Literature DB >> 24531639

Fitts' index of difficulty predicts the 1/f structure of movement amplitude time series.

Andrew B Slifkin1, Jeffrey R Eder.   

Abstract

Studies using a variety of experimental tasks have established that when humans repeatedly produce an action, fluctuations in action output are highest at the lowest frequencies and fluctuation magnitude (power) systematically declines as frequency increases. Such time series structure is termed pink noise. However, the appearance of pink noise seems to be limited to tasks where action is executed in the absence of task-related feedback. A few studies have demonstrated that when action was executed in the presence of task-related feedback, power was evenly distributed across all spectral frequencies--i.e., white noise was revealed. Here, participants produced cyclical aiming movements under visual feedback conditions and we sought to determine whether variations of both the movement amplitude requirement (A) and the target width (W)--in the form of the index of difficulty [ID = log2(2A/W)]--would predict the structure of movement amplitude (MA) time series. There were two ID levels, and there was a small- and large-scale version of each ID: The A and W values of the large-scale version were twice those used for the small-scale version. Given that increases in ID are known to induce increased reliance on the available visual feedback, we predicted an ID-induced shift in MA time series structure from pink to white noise. Indeed, that is what we found. Further, there were no changes in MA structure when scale level changed within each ID level. Such scale invariance of MA time series structure reinforces the notion that MA structure depends on the combined influence of A and W.

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Year:  2014        PMID: 24531639     DOI: 10.1007/s00221-014-3834-z

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  15 in total

1.  1/f-type fluctuation in human visuomotor transformation.

Authors:  Makoto Miyazaki; Yasoichi Nakajima; Hiroshi Kadota; Kazuyoshi Chitose; Tatsuyuki Ohtsuki; Kazutoshi Kudo
Journal:  Neuroreport       Date:  2004-05-19       Impact factor: 1.837

2.  Amplitude requirements, visual information, and the spatial structure of movement.

Authors:  Andrew B Slifkin; Jeffrey R Eder
Journal:  Exp Brain Res       Date:  2012-06-21       Impact factor: 1.972

3.  Aiming for the future: prospective action difficulty, prescribed difficulty, and Fitts' law.

Authors:  Andrew B Slifkin; Suzanne M Grilli
Journal:  Exp Brain Res       Date:  2006-06-13       Impact factor: 1.972

4.  Inferring online and offline processing of visual feedback in target-directed movements from kinematic data.

Authors:  Michael A Khan; Ian M Franks; Digby Elliott; Gavin P Lawrence; Romeo Chua; Pierre-Michel Bernier; Steve Hansen; Daniel J Weeks
Journal:  Neurosci Biobehav Rev       Date:  2006-07-12       Impact factor: 8.989

5.  Target dimension affects 1/f noise in aiming.

Authors:  André B Valdez; Eric L Amazeen
Journal:  Nonlinear Dynamics Psychol Life Sci       Date:  2009-10

6.  Motor learning is optimally tuned to the properties of motor noise.

Authors:  Robert J van Beers
Journal:  Neuron       Date:  2009-08-13       Impact factor: 17.173

7.  Optimality in human motor performance: ideal control of rapid aimed movements.

Authors:  D E Meyer; R A Abrams; S Kornblum; C E Wright; J E Smith
Journal:  Psychol Rev       Date:  1988-07       Impact factor: 8.934

8.  Processing of visual feedback in rapid movements.

Authors:  S W Keele; M I Posner
Journal:  J Exp Psychol       Date:  1968-05

9.  Using 1/f noise to examine planning and control in a discrete aiming task.

Authors:  André B Valdez; Eric L Amazeen
Journal:  Exp Brain Res       Date:  2008-02-19       Impact factor: 1.972

10.  A trade-off study revealing nested timescales of constraint.

Authors:  M L Wijnants; R F A Cox; F Hasselman; A M T Bosman; G Van Orden
Journal:  Front Physiol       Date:  2012-05-23       Impact factor: 4.566

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  2 in total

1.  Non-linear Amplification of Variability Through Interaction Across Scales Supports Greater Accuracy in Manual Aiming: Evidence From a Multifractal Analysis With Comparisons to Linear Surrogates in the Fitts Task.

Authors:  Christopher A Bell; Nicole S Carver; John A Zbaracki; Damian G Kelty-Stephen
Journal:  Front Physiol       Date:  2019-08-07       Impact factor: 4.566

2.  Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints.

Authors:  Grégoire Vergotte; Stéphane Perrey; Muthuraman Muthuraman; Stefan Janaqi; Kjerstin Torre
Journal:  Front Physiol       Date:  2018-07-10       Impact factor: 4.566

  2 in total

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