A population decoding framework for motion aftereffects on smooth pursuit eye movements.

Relationship between eye acceleration and retinal image velocity during foveal smooth pursuit in man and monkey.

Pursuit eye movements correlate with perceived motion in both velocity and direction, even without retinal motion. Cortical cells in the monkey medial temporal region generate signals for initiating pursuit eye movements and respond to retinal motion for perception. However, recent studies suggest multiple motion processes, fast and slow, even for low-level motion. Here we investigated whether the relationship with pursuit eye movements is different for fast and slow motion processes, using a motion aftereffect technique with superimposed low- and high-spatial-frequency gratings. A previous study showed that the low- and high-spatial-frequency gratings adapt the fast and slow motion processes, respectively, and that a static test probes the slow motion process and a flicker test probes the fast motion process (Shioiri & Matsumiya, 2009). In the present study, an adaptation stimulus was composed of two gratings with different spatial frequencies and orientations but the same temporal frequency, moving in the orthogonal direction of ±45° from the vertical. We measured the directions of perceived motion and pursuit eye movements to a test stimulus presented after motion adaptation with changing relative contrasts of the two adapting gratings. Pursuit eye movements were observed in the same direction as that of the motion aftereffects, independent of the relative contrasts of the two adapting gratings, for both the static and flicker tests. These results suggest that pursuit eye movements and perception share motion signals in both slow and fast motion processes.

The directional control of smooth pursuit eye movements was studied by presenting human subjects with targets that moved in a straight line at a constant speed and then changed direction abruptly and unpredictably. To minimize the probability of saccadic responses in the interval following the target's change in direction, target position was offset so as to eliminate position error after the reaction time. Smooth pursuit speed declined at a latency of 90 ms, whereas the direction of smooth pursuit began to change later (130 ms). The amplitude of the offset in target position did not affect the subsequent smooth pursuit response. In other experiments, the target's speed or acceleration was changed abruptly at the time of the change in direction. Step changes in speed elicited short-latency responses in smooth pursuit tracking but step changes in acceleration did not. In all instances, the earliest component of the response did not depend on the parameters of the stimulus. The data were fit with a model in which smooth pursuit resulted from the vector addition of two components, one representing a response to the arrest of the initial target motion and the other the response to the onset of target motion in the new direction. This model gave an excellent fit but further analysis revealed nonlinear interactions between the two vector components. These interactions represented directional anisotropies both in terms of the initial tracking direction (which was either vertical or 45 degrees ) and in terms of the cardinal directions (vertical and horizontal).

1. We recorded single-unit activity from neurons of an output of the cerebellum, the fastigial nucleus, in two rhesus macaques while the monkeys tracked small moving targets with their eyes. Many neurons in the caudal part of the fastigial nucleus exhibited a modulation in their discharge rates when smooth-pursuit eye movements were elicited by either sinusoidal or step-ramp motions of a small target. 2. The pursuit direction that elicited the most vigorous modulation in unit firing to sinusoidal target motion could be horizontal, vertical, or oblique. Most often, the preferred direction was in the contralateral and/or downward direction (50 of 69 neurons) or in the ipsilateral and/or upward direction (13 of 69). 3. For units whose preferred smooth-pursuit directions were either contralateral/downward or ipsilateral/upward during sinusoidal pursuit, peak firing as measured by the phase shift of periodic modulation at 0.5-0.8 Hz occurred near the time of peak velocity. The discharge of 80% of the neurons with contralateral/downward preferred directions preceded eye velocity by an average of -27 degrees; thus these neurons discharged maximally during eye acceleration. In contrast, neurons with ipsilateral/upward preferred directions lagged peak velocity by an average of +10.5 degrees and therefore discharged during eye deceleration. 4. The average eye velocity sensitivity for sinusoidal pursuit between 0.5 and 0.8 Hz was 0.83 +/- 0.57 (SD) spikes/s per degrees/s. We also tested 36 units during pursuit at a variety of frequencies in their preferred directions and found that firing rates increased monotonically with peak eye velocity. However, the firing rate saturated at velocities ranging from 20 to 60 degrees/s for different units. 5. When a monkey tracked a step-ramp target motion, three discharge patterns emerged in the 27 units tested. Just over half of the units discharged a burst of spikes that preceded (average lead of 27.4 +/- 17 ms) and lasted throughout the initial third of the eye acceleration; the burst was followed by a subsequent steady firing that continued after the eye had accelerated to its steady velocity. Fewer neurons discharged a burst that began late in the acceleration and was followed by steady firing. Occasional neurons showed only a gradual increase in firing rate during acceleration followed by a steady discharge. 6. Thirty of the 31 fastigial smooth-pursuit units tested also were modulated during sinusoidal yaw and/or pitch oscillations while the animals fixated a spot that rotated with them.(ABSTRACT TRUNCATED AT 400 WORDS)

