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Journal of Physiology - Paris 98 (2004) 35–52 www.elsevier.com/locate/jphysparis From 1D to 2D via 3D: dynamics of surface motion segmentation for ocular tracking in primates Guillaume S. Masson * Institut de Neurosciences Physiologiques et Cognitives, Centre National de la Recherche Scientifique, 31 Chemin Jospeh Aiguier, 13402 Marseille cedex 20, France Abstract In primates, tracking eye movements help vision by stabilising onto the retinas the images of a moving object of interest. This sensorimotor transformation involves several stages of motion processing, from the local measurement of one-dimensional luminance changes up to the integration of first and higher-order local motion cues into a global two-dimensional motion immune to antagonistic motions arising from the surrounding. The dynamics of this surface motion segmentation is reflected into the various components of the tracking responses and its underlying neural mechanisms can be correlated with behaviour at both single-cell and population levels. I review a series of behavioural studies which demonstrate that the neural representation driving eye movements evolves over time from a fast vector average of the outputs of linear and non-linear spatio-temporal filtering to a progressive and slower accurate solution for global motion. Because of the sensitivity of earliest ocular following to binocular disparity, antagonistic visual motion from surfaces located at different depths are filtered out. Thus, global motion integration is restricted within the depth plane of the object to be tracked. Similar dynamics were found at the level of monkey extra-striate areas MT and MST and I suggest that several parallel pathways along the motion stream are involved albeit with different latencies to build-up this accurate surface motion representation. After 200–300 ms, most of the computational problems of early motion processing (aperture problem, motion integration, motion segmentation) are solved and the eye velocity matches the global object velocity to maintain a clear and steady retinal image. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: 2D visual motion integration; Tracking eye movements; Motion segmentation; Binocular disparity; Non-Fourier motion 1. Introduction Vision is blurred when retinal slip of the image exceeds a few degrees per second. Tracking eye movements help vision by stabilising onto the retinas the images of a moving object of interest (see [84] for a review). To do so, the eyes are rotated smoothly at the same velocity as the selected object. This visual stabilisation mechanisms are found in a wide range of species and it has been so successively investigated that it has been erected as a paradigmatic instance of sensorimotor transformations [56]. In a wide range of species, including human, smooth eye movements have been very carefully scrutinised and, nowadays the fundamentals of oculomotor control are known with high precision and its neural * Tel.: +33-491-164314/164315; fax: +33-491-774969. E-mail address: [email protected] (G.S. Masson). 0928-4257/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jphysparis.2004.03.017 basis are largely unveiled (see [19] for a review). Finally, because similar visual stimulation and ocular recordings techniques can be used in both humans and monkeys and that there are very close behavioural similarities between the two species, it is commonly accepted that monkeys are the best model for understanding the neural basis of oculomotor control. Contrary to the neural motor control of tracking eye movements (see [43,49,56] for reviews), much less is known about the visual mechanisms feeding the sensorimotor transformation. The theoretical consequences of this lack of interest for the afferent information was pointed out by Miles who wrote: ‘‘to date, few studies have been concerned with the optokinetic system’s ability to deal with the more complex optic flow patterns of everyday experience, and most models oversimplify the situation, collapsing the visual processing into a single black box that, in some unspecified way, derives a signal encoding retinal slip’’ [81]. Ten years later 36 G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 however, a growing body of evidence indicates that complex visual motion processing is indeed engaged in controlling tracking eye movements (see [43,51,67,82] for reviews). Efforts have been mostly focused on how target velocity in reconstructed from local motions (e.g. [23,93]) and then represented in cortical visual motion areas [55]. Behavioural studies in man and monkeys can now decipher its dynamics using always more complex motion stimuli inspired from human motion psychophysics. By consequence, eye movements reveal their potential as a ‘‘behavioural probe’’ of the visual brain. These works open the door to a more comprehensive view on the coupling between vision and action, although a new theoretical framework is yet to emerge [106]. Herein, I will review some recent works which demonstrate that reflexive tracking eye movements have a complex temporal dynamics which reflects the progressive build-up of a neural representation of the targetobject motion. I will show that this progressive build-up involves parallel detection mechanisms of the local motion cues and surface motion integration mechanisms. Moreover, behavioural results demonstrate that such integration process is weighted by depth cues such as binocular disparity. This selective integration of 1D local motion cues into a 2D global surface motion is thus intrinsically modulated by contextual, 3D cues which act on the very earliest stages. I will try to relate these various motion processing stages to physiological data collected at the single-cell levels within different monkey visual areas. In particular the emphasis will be put onto the tight link observed between the temporal dynamics at both behavioural and physiological levels. From these experimental evidence, I will then draw a framework which integrates the modern conceptions of parallel, cortical visual motion processing into the frontend of the visuo-oculomotor control in primates. 