Peripheral targets attenuate miniature eye movements during fixation.

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. This, and other previous work showing fewer catch-up saccades during smooth pursuit of peripheral targets suggested that a peripheral target might similarly mitigate microsaccades. Here, human observers fixated one of three stimuli: a small central dot, the center of a peripheral, circular array of small dots, or a central/peripheral stimulus created by combining the two. The microsaccade rate was significantly lower with the peripheral array than with the dot. However, inserting the dot into the array increased the microsaccade rate to single-dot levels. Drift speed also decreased with the peripheral array, both with and without the central dot. Eye position variability was higher with the array than with the composite stimulus. The results suggest that analogous to the foveal pursuit, foveating a stationary target engages the saccadic system likely compromising retinal-image stability. In contrast, fixating a peripheral stimulus improves stability, thereby affording better retinal imaging and releasing attention for experimental tasks.

A common mechanism modulates saccade timing during pursuit and fixation.

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. Observers pursued a linear array of 15 alphanumeric characters that translated across the screen and simultaneously performed a character identification task on it. At a fixed time, a cue briefly surrounded the central element to specify it as the pursuit target. After a random delay, a probe (E or 3) appeared briefly at a randomly selected character location, and observers identified it. For comparison, a fixation condition was also tested with trial parameters identical to the pursuit condition, except that the array remained stationary. We found that during both pursuit and fixation tasks, saccades paused after the cue and then rebounded as expected but also subsided in anticipation of the task. The time courses of the reactive pause, rebound, and anticipatory subsidence were similar, and idiosyncratic subject behavior was consistent across pursuit and fixation. The results provide evidence for a common mechanism of saccade control during pursuit and fixation, which is predictive as well as reactive and has an identifiable temporal signature in individual observers.NEW & NOTEWORTHY During natural scene viewing, voluntary saccades reorient the fovea to different locations for high-acuity viewing. Less is known about small "microsaccades" that also occur when fixating stationary objects and "catch-up saccades" that occur during smooth pursuit of moving objects. We provide evidence that microsaccade and catch-up saccade frequencies are generally modulated by the same mechanism. Furthermore, on a finer time scale the mechanism operates differently in different observers, suggesting that neural saccade generators are individually unique.

Choosing a foveal goal recruits the saccadic system during smooth pursuit.

Models 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. It may be that a central feature on a large object automatically recruits the saccadic system. Alternatively, a cognitive choice is made that the feature is the pursuit goal, and the saccadic system is then recruited to pursue it. Observers pursued a 5-dot stimulus composed of a central dot surrounded by four peripheral dots arranged as a diamond. An attention task specified the pursuit goal as either the central element, or the diamond gestalt. Fewer catch-up saccades occurred with the Gestalt goal than with the central goal, although the additional saccades with the central goal neither enhanced nor impeded pursuit. Furthermore, removing the central element from the diamond goal further reduced catch-up saccade frequency, indicating that the central element automatically triggered some saccades. Higher saccade frequency was not simply due to narrowly focused attention, since attending a small peripheral diamond during pursuit elicited fewer saccades than attending the diamond positioned foveally. The results suggest some saccades are automatically elicited by a small central element, but when it is chosen as the pursuit goal the saccadic system is further recruited to pursue it. NEW & NOTEWORTHY Smooth-pursuit eye movements stabilize retinal image motion to prevent blur. Curiously, smooth pursuit is frequently supplemented by small catchup saccades that could reduce image clarity. Catchup saccades might only be needed to pursue small laboratory stimuli, as they are infrequent during large object pursuit. Yet large objects with central features revive them. Here, we show that voluntarily selecting a feature as the pursuit goal elicits saccades that do not help pursuit.

Category search speeds up face-selective fMRI responses in a non-hierarchical cortical face network.

The 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. The category selectivity of most face-selective areas was enhanced when participants were instructed to report the presence of faces in gradually decreasing noise stimuli. Conversely, the same regions showed much less selectivity when participants were instructed instead to detect cars. When "face" was the target category, the fusiform face area (FFA) showed consistently earlier differentiation of face versus car stimuli than did the "occipital face area" (OFA). When "car" was the target category, only the FFA showed differentiation of face versus car stimuli. These observations provide further challenges for hierarchical models of cortical face processing and show that during gradual revealing of information, selective category-search may decrease the required amount of information, enhancing and speeding up category-selective responses in the human brain.

Divergence between oculomotor and perceptual causality.

