Fixating a small dot is a universal technique for stabilizing gaze in vision and eye movement research, and for clinical imaging of normal and diseased retinae. During fixation, microsaccades and drifts occur that presumably benefit vision, yet microsaccades compromise image stability and usurp task attention. Previous work suggested that microsaccades and smooth pursuit catch-up saccades are controlled by similar mechanisms.
View Article and Find Full Text PDFJ Neurophysiol
November 2019
Smooth pursuit is punctuated by catch-up saccades, which are thought to automatically correct sensory errors in retinal position and velocity. Recent studies have shown that the timing of catch-up saccades is susceptible to cognitive modulation, as is the timing of fixational microsaccades. Are the timing of catchup and microsaccades thus modulated by the same mechanism? Here, we test directly whether pursuit catch-up saccades and fixational microsaccades exhibit the same temporal pattern of task-related bursts and subsidence.
View Article and Find Full Text PDFModels of smooth pursuit eye movements stabilize an object's retinal image, yet pursuit is peppered with small, destabilizing "catch-up" saccades. Catch-up saccades might help follow a small, spot stimulus used in most pursuit experiments, since fewer of them occur with large stimuli. However, they can return when a large stimulus has a small central feature.
View Article and Find Full Text PDFThe human brain is extremely efficient at detecting faces in complex visual scenes, but the spatio-temporal dynamics of this remarkable ability, and how it is influenced by category-search, remain largely unknown. In the present study, human subjects were shown gradually-emerging images of faces or cars in visual scenes, while neural activity was recorded using functional magnetic resonance imaging (fMRI). Category search was manipulated by the instruction to indicate the presence of either a face or a car, in different blocks, as soon as an exemplar of the target category was detected in the visual scene.
View Article and Find Full Text PDFWhen two objects such as billiard balls collide, observers perceive that the action of one caused the motion of the other. We have previously shown (Badler, Lefèvre, & Missal, 2010) that this extends to the oculomotor domain: subjects make more predictive movements in the expected direction of causal motion than in a noncausal direction. However, predictive oculomotor and reactive psychophysical responses have never been directly compared.
View Article and Find Full Text PDFThe ability to predict upcoming events is important to compensate for relatively long sensory-motor delays. When stimuli are temporally regular, their prediction depends on a representation of elapsed time. However, it is well known that the allocation of attention to the timing of an upcoming event alters this representation.
View Article and Find Full Text PDFAnimals often make anticipatory movements to compensate for slow reaction times. Anticipatory movements can be timed using external, sensory cues, or by an internal prediction of when an event will occur. However, it is unknown whether external or internal cues dominate the anticipatory response when both are present.
View Article and Find Full Text PDFSmooth pursuit eye movements are guided largely by retinal-image motion. To compensate for neural conduction delays, the brain employs a predictive mechanism to generate anticipatory pursuit that precedes target motion (E. Kowler, 1990).
View Article and Find Full Text PDFGood performance in the sport of baseball shows that humans can determine the trajectory of a moving object and act on it under the constraint of a rule. We report here on neuronal activity in the supplementary eye field (SEF) of monkeys performing an eye movement task inspired by baseball. In "ocular baseball," a pursuit eye movement to a target is executed or withheld based on the target's trajectory.
View Article and Find Full Text PDFSeveral alternative methods for decoding the desired motor command vector from neural networks containing distributed, place-coded information have been suggested. The two most widely discussed candidate mechanisms are vector summation (VS) and a center-of-mass (CM) computation. The latter mechanism has also been called vector averaging.
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