Guiding Eye Movements For Feature Based Shape Matching

We introduce a novel method for shape-based image database search that uses saccadic targeting for local feature choice. A simulated multiresolution sensor is directed toward salient regions of an image in a series of saccadic movements. At each fixation point 1 a region of the retinal image is stored for later matching by correlation. The utility of this approach is demonstrated on an 86 image database

Priming of Pop-Out Does not Affect the Shooting Line Illusion

We combined a shooting-line illusion with a visual search pop-out task in an effort to determine whether priming of pop-out was due to acceleratcd processing of visual information in the primed dimension. While the priming effect and the line-motion percept were replicated, the visual search task showed no influence on the perceived direction of line motion. These results indicate that the priming effect does not accelerate early visual processing.National Institutes of Health-National Eye Institute (EY05087, 49620-93-1-0407

A neural model of the saccade generator in the reticular formation

A neural model is developed of the neural circuitry in the reticular formation that is used to generate saccadic eye movements. The model simulates the behavior of identified cell types — such as long-lead burst neurons, short-lead excitatory and inhibitory burst neurons, omnipause neurons, and tonic neurons — under many experimental conditions. Simulated phenomena include: saccade staircases, duration and amplitude of cell discharges for saccades of variable amplitude, component stretching to achieve straight oblique saccades, saturation of saccade velocity after saturation of saccade amplitude in response to high stimulation frequencies, tradeoffs between saccade velocity and duration to generate constant saccade amplitude, conservation of saccade amplitude in response to sufficiently brief stimulation of omnipause neurons, and high velocity smooth eye movements evoked by high levels of electrical stimulation of the superior colliculus. Previous saccade generator models have not explained this range of data. These models have also invoked mechanisms for which no neurophysiological evidence has been forthcoming, such as resetable integrators, perfect integrators, or target position movement commands. The present model utilizes only known reticular formation neurons. It suggests that a key part of the feedback loop within the saccade generator is realized by inhibitory feedback from short-lead to long-lead burst neurons, in response to excitatory feedforward signals from long-lead to short-lead burst neurons. When this property is combined with opponent interactions between agonist and antagonist muscle-controlling neurons, and motor error, or vector, inputs from the superior colliculus and other saccade-controlling brain regions, all of the above data can be explained. Taken together, these components generate a saccade reset cycle whereby activation of long-lead burst neurons inhibits omnipause neuron

A Neural Model Of Saccadic Eye Movement Control Explains Task-Specific Adaptation

Multiple brain learning sites are needed to calibrate the accuracy of saccadic eye movements. This is true because saccades can be made reactively to visual cues, attentively to visual or auditory cues, or planned in response to memory cues using visual, parietal, and prefrontal cortex, as well as superior colliculus, cerebellum, and reticular formation. The organization of these sites can be probed by displacing a visual target during a saccade. The resulting adaptation typically shows incomplete and asymmetric transfer between different tasks. A neural model of saccadic system learning is developed to explain these data, as well as data about saccadic coordinate changes