Literature DB >> 28733770

Distinct and flexible rates of online control.

John de Grosbois1,2,3, Luc Tremblay4,5,6.   

Abstract

Elliott et al. (Hum Mov Sci 10:393-418, 1991) proposed a pseudocontinuous model of online control whereby overlapping corrections lead to the appearance of smooth kinematic profiles in the presence of online feedback. More recently, it was also proposed that online control is not a singular process [see Elliott et al. (Psychol Bull 136(6):1023-1044, 2010)]. However, support for contemporary models of online control were based on methodologies that were not designed to be sensitive to different online control sub-processes. The current study sought to evaluate the possibility of multiple distinct (i.e., visual and non-visual) mechanisms contributing to the control of reaching movements completed in either a full-vision, a no-vision, or a no-vision memory-guided condition. Frequency domain analysis was applied to the acceleration traces of reaching movements. In an attempt to elicit a modulation in the online control mechanisms, these movements were completed at two levels of spatio-temporal constraint, namely with 10 and 30 cm target distances. One finding was that performance in the full-vision relative to both no-vision conditions could be distinguished via two distinct frequency peaks. Increases in the peak magnitude at the lower frequencies were associated with visuomotor mechanisms and increases in the peak magnitude at the higher frequencies were associated with non-visual mechanisms. In addition, performance to the 30-cm target led to a lower peak at a lower frequency relative to the 10 cm target, indicating that the iterative rates of visuomotor control mechanisms are flexible and sensitive to the spatio-temporal constraints of the associated movement.

Mesh:

Year:  2017        PMID: 28733770     DOI: 10.1007/s00426-017-0888-0

Source DB:  PubMed          Journal:  Psychol Res        ISSN: 0340-0727


  66 in total

1.  Rapid visual feedback processing in single-aiming movements.

Authors:  H Z Zelaznik; B Hawkins; L Kisselburgh
Journal:  J Mot Behav       Date:  1983-09       Impact factor: 1.328

2.  Learning to optimize speed, accuracy, and energy expenditure: a framework for understanding speed-accuracy relations in goal-directed aiming.

Authors:  Digby Elliott; Steven Hansen; Jocelyn Mendoza; Luc Tremblay
Journal:  J Mot Behav       Date:  2004-09       Impact factor: 1.328

3.  Determinants of offline processing of visual information for the control of reaching movements.

Authors:  Pierre-Michel Bernier; Romeo Chua; Ian M Franks; Michael A Khan
Journal:  J Mot Behav       Date:  2006-09       Impact factor: 1.328

Review 4.  Visually-guided correction of hand reaching movements: The neurophysiological bases in the cerebral cortex.

Authors:  P S Archambault; S Ferrari-Toniolo; R Caminiti; A Battaglia-Mayer
Journal:  Vision Res       Date:  2014-09-28       Impact factor: 1.886

5.  Effects of robotic guidance on sensorimotor control: planning vs. online control?

Authors:  Gerome A Manson; Maria Alekhina; Shirley L Srubiski; Camille K Williams; Arindam Bhattacharjee; Luc Tremblay
Journal:  NeuroRehabilitation       Date:  2014       Impact factor: 2.138

Review 6.  The influence of visual target information on the online control of movements.

Authors:  Fabrice R Sarlegna; Pratik K Mutha
Journal:  Vision Res       Date:  2014-07-16       Impact factor: 1.886

7.  The coordination of arm movements: an experimentally confirmed mathematical model.

Authors:  T Flash; N Hogan
Journal:  J Neurosci       Date:  1985-07       Impact factor: 6.167

8.  The effect of viewing the static hand prior to movement onset on pointing kinematics and variability.

Authors:  Y Rossetti; G Stelmach; M Desmurget; C Prablanc; M Jeannerod
Journal:  Exp Brain Res       Date:  1994       Impact factor: 1.972

9.  Humans use continuous visual feedback from the hand to control fast reaching movements.

Authors:  Jeffrey A Saunders; David C Knill
Journal:  Exp Brain Res       Date:  2003-08-06       Impact factor: 1.972

10.  The latency for correcting a movement depends on the visual attribute that defines the target.

Authors:  Margot M Veerman; Eli Brenner; Jeroen B J Smeets
Journal:  Exp Brain Res       Date:  2008-02-07       Impact factor: 1.972

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  2 in total

1.  Temporospatial Alterations in Upper-Limb and Mallet Control Underlie Motor Learning in Marimba Performance.

Authors:  Tristan Loria; Melissa Tan; John de Grosbois; Aiyun Huang; Michael H Thaut
Journal:  Front Psychol       Date:  2022-02-10

2.  Music-based intervention drives paretic limb acceleration into intentional movement frequencies in chronic stroke rehabilitation.

Authors:  Tristan Loria; John de Grosbois; Catherine Haire; Veronica Vuong; Nina Schaffert; Luc Tremblay; Michael H Thaut
Journal:  Front Rehabil Sci       Date:  2022-10-03
  2 in total

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