The motion aftereffect (MAE) lasts longer when the test period does not immediately follow adaptation, a phenomenon called storage. Does storage of the MAE occur if the test target is present but rendered phenomenally invisible owing to the presence of a rival target presented to the other eye during the storage period? Our experiment addressed this question. Following adaptation to a drifting grating, an intervening period preceded testing with a stationary grating. During this period, the adapted eye either viewed the test target immediately or was occluded, and the unadapted eye either viewed a high-contrast rival target or was occluded. Thus four conditions were employed. The duration of the residual MAE was found to be longer for the rivalry condition (grating and rival target viewed) than for the normal MAE condition (grating viewed), and comparable to that in the stored MAE condition (both eyes occluded). Thus, the MAE is stored when the test target is rendered invisible due to binocular rivalry, indicating that a suppressed target is ineffective at promoting decay of the MAE. So while suppression does not prevent information about the adapting grating from reaching the site of generation of the MAE (Lehmkuhle & Fox, 1975), it can prevent information about the test target from reaching the site of the stored MAE. Current models attribute the MAE to reduced responsiveness of direction-selective cortical neurons (Sutherland, 1961; Barlow & Hill, 1963). Thus, storage should reflect a differential return of these adapted cells to preadapted response levels, dependent on postadaptation stimulation.(ABSTRACT TRUNCATED AT 250 WORDS)

When two overlapping, transparent surfaces move in different directions, there is ambiguity with respect to the depth ordering of the surfaces. Little is known about the surface features that are used to resolve this ambiguity. Here, we investigated the influence of different surface features on the perceived depth order and the direction of smooth pursuit eye movements. Surfaces containing more dots, moving opposite to an adapted direction, moving at a slower speed, or moving in the same direction as the eyes were more likely to be seen in the back. Smooth pursuit eye movements showed an initial preference for surfaces containing more dots, moving in a non-adapted direction, moving at a faster speed, and being composed of larger dots. After 300 to 500 ms, smooth pursuit eye movements adjusted to perception and followed the surface whose direction had to be indicated. The differences between perceived depth order and initial pursuit preferences and the slow adjustment of pursuit indicate that perceived depth order is not determined solely by the eye movements. The common effect of dot number and motion adaptation suggests that global motion strength can induce a bias to perceive the stronger motion in the back.

Auditory and visual attention modulate motion processing in area MT+

1. Purkinje cell activity was recorded from lobules VI and VII of the cerebellar vermis during the performance of visuooculomotor tasks designed to dissociate the signals related to head, smooth-pursuit eye, and retinal image movements. Task-related modulations in the simple spike discharge rates of 157 cells were observed in three alert monkeys. 2. Of 65 Purkinje cells that were completely tested for all three signals, all exhibited smooth-pursuit eye movement-related activity. An additional vestibular or visual response was observed in 17 and 11% of the cells, respectively. Eye, head, and retinal image velocity signals were all recorded in the same unit in 52% of the Purkinje cells. The responses of 5% of the fully tested cells were associated with changes in the direction of eye, head, and retinal image movement. 3. The observed sensorioculomotor responses were direction selective in 98% of the Purkinje cells. For the Purkinje cells that were fully tested, 60% of the cells exhibited peak discharge rates for ipsilateral and 40% for contralateral eye velocity. Of these Purkinje cells, 45% exhibited eye, head, and retinal image velocity signals with equivalent direction preferences. 4. Of 42 Purkinje cells tested, 88% demonstrated some kinds of interactive responses during combined eye and sensory stimulation. The interaction of eye and head velocity signals has been discussed in a companion paper (38). The modulation in discharge rate observed during tracking in the presence of a random dot background pattern could be predicted from the dissociated responses to smooth pursuit in the dark and to movements of the background pattern during suppression of eye movements. 5. The sensitivity to smooth-pursuit eye velocity averaged 1.4 times the sensitivity to head velocity. In 80% of the Purkinje cells, however, the sensitivity to eye velocity exceeded the sensitivity to head velocity by an average of only 10%. The sensitivity to smooth-pursuit eye velocity averaged 1.6 times the sensitivity to retinal image velocity. 6. An increase in Purkinje cell discharge rate was observed during the open-loop period of the initiation of smooth-pursuit eye movements. This open-loop response was consistent with the presence of a visual signal during ocular pursuit, since these cells were also shown to be responsive to a dissociated retinal image velocity signal. Furthermore, the magnitude of the open-loop response indicated an enhancement of the sensitivity to retinal image velocity when visual information became behaviorally significant.(ABSTRACT TRUNCATED AT 400 WORDS)

