Spatial localization without visual references

White, Janis M.; Levi, Dennis M.; Aitsebaomo, A. Philip

doi:10.1016/0042-6989(92)90243-c

Cited by 53 publications

(31 citation statements)

References 40 publications

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“…First, localization of peripheral targets is more precise in the direction than in the R direction (Yap et al, 1987;White et al, 1992;Westheimer, 2005). 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.…”

Section: Modelmentioning

confidence: 79%

“…Three factors determine this precision. First, localization of peripheral targets is more precise in the direction than in the R direction (Yap et al, 1987;White et al, 1992;Westheimer, 2005). 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).…”

Section: Modelmentioning

confidence: 97%

“…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%

“…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. The way in which visual acuity varies across the visual field was estimated from the data of Wertheim (1894).…”

Section: 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%

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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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Section: Modelmentioning

confidence: 79%

Section: Modelmentioning

confidence: 97%

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

See 3 more Smart Citations

The Sources of Variability in Saccadic Eye Movements

Beers¹

2007

J. Neurosci.

156

146

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Spatial localization: tests of a two-process model

Adam¹,

Paas²,

Ekering³

et al. 1995

Exp Brain Res

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The present study examined the recently proposed two-process model of localization performance in which a shift of attention, providing coarse location information, is followed by a saccadic eye movement, providing fine location information. In experiment 1 the nature of the localization response was manipulated. In contrast to the indirect response mode used in the study by Adam et al., i.e., manipulating the "arrow" keys to move the cursor to the target location, experiment 1 required subjects to point to the target location. The high degree of similarity between the pattern of results obtained with the pointing and cursor response indicated that performance in the localization paradigm was not differentially affected by the nature of the required response. In experiment 2 the characteristics of the backward masking stimulus was manipulated by employing three masking conditions: (1) a long-duration mask; (2) a short-duration mask (100 ms); and (3) a no-mask condition. Results showed that the long-duration mask caused interference at short and facilitation at long intervals between onset of target and mask; the short-duration mask caused interference only at short intervals. Overall the findings were consistent with the two-process model of localization performance.

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Visual information throughout a reach determines endpoint precision

Ma-Wyatt

McKee

2006

Exp Brain Res

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People make rapid, goal-directed movements to interact with their environment. Because these movements have consequences, it is important to be able to control them with a high level of precision and accuracy. Our hypothesis is that vision guides rapid hand movements, thereby enhancing their accuracy and precision. To test this idea, we asked observers to point to a briefly presented target (110 ms). We measured the impact of visual information on endpoint precision by using a shutter to close off view of the hand 50, 110 and 250 ms into the reach. We found that precision was degraded if the view of the hand was restricted at any time during the reach, despite the fact that the target disappeared long before the reach was completed. We therefore conclude that vision keeps the hand on the planned trajectory. We then investigated the effects of a perturbation of target position during the reach. For these experiments, the target remained visible until the reach was completed. The target position was shifted at 110, 180 or 250 ms into the reach. Early shifts in target position were easily compensated for, but late shifts led to a shift in the mean position of the endpoints; observers pointed to the center of the two locations, as a kind of best bet on the position of the target. Visual information is used to guide the hand throughout a reach and has a significant impact on endpoint precision.

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Spatial localization without visual references

Cited by 53 publications

References 40 publications

The Sources of Variability in Saccadic Eye Movements

The Sources of Variability in Saccadic Eye Movements

Spatial localization: tests of a two-process model

Visual information throughout a reach determines endpoint precision

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