Literature DB >> 18648785

Performance differences in visually and internally guided continuous manual tracking movements.

Benjamin A Philip1, Yanchun Wu, John P Donoghue, Jerome N Sanes.   

Abstract

Control of familiar visually guided movements involves internal plans as well as visual and other online sensory information, though how visual and internal plans combine for reaching movements remain unclear. Traditional motor sequence learning tasks, such as the serial reaction time task, use stereotyped movements and measure only reaction time. Here, we used a continuous sequential reaching task comprised of naturalistic movements, in order to provide detailed kinematic performance measures. When we embedded pre-learned trajectories (those presumably having an internal plan) within similar but unpredictable movement sequences, participants performed the two kinds of movements with remarkable similarity, and position error alone could not reliably identify the epoch. For such embedded movements, performance during pre-learned sequences showed statistically significant but trivial decreases in measures of kinematic error, compared to performance during novel sequences. However, different sets of kinematic error variables changed significantly between learned and novel sequences for individual participants, suggesting that each participant used distinct motor strategies favoring different kinematic variables during each of the two movement types. Algorithms that incorporated multiple kinematic variables identified transitions between the two movement types well but imperfectly. Hidden Markov model classification differentiated learned and novel movements on single trials based on the above kinematic error variables with 82 +/- 5% accuracy within 244 +/- 696 ms, despite the limited extent of changes in those errors. These results suggest that the motor system can achieve markedly similar performance whether or not an internal plan is present, as only subtle changes arise from any difference between the neural substrates involved in those two conditions.

Entities:  

Mesh:

Year:  2008        PMID: 18648785      PMCID: PMC2574818          DOI: 10.1007/s00221-008-1489-3

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


  22 in total

1.  Probability detection mechanisms and motor learning.

Authors:  O V Lungu; T Wächter; T Liu; D T Willingham; J Ashe
Journal:  Exp Brain Res       Date:  2004-07-16       Impact factor: 1.972

2.  fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences.

Authors:  Raju S Bapi; K P Miyapuram; F X Graydon; K Doya
Journal:  Neuroimage       Date:  2006-06-22       Impact factor: 6.556

Review 3.  Cortical control of motor sequences.

Authors:  James Ashe; Ovidiu V Lungu; Alexandra T Basford; Xiaofeng Lu
Journal:  Curr Opin Neurobiol       Date:  2006-03-24       Impact factor: 6.627

4.  Adaptation to visual feedback delays in manual tracking: evidence against the Smith Predictor model of human visually guided action.

Authors:  R C Miall; J K Jackson
Journal:  Exp Brain Res       Date:  2006-01-20       Impact factor: 1.972

5.  The neural correlates of motor skill automaticity.

Authors:  Russell A Poldrack; Fred W Sabb; Karin Foerde; Sabrina M Tom; Robert F Asarnow; Susan Y Bookheimer; Barbara J Knowlton
Journal:  J Neurosci       Date:  2005-06-01       Impact factor: 6.167

6.  Functional MRI evidence for adult motor cortex plasticity during motor skill learning.

Authors:  A Karni; G Meyer; P Jezzard; M M Adams; R Turner; L G Ungerleider
Journal:  Nature       Date:  1995-09-14       Impact factor: 49.962

7.  Abstract and effector-specific representations of motor sequences identified with PET.

Authors:  S T Grafton; E Hazeltine; R B Ivry
Journal:  J Neurosci       Date:  1998-11-15       Impact factor: 6.167

8.  Learning of sequential movements in the monkey: process of learning and retention of memory.

Authors:  O Hikosaka; M K Rand; S Miyachi; K Miyashita
Journal:  J Neurophysiol       Date:  1995-10       Impact factor: 2.714

9.  Cognitive channels computing action distance and direction.

Authors:  R B Bhat; J N Sanes
Journal:  J Neurosci       Date:  1998-09-15       Impact factor: 6.167

10.  Neural correlates of reach errors.

Authors:  Jörn Diedrichsen; Yasmin Hashambhoy; Tushar Rane; Reza Shadmehr
Journal:  J Neurosci       Date:  2005-10-26       Impact factor: 6.709

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  1 in total

1.  Gray and white matter changes associated with tool-use learning in macaque monkeys.

Authors:  M M Quallo; C J Price; K Ueno; T Asamizuya; K Cheng; R N Lemon; A Iriki
Journal:  Proc Natl Acad Sci U S A       Date:  2009-10-09       Impact factor: 11.205

  1 in total

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