When 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. They should be correlated if they tap into the same mental processes. To test this, we recorded oculomotor responses to launching stimuli, then asked subjects to manually classify those stimuli as causal or noncausal. Overall the psychophysical classifications matched the oculomotor biases, although correlations across subjects were mostly absent. In subsequent experiments, 50% of the trials had a 300-millisecond delay after the collision to impede the perception of causality. Subjects maintained their causal oculomotor bias but used different classification strategies, usually grouping the stimuli either by delay or by direction. In addition, there was no evidence that the two response types were correlated on a trial-by-trial basis. The results suggest divergent processes underlying oculomotor responses to and judgments of causal stimuli.

Anticipatory pursuit is influenced by a concurrent timing task.

The 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. The role of attention on the temporal processing component of prediction was investigated in a visual smooth pursuit task that was performed either in isolation or concurrently with a manual response task. Subjects used smooth pursuit eye movements to accurately track a moving target after a constant-duration delay interval. In the manual response task, subjects had to estimate the instant of target motion onset by pressing a button. The onset of anticipatory pursuit eye movements was used to quantify the subject's estimate of elapsed time. We found that onset times were delayed significantly in the presence of the concurrent manual task relative to the pursuit task in isolation. There was also a correlation between the oculomotor and manual response latencies. In the framework of Scalar Timing Theory, the results are consistent with a centralized attentional gating mechanism that allocates clock resources between smooth pursuit preparation and the parallel timing task.

Anticipatory movement timing using prediction and external cues.

Animals 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. Smooth pursuit eye movements are generated by a motor system heavily influenced by anticipation. We measured pursuit to determine how its timing was influenced when both a predictable event and a visual cue were present. Monkeys tracked a moving target that appeared at a constant time relative to the onset of a fixation point. At a randomized time before target onset, the fixation point disappeared, creating a temporal "gap" that cued impending target motion. We found that the gap onset cue and prediction of target onset together determined pursuit initiation time. We also investigated whether prediction could override the gap onset cue or vice versa by manipulating target onset and, hence, the duration of time that the animal had to estimate to predict it. When target motion began earlier, the pursuit system relied more on prediction to trigger a movement, whereas the cue was more often used when the target moved later. Pursuit latency in previous trials partially accounted for this behavior. The results suggest that neither internal nor external factors dominate to control the anticipatory response and that the relative contributions vary with stimulus conditions. A model in which neuronal anticipation and fixation signals interact can explain the results.

Timing and velocity randomization similarly affect anticipatory pursuit.

Smooth 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). A critical question for interpreting neural signals recorded during pursuit concerns how this mechanism is interfaced with sensorimotor processing. It has been shown that the predictor is not simply turned-off during randomization because anticipatory eye velocity remains when target velocity is randomized (E. Kowler & S. McKee, 1987; G. W. Kao & M. J. Morrow, 1994). This study was completed to compare pursuit behavior during randomized motion-onset timing with that occurring during direction or speed randomization. We found that anticipatory eye velocity persisted despite motion-onset randomization, and that anticipation onset time was between that observed in the different constant-timing conditions. This centering strategy was similar to the bias of eye velocity magnitude away from extremes observed when direction or speed was randomized. Such a strategy is comparable to least-squares error minimization, and could be used to facilitate acquisition of a target when it begins to move. Centering was in some observers accounted for by a shift of eye velocity toward that generated in the preceding trial. The results make unlikely a model in which the predictor is disengaged by randomizing stimulus timing, and suggest that predictive signals always interact with those used in sensorimotor processing during smooth pursuit.

Trajectory interpretation by supplementary eye field neurons during ocular baseball.

Good 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. We found that a subset of neurons in the SEF interpreted the trajectory according to the task rule. Other neurons specified at a later time the command to pursue the target with the eyes. The results suggest that the SEF can interpret sensory signals about target motion in the context of a rule to guide voluntary eye movement initiation.

Decoding of a motor command vector from distributed activity in superior colliculus.

Several 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. The present paper compares the operation of these two methods in a model of an experimentally well-studied neural structure, the superior colliculus (SC). The SC is one structure that has been shown to be responsible for generating saccadic command vectors in the form of distributed neural activity that is topologically arranged across its surface. It has been suggested that the pattern of eye-movement errors obtained following the placement of a collicular lesion can distinguish between these two mechanisms. As a result of this suggestion, the pattern of saccadic errors produced by lesions in the SC have been widely cited to support the CM hypothesis. In the present paper the placement of a discrete lesion is simulated in a recurrent (dynamic) neural network model of the SC. These dynamic connections in the model SC network produce a systematic shift of the locus of distributed activity away from the site of the lesion. The spatiotemporal shift in the location of SC activity then produces a pattern of saccadic errors that appear to support the CM hypothesis, even though ensemble activity in our model colliculus is decoded by VS. This result demonstrates that, when ensemble activity on the SC motor map is dynamically modulated over space and time by intrinsic collicular circuitry, an explicit CM computation is not needed to reproduce the pattern of physiological results that follow focal SC lesions.