2. What visual motion? In the everyday life, the image of the visual world is constantly changing as any displacement of our eyes, head or body results in complex optic flow patterns. During self-translation, the retinal velocity of any point in the visual array depends upon its distance from the observer. Therefore, stabilising the image of a given object of interest requires that the visual system singles out its motion from the surrounding and elaborates a precise estimate of its direction and speed. Such processing involves a series of mechanisms which goes from 1D local motion detection to 3D object motion segmentation. These stages have been extensively investigated in motion psychophysics and physiology (see [10,104,114] for reviews). As a consequence, specific motion stimuli have been designed to tackle each of them and they can be adapted for investigating the tracking systems. To correctly study visual motion processing in the context of tracking eye movements, three obstacles need first to be cleared up. First, some specific motion information is needed for initiating and controlling tracking eye movements, which can be different from those used by visual perception. In a complex visual scene, many objects are moving simultaneously and the resulting motion parallax flow field is an important cue for an accurate 3D perception. Motion transparency perception generated by a random dot pattern where each half of the dot population move at different speeds is a good example of this: transparency will only be perceived if the two speeds are neuraly represented at the same time by the motion system (see [10]). Here, motion segmentation requires a precise estimate of each local motion and a selective grouping of the dots moving at the same speed to generate the perception of two transparent surfaces sliding one over each other. Perceptual performance is rather poor and sluggish in such displays when compare to motion detection and discrimination performance [70,80]. How the tracking system performs which such displays? If you ask the subjects to track one of the surface (say, the faster), pursuit initiation has the usual fast (100 ms) latency, albeit with a lower performance than perception [112]. This contradicts in part the fact that perceptual judgements need rather long (200 ms) stimulus duration to reach a decent performance [70]. When both motions are in the same direction, tracking responses are initiated by the average speed of the display. During steady state, the instantaneous eye speed will fluctuate between surface speeds, depending to which surface the subject is paying attention at a given moment. Then, very little influence of the non-attending moving surfaces will be seen, as if the information was discarded [79]. These results indicate that, contrary to the perceptual system, the visuo– motor system needs a simple representation, or the selective read-out of a more complex representation, which provides only one speed signal at a time and for which any other motion is disregarded as noise. Moreover, since initial pursuit is driven by the vector average of the motion display, the pursuit response is obviously initiated before a complete motion segmentation is performed. This example shows that investigating motion processing in the context of either perception or ocular behaviour yield to different results. A lesson is that eye movements are not solely an objective tool to investigate perception. We must take into account the behavioural constraints imposed onto the visual motion processing to understand the visuo–motor transformation and to probe the neural representation of target motion driving the eyes. Secondly, any movement of the eyeball will result in a displacement of the sensing organ, the retina. Tracking G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 acts as a negative feedback loop where eye movements tend to minimise the retinal image motion by smoothly changing the orientation of the fovea. The consequence is that, during closed-loop behaviour, the retinal stimulus is different from the displayed stimulus. It is therefore difficult to isolate the properties of the visual processing involved in the control of the eyes. Obviously, other sources of information such as the eye velocity memory provided by an extra-retinal signal comes into play. One solution to solve this problem is to investigate the earliest phase of pursuit initiation (see [56]). During a short period of time, shorter than the pursuit reaction time, the system is transiently openloop and the ocular responses depend solely upon the displayed stimulus. As a consequence, a precise mapping between stimulus parameters and tracking properties can be measured and, eventually related to the physiological data gained by recording the activities of single neurones using the same parameters space (see [45,82]). Third, tracking responses are continuous rotations of the eyes. By recording the eye velocity profiles and then quantifying responses at certain points in time, we can measure the temporal evolution of visual motion processing. Hence, we have a tool to tape the build-up of the visual symphony where different instruments starts playing at different epochs but converge onto a common beat (e.g. [66,71,84]). Two timing information can then be accurately measured: the latency of each specific tracking component and the time course of their integration. This is clearly an advantage over motion psychophysics where different visual latencies can hardly be measured only by using different stimulus durations (see [121] for instance). From eye movement responses, analogies with neural physiology data can then be drawn both in terms of the basic properties of some given functional pathways [45] and of sequences of early and late waves of activation in the visual system (see [13,53]). In the present review, I will focus on the properties of the visual processing underlying the initiation of shortlatency ocular following responses, first identified by Miles and his group (see [82,83] for reviews). If motion of a large visual scene is applied in the wake of a saccadic eye movement, reflexive tracking responses are elicited at ultra-short latencies (85 ms in humans and 55 ms in monkeys). These machine-like responses exhibit many properties of low-level motion detectors. For instance, when elicited by moving luminance sine-wave gratings, their latencies depend upon both contrast and temporal frequency [36,84]. Amplitude of the responses shows non-monotonic speed tuning, peaking at values 30–40°/s [74]. In both monkeys and man, motion in the periphery of the visual field modulates the ocular following to motion in the central field, indicating that the visual signal driving the eyes results from an integration process [84]. This later result suggests that ocular fol- 37 lowing eye movements depend upon the segmentation of the moving visual scene and are not driven