Psychophysical and saccadic information about direction for briefly presented visual targets

Aitsebaomo, A. Philip; Bedell, Harold E.

doi:10.1016/0042-6989(92)90165-f

Cited by 28 publications

(25 citation statements)

References 31 publications

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“…Variability in saccadic landing position (error) was quantified by Aitsebaomo and Bedell (1992), but primarily for different target durations, showing a decrease with increasing target duration. Saccadic landing variability also grows with increasing eccentricity (Figure 8), which was also measured in van Beers's (2007, Figure 2; increasing scatter for landing positions) study.…”

Section: Discussionmentioning

confidence: 99%

Visual orienting in dynamic broadband (1/f) noise sequences

Rasche

Gegenfurtner

2010

Attention, Perception, & Psychophysics

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Section: Discussionmentioning

confidence: 99%

Visual orienting in dynamic broadband (1/f) noise sequences

Rasche

Gegenfurtner

2010

Attention, Perception, & Psychophysics

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“…Second, localization precision in the direction displays a strong "oblique effect": precision is much better along the horizontal and vertical meridians than in oblique directions (White et al, 1992;Westheimer, 2001). Third, localization precision varies across the visual field in the same way as visual acuity does (Yap et al, 1987;Levi and Klein, 1990;Aitsebaomo and Bedell, 1992;White et al, 1992;Westheimer, 2001Westheimer, , 2005. We quantified the first two factors using the values reported by White et al (1992): R / ϭ 6 for targets on the horizontal and vertical meridians, and R / ϭ 2 for targets in other directions.…”

Section: Modelmentioning

confidence: 99%

“…Aitsebaomo and Bedell (1992) previously showed that both sensory noise and motor noise contribute to saccadic variability. Our results extend this by quantifying their contributions.…”

Section: The Sources Of Variabilitymentioning

confidence: 99%

“…The only factor that influenced the fit parameters more strongly is the level of sensory noise. We used literature values about the precision with which visual targets are localized, but this precision can vary across subjects (Aitsebaomo and Bedell, 1992;White et al, 1992). Indeed, the observed saccade variability varied across our subjects (the raw data in Figs.…”

Section: Validity Of the Modelmentioning

confidence: 99%

“…We used the average of the results of White et al (1992) and Aitsebaomo and Bedell (1992) as the CV of localization in the R direction at large eccentricities along the horizontal meridian: 0.045. To account for the directional dependence of the precision, we applied the following scaling to the localization precision CV in both the and R directions:…”

Section: Modelmentioning

confidence: 99%

See 2 more Smart Citations

The Sources of Variability in Saccadic Eye Movements

Beers¹

2007

J. Neurosci.

156

146

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Our movements are variable, but the origin of this variability is poorly understood. We examined the sources of variability in human saccadic eye movements. In two experiments, we measured the spatiotemporal variability in saccade trajectories as a function of movement direction and amplitude. One of our new observations is that the variability in movement direction is smaller for purely horizontal and vertical saccades than for saccades in oblique directions. We also found that saccade amplitude, duration, and peak velocity are all correlated with one another. To determine the origin of the observed variability, we estimated the noise in motor commands from the observed spatiotemporal variability, while taking into account the variability resulting from uncertainty in localization of the target. This analysis revealed that uncertainty in target localization is the major source of variability in saccade endpoints, whereas noise in the magnitude of the motor commands explains a slightly smaller fraction. In addition, there is temporal variability such that saccades with a longer than average duration have a smaller than average peak velocity. This noise model has a large generality because it correctly predicts the variability in other data sets, which contain saccades starting from very different initial locations. Because the temporal noise most likely originates in movement planning, and the motor command noise in movement execution, we conclude that uncertainty in sensory signals and noise in movement planning and execution all contribute to the variability in saccade trajectories. These results are important for understanding how the brain controls movement.

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