To examine the spatial shift of attention during smooth pursuit, we measured reaction times (RTs) to a visual target that appeared during pursuit. Participants pursued a moving row of circular frames and responded to a target presented within one of the frames. The results showed large RT differences between stimulus velocities up to 5º/s and 10º/s or above. RTs were faster for a target appearing in the pursuit direction than for one in the opposite direction. When an auditory precue was presented, the RTs during pursuit at 10º/s were faster with increases in the stimulus onset asynchrony (SOA) between the cue and the target. Furthermore, RTs were faster in the cued than in the uncued direction. These results not only support the idea that RTs during pursuit reflect the operation of attention, but also suggest that attention during pursuit can be shifted by the abrupt onset of a target stimulus and/or by prior information regarding the onset of a target stimulus.

The motion after-effect (MAE) persists in crowding conditions, i.e., when the adaptation direction cannot be reliably perceived. The MAE originating from complex moving patterns spreads into non-adapted sectors of a multi-sector adapting display (i.e., phantom MAE). In the present study we used global rotating patterns to measure the strength of the conventional and phantom MAEs in crowded and non-crowded conditions, and when attention was directed to the adapting stimulus and when it was diverted away from the adapting stimulus. The results show that: (i) the phantom MAE is weaker than the conventional MAE, for both non-crowded and crowded conditions, and when attention was focused on the adapting stimulus and when it was diverted from it, (ii) conventional and phantom MAEs in the crowded condition are weaker than in the non-crowded condition. Analysis conducted to assess the effect of crowding on high-level of motion adaptation suggests that crowding is likely to affect the awareness of the adapting stimulus rather than degrading its sensory representation, (iii) for high-level of motion processing the attentional manipulation does not affect the strength of either conventional or phantom MAEs, neither in the non-crowded nor in the crowded conditions. These results suggest that high-level MAEs do not depend on attention and that at high-level of motion adaptation the effects of crowding are not modulated by attention.

To localize a seen object, the CNS has to integrate the object's retinal location with the direction of gaze. Here we investigate this process by examining the localization of static objects during smooth pursuit eye movements. The normally experienced stability of the visual world during smooth pursuit suggests that the CNS essentially compensates for the eye movement when judging target locations. However, certain systematic localization errors are made, and we use these to study the process of sensorimotor integration. During an eye movement, a static object's image moves across the retina. Objects that produce retinal slip are known to be mislocalized: objects moving toward the fovea are seen too far on in their trajectory, whereas errors are much smaller for objects moving away from the fovea. These effects are usually studied by localizing the moving object relative to a briefly flashed one during fixation: moving objects are then mislocalized, but flashes are not. In our first experiment, we found that a similar differential mislocalization occurs for static objects relative to flashes during pursuit. This effect is not specific for horizontal pursuit but was also found in other directions. In a second experiment, we examined how this effect generalizes to positions outside the line of eye movement. We found that large localization errors were found in the entire hemifield ahead of the pursuit target and were predominantly aligned with the direction of eye movement. In a third experiment, we determined whether it is the flash or the static object that is mislocalized ahead of the pursuit target. In contrast to fixation conditions, we found that during pursuit it is the flash, not the static object, which is mislocalized. In a fourth experiment, we used egocentric localization to confirm this result. Our results suggest that the CNS compensates for the retinal localization errors to maintain position constancy for static objects during pursuit. This compensation is achieved in the process of sensorimotor integration of retinal and gaze signals: different retinal areas are integrated with different gaze signals to guarantee the stability of the visual world.

The function of smooth pursuit is to keep the fovea pointed at a small visual target that moves smoothly across a patterned background. Chemical lesions, single cell recordings, and behavioral measures have shown that the cortical motion processing pathways form the afferent limb for pursuit. Important areas include at least the striate cortex and the middle temporal visual area, and probably the medial superior temporal visual area and the posterior parietal cortex. We argue that the visual inputs are transmitted through a simple sensory motor interface in the pons, to the efferent limb in the brain stem and cerebellum. The efferent limb uses neural velocity memory to maintain pursuit automatically. We present evidence that the velocity memory is provided, at least in part, by eye velocity positive feedback between the flocculus of the cerebellum and the brain stem. Finally, we use a computer model to show how the maintenance of pursuit can be simulated on a millisecond time scale. The structure and internal elements of the model are based on the biological experiments reviewed in our paper. In the past five years, progress on the neural basis of pursuit eye movements has been rapid. Several areas of research have made substantial contributions, by using combinations of new and conventional methods. Many of the pathways that contribute to pursuit have been identified, and their physiological activity and functions are becoming understood. Continuing progress promises to yield an understanding of one specific form of visually guided movement, at the level of neuronal circuits and behavior, in the primate.

