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Testing a simplified method for measuring velocity integration in saccades using a manipulation of target contrast.

Etchells PJ, Benton CP, Ludwig CJ, Gilchrist ID - Front Psychol (2011)

Bottom Line: Observers generated saccades to one of two moving targets which were presented at high (80%) or low (7.5%) contrast.The extent to which the saccade endpoint can be accounted for as a weighted combination of the pre- or post-step velocities allows for identification of the temporal velocity integration window.Our results show that the temporal integration window takes longer to peak in the low when compared to high contrast condition.

View Article: PubMed Central - PubMed

Affiliation: School of Experimental Psychology, University of Bristol Bristol, UK.

ABSTRACT
A growing number of studies in vision research employ analyses of how perturbations in visual stimuli influence behavior on single trials. Recently, we have developed a method along such lines to assess the time course over which object velocity information is extracted on a trial-by-trial basis in order to produce an accurate intercepting saccade to a moving target. Here, we present a simplified version of this methodology, and use it to investigate how changes in stimulus contrast affect the temporal velocity integration window used when generating saccades to moving targets. Observers generated saccades to one of two moving targets which were presented at high (80%) or low (7.5%) contrast. In 50% of trials, target velocity stepped up or down after a variable interval after the saccadic go signal. The extent to which the saccade endpoint can be accounted for as a weighted combination of the pre- or post-step velocities allows for identification of the temporal velocity integration window. Our results show that the temporal integration window takes longer to peak in the low when compared to high contrast condition. By enabling the assessment of how information such as changes in velocity can be used in the programming of a saccadic eye movement on single trials, this study describes and tests a novel methodology with which to look at the internal processing mechanisms that transform sensory visual inputs into oculomotor outputs.

No MeSH data available.


Related in: MedlinePlus

The time course of a saccade from target detection to the end of the saccade. During this time, average velocity is estimated using a weighting window similar in nature to the one shown here (red solid line). The target is assumed to move at this averaged velocity during the prediction period. This information can then be used to determine where the target is likely to be located at the end of the saccade.
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Figure 1: The time course of a saccade from target detection to the end of the saccade. During this time, average velocity is estimated using a weighting window similar in nature to the one shown here (red solid line). The target is assumed to move at this averaged velocity during the prediction period. This information can then be used to determine where the target is likely to be located at the end of the saccade.

Mentions: Our model is not a process model that specifies the visual mechanisms involved in velocity estimation. However, there is a process interpretation of the model, which is illustrated in Figure 1. We assume that during the latency period object velocity is estimated by convolving the input velocities with some temporal filter (Benton and Curran, 2009) such as that seen in Figure 1. This operation is analogous to computing a running, weighted average of the input. The temporal integration performed by the filter necessarily results in a certain amount of blurring of the velocity information when the velocity is variable. As a result, the observed endpoints may not simply reflect either the pre-step velocity or the post-step velocity, but may be driven by intermediate velocity estimates. The prediction period shown in the figure is assumed to consist of the dead-time and the saccade duration itself.


Testing a simplified method for measuring velocity integration in saccades using a manipulation of target contrast.

Etchells PJ, Benton CP, Ludwig CJ, Gilchrist ID - Front Psychol (2011)

The time course of a saccade from target detection to the end of the saccade. During this time, average velocity is estimated using a weighting window similar in nature to the one shown here (red solid line). The target is assumed to move at this averaged velocity during the prediction period. This information can then be used to determine where the target is likely to be located at the end of the saccade.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3108583&req=5

Figure 1: The time course of a saccade from target detection to the end of the saccade. During this time, average velocity is estimated using a weighting window similar in nature to the one shown here (red solid line). The target is assumed to move at this averaged velocity during the prediction period. This information can then be used to determine where the target is likely to be located at the end of the saccade.
Mentions: Our model is not a process model that specifies the visual mechanisms involved in velocity estimation. However, there is a process interpretation of the model, which is illustrated in Figure 1. We assume that during the latency period object velocity is estimated by convolving the input velocities with some temporal filter (Benton and Curran, 2009) such as that seen in Figure 1. This operation is analogous to computing a running, weighted average of the input. The temporal integration performed by the filter necessarily results in a certain amount of blurring of the velocity information when the velocity is variable. As a result, the observed endpoints may not simply reflect either the pre-step velocity or the post-step velocity, but may be driven by intermediate velocity estimates. The prediction period shown in the figure is assumed to consist of the dead-time and the saccade duration itself.

Bottom Line: Observers generated saccades to one of two moving targets which were presented at high (80%) or low (7.5%) contrast.The extent to which the saccade endpoint can be accounted for as a weighted combination of the pre- or post-step velocities allows for identification of the temporal velocity integration window.Our results show that the temporal integration window takes longer to peak in the low when compared to high contrast condition.

View Article: PubMed Central - PubMed

Affiliation: School of Experimental Psychology, University of Bristol Bristol, UK.

ABSTRACT
A growing number of studies in vision research employ analyses of how perturbations in visual stimuli influence behavior on single trials. Recently, we have developed a method along such lines to assess the time course over which object velocity information is extracted on a trial-by-trial basis in order to produce an accurate intercepting saccade to a moving target. Here, we present a simplified version of this methodology, and use it to investigate how changes in stimulus contrast affect the temporal velocity integration window used when generating saccades to moving targets. Observers generated saccades to one of two moving targets which were presented at high (80%) or low (7.5%) contrast. In 50% of trials, target velocity stepped up or down after a variable interval after the saccadic go signal. The extent to which the saccade endpoint can be accounted for as a weighted combination of the pre- or post-step velocities allows for identification of the temporal velocity integration window. Our results show that the temporal integration window takes longer to peak in the low when compared to high contrast condition. By enabling the assessment of how information such as changes in velocity can be used in the programming of a saccadic eye movement on single trials, this study describes and tests a novel methodology with which to look at the internal processing mechanisms that transform sensory visual inputs into oculomotor outputs.

No MeSH data available.


Related in: MedlinePlus