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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

Comparison of the weighting function fits based on the original Gamma model (green), the simplified Gamma model (red), and a Gaussian (blue). The top panel shows the participant data set with the highest correlation between the model fits (P5).The bottom panel shows the participant data set with the lowest correlation between the model fits (P2).
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Figure 6: Comparison of the weighting function fits based on the original Gamma model (green), the simplified Gamma model (red), and a Gaussian (blue). The top panel shows the participant data set with the highest correlation between the model fits (P5).The bottom panel shows the participant data set with the lowest correlation between the model fits (P2).

Mentions: Table 1 shows the values of the BICs for all four models and observers, the summed totals across the sample, as well as the BICs for the fits of the saturated Gamma functions for each observer. The Gamma function is the clear overall winner with the lowest BIC – the BIC difference with the saturated Gaussian model is 41, which corresponds to a corrected likelihood-ratio, or Bayes factor, of greater than 1000 (Wagenmakers, 2007). Thus it is clear that fitting the data with a Gaussian function results in a less desirable fit of the data; however, the Gaussian still offers a considerable advantage in terms of the interpretability of the parameters. Moreover, inspection of a plot of the weightings based on the best-fitting Gaussian and Gamma (both full and simplified versions) models shows that all three functions fit the data reasonably well (see Figure 6).


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)

Comparison of the weighting function fits based on the original Gamma model (green), the simplified Gamma model (red), and a Gaussian (blue). The top panel shows the participant data set with the highest correlation between the model fits (P5).The bottom panel shows the participant data set with the lowest correlation between the model fits (P2).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Comparison of the weighting function fits based on the original Gamma model (green), the simplified Gamma model (red), and a Gaussian (blue). The top panel shows the participant data set with the highest correlation between the model fits (P5).The bottom panel shows the participant data set with the lowest correlation between the model fits (P2).
Mentions: Table 1 shows the values of the BICs for all four models and observers, the summed totals across the sample, as well as the BICs for the fits of the saturated Gamma functions for each observer. The Gamma function is the clear overall winner with the lowest BIC – the BIC difference with the saturated Gaussian model is 41, which corresponds to a corrected likelihood-ratio, or Bayes factor, of greater than 1000 (Wagenmakers, 2007). Thus it is clear that fitting the data with a Gaussian function results in a less desirable fit of the data; however, the Gaussian still offers a considerable advantage in terms of the interpretability of the parameters. Moreover, inspection of a plot of the weightings based on the best-fitting Gaussian and Gamma (both full and simplified versions) models shows that all three functions fit the data reasonably well (see Figure 6).

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