Literature DB >> 17476493

Behavioral analysis of predictive saccade tracking as studied by countermanding.

Wilsaan M Joiner1, Jung-Eun Lee, Mark Shelhamer.   

Abstract

The ability to make predictive saccadic eye movements is dependent on neural signals that anticipate the onset of a visual target. We used a novel paradigm-based on the saccade-countermanding task-as a tool to investigate rhythm saccade pacing and to provide information on the mechanisms of predictive timing. In particular, we examined the ability of normal subjects to stop a sequence of periodically paced eye movements when cued by a stop signal that was presented at different times with respect to the last target of the sequence (stop signal delay, SSD). The timing of the stop signal affected the ability to stop the saccadic sequence (make a saccade to a central target rather than to the peripheral alternating targets) in different ways, depending on the preceding tracking behavior. For the same SSD, subjects cancelled fewer trials during predictive tracking (promoted by tracking targets alternating at a fast pacing rate, 1.0 Hz) than during reactive tracking (tracking alternating targets at a low pacing rate, 0.2 Hz). In addition, on non-canceled trials, there was an increase in the delay of the corrective saccade to the central target with increasing SSD for pacing at 0.2 Hz, but the timing of the corrective saccade remained near constant for 1.0 Hz pacing. In examining the timing between movements, we estimate that the repetitive GO process that drives the saccades during predictive tracking begins earlier and has a shorter duration than the repetitive GO process during reactive tracking. These behavioral results provide further insight into the initiation process of predictive responses. In particular, the reduced reaction time and the corresponding short duration of the predictive process may result from a faster accumulation of neuronal discharge to a relatively fixed threshold.

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Year:  2007        PMID: 17476493     DOI: 10.1007/s00221-007-0933-0

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


  30 in total

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Authors:  V Stuphorn; T L Taylor; J D Schall
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3.  Saccadic countermanding: a comparison of central and peripheral stop signals.

Authors:  K N Asrress; R H Carpenter
Journal:  Vision Res       Date:  2001-09       Impact factor: 1.886

4.  Control of saccade initiation in a countermanding task using visual and auditory stop signals.

Authors:  D W Cabel; I T Armstrong; E Reingold; D P Munoz
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5.  Cancelling of pursuit and saccadic eye movements in humans and monkeys.

Authors:  Krista Kornylo; Natalie Dill; Melissa Saenz; Richard J Krauzlis
Journal:  J Neurophysiol       Date:  2003-06       Impact factor: 2.714

6.  Canceling planned action: an FMRI study of countermanding saccades.

Authors:  Clayton E Curtis; Michael W Cole; Vikas Y Rao; Mark D'Esposito
Journal:  Cereb Cortex       Date:  2004-12-22       Impact factor: 5.357

7.  Countermanding eye-head gaze shifts in humans: marching orders are delivered to the head first.

Authors:  Brian D Corneil; James K Elsley
Journal:  J Neurophysiol       Date:  2005-02-23       Impact factor: 2.714

8.  Countermanding saccades in humans.

Authors:  D P Hanes; R H Carpenter
Journal:  Vision Res       Date:  1999-08       Impact factor: 1.886

9.  Neural control of voluntary movement initiation.

Authors:  D P Hanes; J D Schall
Journal:  Science       Date:  1996-10-18       Impact factor: 47.728

10.  Children's and adults' predictive saccades to square-wave targets.

Authors:  S M Ross; L E Ross
Journal:  Vision Res       Date:  1987       Impact factor: 1.886

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  3 in total

1.  Measurement of the extraocular spike potential during saccade countermanding.

Authors:  David C Godlove; Anna K Garr; Geoffrey F Woodman; Jeffrey D Schall
Journal:  J Neurophysiol       Date:  2011-04-13       Impact factor: 2.714

Review 2.  Microsaccade production during saccade cancelation in a stop-signal task.

Authors:  David C Godlove; Jeffrey D Schall
Journal:  Vision Res       Date:  2014-11-06       Impact factor: 1.886

3.  A model of time estimation and error feedback in predictive timing behavior.

Authors:  Wilsaan M Joiner; Mark Shelhamer
Journal:  J Comput Neurosci       Date:  2008-06-19       Impact factor: 1.621

  3 in total

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