by its en masse motion [82]. In monkeys, the neural basis of ocular following have been carefully scrutinised by the group of Kawano (Fig. 1). Correlated neural activity was found in visual cortical areas MT and MST, leading the eye movements onset by 10 ms and showing both a strong directional selectivity and a preference for high speeds [48]. Neural activity linked to ocular following has also been shown in the dorsolateral pontine nucleus (DLPN) [46] and the ventral paraflocullus lobes of the cerebellum (VPFL) [100]. The analysis of the visual latencies at these different cortical and sub-cortical stages suggests a progression of the information flow from visual processing to motor command. Moreover, although neurons in MST and DLPN show a wide range of directional preferences (Fig. 1), consistent with their role in visual processing, simple-spikes of Purkinje cells in the VPFL are directionally tuned in motor coordinates (i.e. vertical or horizontal) [37]. Kawano and colleagues suggested that visual information concerning the moving visual scene is encoded in MST and relayed via DLPN to the ventral paraflocullus which computes the motor command for driving the eyes [120]. However, additional cerebellar afferences from the pretectal nucleus of the optic tract [44] should also be taken into account to render the exact wiring diagram of this exquisite model of sensorimotor transformation. More detailed reviews on the neural mechanisms of ocular following eye movements can be found elsewhere [45,120]. 3. From luminance to 1D motion cues: detection and triggering How is the visual information about motion of the scene encoded? Any visual motion is primarily a local change in luminance, but changes in other local visual cues (contrast, texture, colour, binocular disparity, etc.) also provide motion information. There is mounting evidence that visual motion computation involves several parallel streams (see [62]). A first-order system extracts motion from drifting luminance modulations and a second-order system measures motion from texture contrast modulations where local luminance remains constant. These two systems are monocular, fast and sensitive to a wide range of spatial and temporal frequencies. They both use motion energy analysis [1,115] or, equivalently, elaborated Reichardt detectors [111]. A third-order system has been described which computes motion from a saliency map. It is much more sluggish, with a more restricted spatio-temporal sensitivity [62]. Although it provides a powerful tool for a precise investigation of the relationship between tracking eye 38 G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 Fig. 1. Information flow along the sensori-motor path for ocular following. Typical neuronal responses in cortical visual area MST, in the dorsolateral pontine nucleus (DLPN) and in the ventral lobe of the paraflocculus (VLPF) of the cerebellum to motion of a large field random dot pattern. Neuron responses are plotted together with the mean eye velocity profiles, on the same time axis. Arrows indicate the mean latency of the cell responses and of the tracking eye movements. Right-end insets show the distribution of directional selectivities, for the three recording sites. All directions of motion are represented in both MST and DLPN while in VFPL, directions along the horizontal and vertical axes are represented in different sub-populations coding for horizontal and vertical eye movements, respectively. Modified from [45]. movements and attention, it is beyond the scope of the present article. A classical signature of linear motion processing, such as motion energy detectors, is called reversed phi motion [21,61]. With appropriate spatio-temporal parameters, a single, step-wise displacement of a high density random dot pattern produces a vivid sensation of forward, apparent motion [116]. If the luminance polarity of the pattern is reversed during the step, apparent motion is perceived in the opposite direction to the actual displacement, a situation called ‘‘reversed phi motion’’ [5]. A similar reversing of optomotor responses had been previously observed by Reichardt with contrast-polarity reversing stimulus presented to the beetle eye. This observation has formed the core of his correlation model [96] and Lu and Sperling later showed that motion reversing is predicted by all motion-energy like models based on a linear spatio-temporal filtering of the input sequence [61]. It is interesting to note that inversion of neural responses with an inverted contrast-polarity has been used to identify linear neural processing of visual information in many different invertebrate (e.g. [28,35,96]) and vertebrate (e.g. [24, 57,89]) nervous systems. G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 In humans, we have recorded the ocular following responses to apparent motion generated by a single position step of a large random dot pattern [73]. Despite the transient nature of the motion signal, robust ocular following responses were initiated at very short latencies (<80 ms). These responses showed a non-monotonic tuning function between amplitude and step size (Fig. 2), that is best fitted with an odd Gabor function which peaks for step sizes of roughly half the period of the random dot pattern fundamental frequency, and asymptote to a default value for step sizes larger than this period. Such spatial parameters are classical parameters of short-range apparent motion processing (e.g. [98]). If the contrast polarity of the random dot pattern was reversed between the first and second frame, ocular following responses were initiated in the direction opposite to the actual displacement (Fig. 2). These reversed responses had the same ultra-short latency (<80 ms) and their tuning curves were symmetrical to those found for forward responses, the best-fitted Gabor functions being 180° out of phase. These similar spatial parameters indicate that the same spatio-temporal filtering is used for both conditions from local changes in luminance. This result demonstrates that the earliest phase of ocular following is driven by a motion detection processing that linearly samples the local changes in luminance before computing its motion. 