Contemporary theories of 1/f noise in motor control

Diniz, Ana; Wijnants, Maarten; Torre, Kjerstin; Barreiros, João; Crato, Nuno; Bosman, A.M.T.; Hasselman, Fred; Cox, Ralf F. A.; Orden, Guy C. Van; Delignières, Didier

doi:10.1016/j.humov.2010.07.006

Cited by 121 publications

(113 citation statements)

References 62 publications

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“…In the social and life sciences alone it has been applied in an increasing number of studies across the different sub-disciplines such as motor development in infants (Assmann, Romano, Thiel, & Niemitz, 2007), parent-child interaction (De Graag, Cox, Hasselman, Jansen, & De Weerth, 2012;Lichtwarck-Aschoff, Hasselman, Cox, & Granic, 2012), syntactic coordination between child and caregiver (Dale & Spivey, 2006), dynamics of motor control (Diniz et al, 2011;Wijnants, Bosman, Cox, Hasselman, & Van Orden, 2011;Wijnants, Bosman, Hasselman, Cox, & Van Orden, 2009), cognitive constraints on postural stability (Shockley, Baker, Richardson, & Fowler, 2007;Shockley, Santana, & Fowler, 2003), eye-movements during conversation (D. C. Richardson, Dale, & Kirkham, 2007), insight in problem solving (Stephen, Dixon, & Isenhower, 2009), and as a novel analysis tool in cognitive neuroscience (Bianciardi et al, 2007;Schinkel, Marwan, & Kürths, 2007, 2009). The quantification measures as used in these studies often take their role as predictors or covariates next to more familiar variables in some statitsical analysis.…”

Section: Interaction Dominant Dynamics Measures: Quantifying Complex mentioning

confidence: 99%

Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns

Hasselman

2014

Preprint

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Section: Interaction Dominant Dynamics Measures: Quantifying Complex mentioning

confidence: 99%

Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns

Hasselman

2014

Preprint

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“…Although there is still controversy about the meaning of fractal scaling and its origin (Van Orden et al, 2005;Diniz et al, 2011), one position in the debate is that fractal scaling is a characteristic manifestation of self-organizing systems, which reflects the balance between independent and interdependent activity of their components. In this sense, deviations from perfect β = 1 pink noise would imply unbalancing this equilibrium in favour of either independent or interdependent activity, affecting the ability of a system to behave in a self-organized manner.…”

Section: Fractal Scaling and Self-organization In Complex Networkmentioning

confidence: 99%

Quantifying Political Self-Organization in Social Media. Fractal patterns in the Spanish 15M movement on Twitter

Aguilera

Morer²,

Barandiaran

et al. 2013

Advances in Artificial Life, ECAL 2013

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The objective of this work is to better analyse and understand social self-organization in the context of social media and political activism. More specifically, we centre our analysis in the presence of fractal scaling in the form of 1/f noise in different Twitter communication networks related to the Spanish 15M movement. We show how quantitative indexes of brown, white and pink noise correlate with qualitatively different forms of social coordination of protests: rigidly organized protests (brown noise), reactive-spontaneous protests (white noise) and complex genuinely self-organized protests (pink noise). In addition, pink noise processes present correlations that reach much further in time, maintaining a dynamical coherence that last several days, and also show a balance between mean distance and clustering coefficient within the interaction network.

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“…This scaling exponent is found to decrease with increased difficulty and an increased level of external perturbation of the response time task (cf. Diniz et al, 2011) and has been suggested to reflect the self-organization of cognitive processes (e.g., Van Orden et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Multifractal analyses of response time series: A comparative study

Ihlen

2013

Behav Res

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Response time series with a non-Gaussian distribution and long-range dependent dynamics have been reported for several cognitive tasks. Conventional monofractal analyses numerically define a long-range dependency as a single scaling exponent, but they assume that the response times are Gaussian distributed. Ihlen and Vereijken (Journal of Experimental Psychology: General, 139, 436-463, 2010) suggested multifractal extensions of the conventional monofractal analyses that are more suitable when the response time has a non-Gaussian distribution. Multifractal analyses estimate a multifractal spectrum of scaling exponents that contain the single exponent estimated by the conventional monofractal analyses. However, a comparison of the performance of multifractal analyses with behavioral variables has not yet been addressed. The present study compares the performance of seven multifractal analyses. The multifractal analyses were tested on multiplicative cascading noise that generates time series with a predefined multifractal spectrum and with a structure of variation that mimics intermittent response time variation. Time series with 1,024 and 4,096 samples were generated with additive noise and multiharmonic trends of two different magnitudes (signal-to-noise/trend ratio; 0.33 and 1). The results indicate that all multifractal analysis has individual pros and cons related to sample size, multifractality, and the presence of additive noise and trends in the response time series. The summary of pros and cons of the seven multifractal analyses provides a guideline for the choice of multifractal analyses of response time series and other behavioral variables.

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Contemporary theories of 1/f noise in motor control

Cited by 121 publications

References 62 publications

Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns

Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns

Quantifying Political Self-Organization in Social Media. Fractal patterns in the Spanish 15M movement on Twitter

Multifractal analyses of response time series: A comparative study

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