
references.citation: Curr Biol. 2009 Feb 24;19(4):352-7
references.pmid: 19217296
references.citation: Curr Opin Neurobiol. 2012 Dec;22(6):996-1003
references.pmid: 22647641
references.citation: Med Hypotheses. 2005;65(2):211-4
references.pmid: 15922090
references.citation: Am J Physiol Heart Circ Physiol. 2000 Jun;278(6):H2039-49
references.pmid: 10843903
references.citation: PLoS Comput Biol. 2011 Mar;7(3):e1001112
references.pmid: 21483475
references.citation: IEEE Eng Med Biol Mag. 1998 May-Jun;17(3):89-94
references.pmid: 9604706
references.citation: Chaos. 2006 Dec;16(4):043102
references.pmid: 17199380
references.citation: Ann N Y Acad Sci. 2001 Dec;954:245-67
references.pmid: 11797860
references.citation: Sci Transl Med. 2013 Aug 14;5(198):198ra105
references.pmid: 23946194
references.citation: Neural Comput. 2001 Nov;13(11):2409-63
references.pmid: 11674845
references.citation: Theory Biosci. 2012 Sep;131(3):161-79
references.pmid: 22125233
references.citation: Med Hypotheses. 2008;70(1):182-5
references.pmid: 17888582
references.citation: J Mot Behav. 2000 Sep;32(3):314-20
references.pmid: 10975279
references.citation: Front Hum Neurosci. 2012 Oct 19;6:291
references.pmid: 23125827