39 Many neurons in the middle temporal (MT) area, which provides the major inputs to MST, show directional selectivity for motion and exhibit reversed directionality with luminance-reversing motion stimuli [57,58]. Similar reversal have been observed in retinal directional cells in rabbit [7], cat striate cortex [30] and fly lobular plate [28]. In monkeys it has been found that simple––but not complex––cells in area V1 show inverted responses to opposite-contrast [57]. This result suggest that the earliest direction selectivity of MT neurons result from interactions between V1 simple cells [58]. These results suggest that the earliest ocular following is driven by motion signals elaborated by MT/ MST motion detectors that linearly combined fast inputs from V1 simple cells. There is another type of eye movement––disparity vergence––that shares a number of features with ocular following: (1) In both humans and monkeys, it is elicited at ultra-short latency when the appropriate stimuli––this time, disparity steps––are applied to large random dot patterns [16,17]; (2) it shows response reversal with luminance-reversing stimuli, referred to as ‘‘anti-correlated stimuli’’ [69]; (3) in monkeys, it seems to be mediated, at least in part, by visual area MST [108] whose disparity-selective neurons show response reversals with stimuli of opposite contrast polarity at the two eyes [110]. Interestingly, many disparity-selective neurons in monkey striate cortex [24] Fig. 2. Forward and reversed ocular following: step-size tuning. (a) Space-time diagram of a single step apparent motion when the contrast polarity remains constant (top panel) or is reversed (bottom panel) across the displacement. One slice of a random dot pattern is presented. Image was blanked briefly during the step (grey horizontal bar). (b) Mean version velocity profiles (Vs ) of ocular following responses to rightward steps with (red) or without (blue) contrast reversal. Numbers indicate the size of the displacement (in degrees). Vertical dotted lines indicate the estimated mean latency. The light blue area indicates the time-window over which the change in version position was computed, for each trial. (c) Mean (±SE) change in version position, plotted as a function of step size. Positive (negative) values indicate rightward (leftward) apparent motion and ocular tracking. Blue and red circles plot data obtained with constant and reversed contrast polarity, respectively. Continuous lines are best-fitted Gabor functions. The function for reversed contrast polarity condition shows a smaller peak-to-peak amplitude and a 180° phase shift to that found with constant contrast polarity. Both curves do not converge onto the zero level because we subtracted the mean change in version position obtained with a catchtrial (no position step) from each data point to remove any effects due to post-saccadic drift. Different catch-trial were used for constant and reversed contrast polarity conditions. Data from [73,74]. 40 G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 and area MT [52] show similar inversion of their tuning curves when presented with anti-correlated stimuli. Hence, the clear suggestion is that the motion that initiates version and the disparity that initiates vergence are both sensed by first-order energy mechanisms in cortex. However, although forward and reversed phi motion stimuli had similar Fourier Power Spectra, the magnitude of the reversed ocular following were only half of that found for forward responses (Fig. 2b and c, [73]). Interestingly, Livingstone and co-workers have also reported weaker responses to contrast-reversing in directionally selective cells in areas V1 and MT [57,58]. This reduction in the magnitude of the responses is a significant deviation from the linear prediction and could be explained in two different ways. First, an early non-linearity could prevent most of the motion detectors to respond to anti-correlated stimuli. Such non-linearity has been found in the fly lobula plate [35] and, in a somewhat lesser extent, in monkey area V1 [57]. Alternatively, there might be competing motion signals which antagonise the ocular following response. In the reversed phi motion condition, higher-order motion signals remain in the forward direction and therefore compete with the reversed, first-order motion signals [61]. Such competition might explain why reversed ocular following were smaller and suggests that secondorder motion signals can also drive ocular following eye movements. Direct evidence for this are still lacking in humans but, in monkeys it has been found that pure second-order motion stimuli can elicit ocular following responses albeit with a latency delayed by 20 ms relative to grating-driven responses [8]. In the same vein, voluntary pursuit eye movements are elicited by secondorder motion targets although with a lower initial acceleration profile and a longer latency [18,40,54]. In brief, there is now experimental evidence that different motion cues contribute to the initiation of tracking eye movements in both human and non-human primates. Whether or not pure second-order motion can elicit, and at which latency, reflexive, ocular following responses is still a lacking critical piece of evidence. If so, it will be possible to titrate the interaction between pure first- and second-order motion signals and to measure its temporal dynamics. 4. From 1D to 2D: integration of motion signals That different motion cues can be used for measuring the actual motion of a given surface has been already suggested in order to solve the so-called ‘‘aperture problem’’ [113]. Single extended contours of a surface are of considerable importance for the visual system. However, because of spatial and temporal limits of any retinal image sampling mechanism, the motion of these one-dimensional (1D) features is inherently ambiguous: there will be always a family of physical movements in two dimensions that produces the same local visual motion of this isolated contour. All these possible translational velocities lie on a constraint line in the velocity space [31,65]. One solution to solve this motion ambiguity is to extract the different 1D motion signals present across the visual field and then to combine them [2,86]. Plaid motion stimuli provide one good example of such a computation. A moving plaid is constructed by summing two sets of parallel 1D contours, called components, such as lines or sinusoidal gratings of different orientations. Each component moves in the direction orthogonal to its orientation. Under certain circumstances, the components cohere to form a 2D pattern which moves in a direction different from the components motion directions [2,113]. Different computational solutions have been proposed to reconstruct the 2D pattern motion direction from its 1D component motions: intersection of constraints (IOC), vector average or feature