The visual system is intelligent—it is capable of recovering a coherent surface from an incomplete one, a feat known as perceptual completion or filling in. Traditionally, it has been assumed that surface features are interpolated in a way that resembles the fragmented parts. Using displays featuring four circular apertures, we showed in the study reported here that a distinct completed feature (horizontal motion) arises from local ones (oblique motions)—we term this process emergent filling in. Adaptation to emergent filling-in motion generated a dynamic motion aftereffect that was not due to spreading of local motion from the isolated apertures. The filling-in motion aftereffect occurred in both modal and amodal completions, and it was modulated by selective attention. These findings highlight the importance of high-level interpolation processes in filling in and are consistent with the idea that during emergent filling in, the more cognitive-symbolic processes in later areas (e.g., the middle temporal visual area and the lateral occipital complex) provide important feedback signals to guide more isomorphic processes in earlier areas (V1 and V2).

1. We investigated cells in the middle temporal visual area (MT) and the medial superior temporal area (MST) that discharged during smooth pursuit of a dim target in an otherwise dark room. For each of these pursuit cells we determined whether the response during pursuit originated from visual stimulation of the retina by the pursuit target or from an extraretinal input related to the pursuit movement itself. We distinguished between these alternatives by removing the visual motion stimulus during pursuit either by blinking off the visual target briefly or by stabilizing the target on the retina. 2. In the foveal representation of MT (MTf), we found that pursuit cells usually decreased their rate of discharge during a blink or during stabilization of the visual target. The pursuit response of these cells depends on visual stimulation of the retina by the pursuit target. 3. In a dorsal-medial region of MST (MSTd), cells continued to respond during pursuit despite a blink or stabilization of the pursuit target. The pursuit response of these cells is dependent on an extraretinal input. 4. In a lateral-anterior region of MST (MST1), we found both types of pursuit cells; some, like those in MTf, were dependent on visual inputs whereas others, like those in MSTd, received an extraretinal input. 5. We observed a relationship between pursuit responses and passive visual responses. MST cells whose pursuit responses were attributable to extraretinal inputs tended to respond preferentially to large-field random-dot patterns. Some cells that preferred small spots also had an extraretinal input. 6. For 92% of the pursuit cells we studied, the pursuit response began after onset of the pursuit eye movement. A visual response preceding onset of the eye movement could be observed in many of these cells if the initial motion of the target occurred within the visual receptive field of the cell and in its preferred direction. In contrast to the pursuit response, however, this visual response was not dependent on execution of the pursuit movement. 7. For the remaining 8% of the pursuit cells, the pursuit discharge began before initiation of the pursuit eye movement. This occurred even though the initial motion of the target was outside the receptive field as mapped during fixation trials. Our data suggest, however, that such responses may be attributable to an expansion of the receptive field that accompanies enhanced visual responses.(ABSTRACT TRUNCATED AT 400 WORDS)

Prior experiences can influence future actions. These experiences can not only drive adaptive changes in motor output, but they can also modulate the rate at which these adaptive changes occur. Here we studied anterograde interference in motor adaptation – the ability of a previously learned motor task (Task A) to reduce the rate of subsequently learning a different (and usually opposite) motor task (Task B). We examined the formation of the motor system's capacity for anterograde interference in the adaptive control of human reaching-arm movements by determining the amount of interference after varying durations of exposure to Task A (13, 41, 112, 230, and 369 trials). We found that the amount of anterograde interference observed in the learning of Task B increased with the duration of Task A. However, this increase did not continue indefinitely; instead, the interference reached asymptote after 15–40 trials of Task A. Interestingly, we found that a recently proposed multi-rate model of motor adaptation, composed of two distinct but interacting adaptive processes, predicts several key features of the interference patterns we observed. Specifically, this computational model (without any free parameters) predicts the initial growth and leveling off of anterograde interference that we describe, as well as the asymptotic amount of interference that we observe experimentally (R2 = 0.91). Understanding the mechanisms underlying anterograde interference in motor adaptation may enable the development of improved training and rehabilitation paradigms that mitigate unwanted interference.