tracking. The IOC solution is the unique translation vector consistent with the information of both vectors and is defined geometrically by the intersection of both constraint lines in the velocity space. The vector average solution is the average of the two normal velocities. Finally, the feature tracking solution is defined by the velocity of some features of the plaid intensity pattern such as the so-called ‘‘blobs’’ present at the intersection between gratings. Both the IOC and the feature tracking solutions correspond to the veridical (true) pattern motion direction [2,33,38]. The simplest solution is to compute the vector average of the different 1D motions. For one familly of plaid patterns, called Type I plaids by Ferrera and Wilson [33], the perceived direction correspond to this linear solution. In humans, we have recorded the ocular following responses to both single grating and plaid pattern motions [66]. Plaid patterns were constructed by summing two low spatial frequency gratings whose orientations and motion directions differed by 90°. We found that responses were initiated at the same ultrashort latency (85 ms) by both type of stimuli and were very similar when a single grating or a Type I plaid pattern moved in the same direction (Fig. 3, left panel). Moreover, a trial-by-trial analysis revealed that ocular following responses to a moving plaid can be predicted from the vector average of the responses to its component gratings. Finally, the local signals do not need to overlap to be averaged since similar results were observed when comparing the ocular following to stimulus arrays made of 16 Gabor patches with either one or two different carrier motion directions (Fig. 3, right panel). These results suggest that tracking of a 2D motion can be initiated after computing a spatial average of the different 1D motion signals. Such linear combination of local motion signals is extremely fast since ocular fol- G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 41 Fig. 3. Ocular following responses to overlapping and non-overlapping grating motions. Left panels illustrate the mean horizontal (_eh ) and vertical (_ev ) eye velocities of responses to a single grating (broken lines) or a Type I plaid (continuous lines), both moving in the same direction (45°, oblique axis). The Type I plaid was made of two orthogonal gratings moving in the rightward (0°) and the upward (90°) directions. Right panels show similar results obtained with an array of Gabor patches, all moving in the same direction (broken lines) or each half moving in two orthogonal directions (continuous lines). Vertical dotted lines indicate an estimate of latencies for horizontal and vertical responses. Data from [66]. lowing to either multiple or single 1D motions stimuli have similar ultra-short latencies. There are neurons in monkey area MT that do respond to Type I plaid and therefore seem able to encode the pattern motion direction [97], irrespective of the orientation of the component gratings. These pattern cells are different from the component cells that are activated only when one of the two gratings is moving in its preferred direction [86]. To the extent that a component cell ‘‘sees’’ only the local motion generated by each grating, the directional tuning curve for the plaid stimulus must have separate peaks corresponding to each plaid motion direction that drift one of the grating along its preferred direction. Moreover, the difference between the two peaks must be equal to the angular difference in the orientation of the two component gratings. On a contrary, a pattern cell must have a directional tuning curve presenting only one peak, which is aligned with a the pattern motion direction, irrespective of the two component motion directions. It was then found that the two sub-populations of cells are present at the level of area MT but that only component cells were found at the level of area V1 [86]. A recent study by Pack et al. [92] unveiled a more complex picture in area MT of behaving monkeys: for many cells, responses changed over time. Over the first 20 ms of most cells responses, direction tuning were bimodal and consistent with the ‘‘component prediction’’. However, the subsequent direction tuning, as determined by averaging firing rate over the next 1500 ms of stimulus presentation, was unimodal and consistent with the ‘‘pattern prediction’’. Clearly, the neural responses undergo a complex temporal dynamics where the earliest part reflects predominantly the ambiguous 1D grating motions. Thereafter, direction selectivity gradually converges towards the unambiguous 2D pattern motion. This results suggest that local, 1D motion cues have the fastest access to area MT. Consistent results were found by the group of Movshon which described the dynamics of a large population of MT cells in opiate-anesthetized monkeys. Following plaid pattern presentation, component cells emerged rapidly (<70 ms) but pattern cells 42 G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 exhibit a more progressive build-up of their plaid motion selectivity, the entire sub-population being stabilised 120–140 ms after stimulus onset. Some cells effectively demonstrated a bi-phasic dynamics, being first component-like and then pattern-like [87]. How can we explain the averaging process observed for ocular following responses to Type I plaids? Since these responses had the usual ultra-short latencies, they cannot be explained on the sole basis of the pattern cells sub-population. We found no evidence for a slow build-up of the responses or a delayed onset. Obviously, if both component and pattern cells first encode 1D local motion, a simple read-out such as a vector averaging or summation of the population activity would be sufficient to explain our behavioural results. The existence of such a read-out has been demonstrated by micro-stimulation studies in area MT in the context of smooth pursuit initiation [39]. Moreover, a second, fastest averaging mechanism can take place at the level of MT since several studies have demonstrated that MT cells average multiple inputs (e.g. [12,32,95,105]). Thus, average component velocity can be represented at the level of single neurones and drive the ocular following responses, without the need for pattern selective cells. Further studies are needed to demonstrate whether the averaging process takes place at the level of MT cells or at the level of its population read-out (see [32,95]). Thus, the key question remains open: how can we probe the temporal dynamics of the build-up of the surface motion representation and disentangle it from more simpler, averaging solution. Fortunately, there are instances where the 2D pattern motion direction (i.e. the IOC solution) cannot be predicted by a linear combination of the component motions. These plaids have been called ‘‘Type II plaids’’ by Ferrera and Wilson [33] and they offer an excellent opportunity to test the contribution of the different computational solution for motion integration. Psychophysical studies by the group of Hugh Wilson provided two seminal results. First, the perceived pattern motion direction of Type II plaids evolves over time, from the vector average to the IOC predictions. Second, with stimulus durations longer than 100 ms the perceived direction can be predicted from the vector sum between first- and second-order motion signals [121]. In plaids, first-order motions correspond to the sinusoidal luminance grating motions. Second-order motion can be extracted from them through a filter–rectify–filter scheme [118]. When pattern and grating motions are of different directions, if these motion signals have different dynamics we should be able to tease them apart from the tracking responses. We carefully investigated the open-loop, initial part of the ocular following to uni-kinetic plaids. Uni-kinetic plaids are a limiting case of Type II plaids, where a single moving grating is added to a static grating of different orientations [38]. The orientation of the static grating orientation fully determines the pattern motion direction (Fig. 4a). We found that ocular following was always first initiated in the direction of the moving grating, at the usual short-latency (85 ms). However, a second component was systematically observed 25 ms later which rotated the tracking responses towards the pattern motion direction (Fig. 4b and c). Consequently, the 2D tracking direction evolved over time as illustrated in Fig. 4d. With upward grating motion, tracking was initiated and maintained in the upward direction, as showed by instantaneous mean tracking direction vectors. When a static, oblique grating was added to the same grating motion, the initial vectors pointed in the direction of the grating motion but after 20 ms, the instantaneous vectors progressively shifted towards the oblique direction, that is the pattern motion direction. A trial by trial analysis indicated that final tracking direction reflected this behaviour, with the mean value being shifted 30° away from the grating motion direction (Fig. 4d). Thus, processing of pattern motion cues is delayed by 20 ms relative to grating motion processing and tracking initiation slowly converge towards the pattern motion, indicating that 2D motion integration is a slow and progressive build-up [66]. Static and moving gratings cohere into a uni-kinetic plaid pattern only if they have similar spatial frequencies and contrast [26]. Indeed, we found that the amplitude of the late tracking component was dependent upon the relative spatial frequency of gratings. Moreover, early and late components exhibit different contrast response functions when tested independently. The earliest component was characterised by a very high contrast sensitivity, a steep contrast response function and a saturation with high grating contrast values. On the contrary, the late component showed a more sluggish contrast response function with no or very little saturation [66]. Interestingly, these two contrast response functions are very similar to those reported for the magno-cellular and the parvo-cellular pathways of the geniculo-cortical visual streams [99]. These behavioural results elucidates the dynamics of cortical motion processing. Temporal dynamics of ocular following eye movements reveals parallel processing of grating- and pattern-related motion cues. These processes have different delays but converge onto a single integrative stage. Such computational scheme corresponds to the multiple motion pathways models already suggested from psychophysical studies [21,62, 117,118]. These models propose that first- and secondorder motions are processed through parallel pathways before being integrated to reconstruct the 2D surface motion. Wilson et al. [118] suggested that the nonFourier pathway is slower, which is also supported by oculomotor studies [8,18,54,66]. G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 43 Fig. 4. Early and late ocular following responses to uni-kinetic plaids. (a) A uni-kinetic plaid moving in the rightward–upward (p1) or the leftward– downward (p2) direction is constructed by summing a static oblique grating and an horizontal grating moving either upward (g1) or downward (g2) respectively. (b) Mean horizontal and vertical eye velocity in response to each grating (continuous line) or plaid (broken line) motion direction. One notes that for plaid motion, the vertical responses is initiated at ultra-short latency but the horizontal responses are initiated only 25 ms later. The latency estimates for each component is indicated by the broken vertical lines. (c) For three subjects, mean (±SD) latency of horizontal and vertical responses to plaid motions in the rightward–upward (p1, top panel) and the leftward–downward (p2, lower panel). Notice that the latency of the horizontal component is always longer that the latency of the vertical components. (d) Left panel indicates the frequency distribution of the mean final direction (time window: 95–135 ms) of ocular following to either grating (broken line) or plaid (continuous line) motion. Right panel shows the mean tracking vector, sampled every 4 ms, in response to the same moving grating or uni-kinetic plaid. Data from [66]. 5. Towards surface motion: 2D features integration Many authors have suggested that second-order motion processing is in fact a texture grabber followed by motion energy detectors (e.g. [62,117]). From a computational point of view, the same algorithm can be applied to extract single, localised 2D features and nonlocalised, periodic second-order cues [59]. Therefore, the two pathways scheme proposed by Wilson et al. [118] to compute the 2D pattern motion direction of a plaid appears to be very similar to the feature tracking solution indicated above [117]. It has long been recognised that individual visual features are essential for various visual tasks such as pattern recognition, surface segmentation and so on. Wallach [113] already indicated the critical role of 2D visual features in reconstructing 2D surface motion. The barber-pole illusion offers a simple, but very powerful tool to investigate the basic rule underlying motion integration (e.g. [20,34,94,101]). When seen behind a large aperture, the perceived global direction of a set of bars depends on the shape of the aperture and of the geometrical relationships between the bars and the aperture edges. The laboratory version of it is illustrated in Fig. 5a, where a sinusoidal horizontal grating is presented behind three instances of aperture aspect ratio (the ratio between the two axis of the aperture) and main orientation (the orientation of the long axis). If the grating is set into upward motion, the global perceived direction of the surface within the aperture is either upward (case 2), upward–rightward (case 1) and upward–leftward (case 3). Pairwise comparison between these instances indicate the main phenomena of the so-called ‘‘barber-pole illusion’’ (i.e. the fact that the same grating motion provide different perceived motions). Comparison between cases 1 and 3 hence demonstrates that the perceived direction is biased 44 G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 Fig. 5. Ocular following responses to the barber-pole stimuli. (a) A horizontal grating, moving upward is seen behind three different apertures. A diamond is an aperture with an aspect ratio of 1, and perceived direction is upward (case 2, continuous arrow). With elongated apertures (aspect ratio ¼ 3), tilted counter-clockwise (case 1) or clockwise (case 3), the perceived direction is along the upward–leftward and upward–rightward directions (continuous arrows), respectively, corresponding to the classical ‘‘barber-pole illusion’’. Right panel illustrates the horizontal and vertical mean eye velocity of tracking responses to each condition. With the diamond aperture, the responses is purely vertical (blue line) at the earliest latency. With the elongated apertures, the first component is upward, at the earliest latency (red lines) but a second component is initiated 20 ms later, either rightward (continuous red line) or leftward (broken red line), corresponding to the predictions based on perceived direction. (b) A framework for ocular following of global motion. Local motion signals from grating and line-endings are extracted through parallel pathways which converge onto an integrative stage, albeit with an additional delay (d) for features processing. The integrative stage (presumably visual area MT in primates) average the incoming signals so that the net population vector evolves over time. Data from from [71]. towards the orientation of the long axis of the aperture. Comparing cases 2 and 3 indicates that the perceived direction also depends upon the aperture aspect ratio. The classical explanation for this is that three different local motion signals compete one with each other to dominate the global motion perception. The grating provides ambiguous 1D motion signals from everywhere within the surface. But line-endings or terminators arise at the intersection between the grating and the aperture edges. They form 2D features whose motion is unambiguous. Terminator motions have different directions along the two orthogonal sets of apertures edges. For an aspect ratio of 1, they cancel each other but, for higher aspect ratios, one of the two feature motion directions will dominate and therefore the global perceived direction will be biased towards it. Ocular following to large barber-pole stimuli had the striking behaviour illustrated in Fig. 5a (right panel) [71]. Ocular responses were always initiated first in the direction of the grating motion (i.e. upward for the illustrated examples), at the usual ultra-short-latency (85 ms), irrespective of the geometry of the aperture. A later component was initiated at a latency 110 ms in the direction of the long axis of the aperture. The amplitude, but not the latency of this later component varied with the aperture aspect ratio indicating that it reflected the output of a global motion integration. Indeed, by the end of the open loop period the mean tracking direction were best predicted by a vector sum- mation of the 1D and 2D motions in the display. The specific role of grating and line-endings was demonstrated by an independent modulation of either early or late component. Adding a foveal mask of similar shape inside the aperture and increasing its size up to 90% of the grating area reduced specifically the earliest component but not the later. On the contrary, changing the orientation of line-ending motions by staircasing the luminance profile of the aperture edges [94] specifically decreased the amplitude, not the latency, of the later tracking component [71]. These results further demonstrate that global motion computation is a dynamical mechanism which integrates different local motion cues processed through parallel channels which have different latencies (Fig. 5b). The two motion pathways model adequately renders this dynamical process. Indeed, computational studies have demonstrated that a filter–rectify–filter scheme can also extract 2D features such like line-endings or corners [59]. As pointed out already, this class of model suggests that the various visual motion cues converge onto an integrative stage, albeit with different latencies. Thus, at the level of this integration stage, the coding of the global motion should exhibit a temporal evolution closely related to that found for eye movements. As said above, area MT is thought to implement this integration stage. Recent work by Born and colleagues shows that single neurone activity exhibit a temporal evolution very similar to that reported here for tracking eye movements. G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 They investigated the directional selectivity of MT neurones when presented with a set of parallel bars travelling orthogonal or oblique to their orientation [91]. They found that the earliest part of the response showed a strong interaction between the orientation of the bars and their motion direction: the earliest preferred direction corresponded to the direction orthogonal to the bars. Later direction selectivity was however insensitive to the bars orientation. At the population level, they found that direction selectivity evolves over time from the direction orthogonal to the orientation of a luminance edges to its actual direction, with a time constant of 70 ms. This shift towards an orientation-independent, direction-selectivity, may reflect the temporal evolution of the neural solution of the aperture problem. The mechanism proposed to explain this temporal evolution is still a matter of hot controversies. One hypothesis suggests a specific role for terminators, which can be extracted by slower, non-linear mechanisms such as second-order motion processing [59] or end-stopped cells [60]. Our behavioural results link the dynamics of plaid and barber-pole motion perception and therefore argue in favour of this hypothesis. An alternative view is that the higher spatial frequency components of tiltedline patterns, which are related to the line-endings signals, have a lower contrast and are therefore extracted through the same linear filtering as the grating motion, but with a longer latency [63]. Once again, a critical test to decide between these two solutions is to probe whether or not ocular following to pure second-order motion such as contrast modulated random dot patterns are indeed delayed by 20 ms relative to responses to grating motion. 6. A role for 3D cues: segmentation of surface motion Our studies demonstrate that the initiation of tracking eye movements involves several different local motion processing that extract local 1D and 2D motion signals. These different local motions are integrated with different delays, and with a rather long temporal dynamics which explains why the ocular behaviour evolves over time [66,71]. In the last part of this review, I will show how ocular tracking depends on the counterpart of motion integration: motion segregation. In a crowded environment, the visual motion of the object of interest is surrounded by motions of other surfaces. Hence, for the tracking mechanism to respond selectively to the retinal motion of the object of interest, it must ignore the retinal motion of other objects which are nearer or farther away. In fact, earlier studies on optokinetic responses to wide field motion have reported that this is indeed the case and that binocular disparity of the retinal images of these objects located outside the plane of fixation might play a role [42]. Together with 45 Miles and his colleagues, we further examined this hypothesis by looking at the ocular following to complex patterns where competing motions are presented in a corrugated display [72]. Half of the bands were presented in the plane of fixation when the relative depth of the other half was manipulated by changing the binocular disparity between their left and right eye images (Fig. 6a, bottom panel). As a baseline condition, test bands were presented alone (Fig. 6a, top panel) and their motion drove strong ocular following responses at the usual short-latency (Fig. 6b, ‘‘test bands only’’ velocity profile). On the contrary, when the antagonistic motion (conditioning bands) were presented simultaneously in the same plane of fixation then only minimal responses were observed (Fig. 6b, ‘‘0°’’ velocity profile). We found that increasing the binocular disparity of this conditioning motion stimulus had profound effects on the earliest ocular following: the larger was the binocular disparity the larger were the tracking responses in the direction to the test bands. This relationship was best summarised by the tuning curve relating the amplitude of the earliest ocular following to the binocular disparity of the conditioning motion (Fig. 6c). Maximum interaction (i.e. minimum responses) was observed for no disparity but minimum motion interactions (i.e. larger responses in the test bands motion direction) were found with disparities larger than 2–3°. At these large disparity values, antagonistic motion from the conditioning bands was almost completely eliminated and the amplitude of earliest ocular following closely matched that observed for the control, test bands only condition. Thus, ocular following exhibits a bellshaped sensitivity to binocular disparity of competing motion [72]. Binocular, direction-selective neurones in areas MT and MST have the requisite receptive field sensitivity to implement this ultra-fast image segmentation based on binocular disparity. MT neurones respond to motion with a strong direction-selectivity and antagonistic motion presented simultaneously within the receptive field has a strong inhibitory influence [105]. Most of them are also disparity-sensitive such that they respond best to motion presented at the preferred depth [75]. As a consequence, when motion in the non-preferred direction are presented outside the preferred-disparity range, its antagonistic influence decreases [11]. Similar mechanisms have been demonstrated in area MSTl albeit with a different spatial arrangement of antagonistic motions. Motion presented in the surround of the receptive field have strong modulation effects on the responses evoked by motion in the centre and such modulation also depends on the disparity of the surround stimulus [29]. Interestingly, ocular following responses in monkeys are also modulated by surround, antagonistic motions [84] and this modulation depends on the binocular disparity of the surrounding stimuli [47]. Consistently, 46 G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 Fig. 6. Binocular disparity sensitivity of the early component of ocular following. (a) A pattern is made of alternating stripes with random dots moving either rightward or leftward. In the control condition, the test bands are presented alone, always in the plane of fixation. In the test condition, the test bands (e.g. leftward motion) are presented in the plane of fixation but interlaced with conditioning bands (e.g. rightward motion) whose binocular disparity is manipulated. (b) Mean version velocity profiles of ocular following responses to the conditioning bands only (rightward eye movements, continuous line), the test band only (leftward eye movements, continuous line) or when both bands are presented together (broken lines). Number indicate the binocular disparity of the conditioning bands (in degrees). (c) Mean change in version position, as a function the disparity of the conditioning bands. Responses are minimal when both patterns are in the plane of fixation (disparity ¼ 0°). As the disparity of the antagonistic motion increases, the amplitude of the responses in the leftward direction increases, up to the base-line values observed with the test bands only condition. Modified from [72]. Takemura et al. [109] showed that horizontal disparity steps applied to the image during the centering saccade have similar effects both on earliest ocular following (see also [15]) and mean firing rate of MST neurons. Altogether, these results suggest that the sensitivity of the earliest ocular following to binocular disparity is mediated by disparity-selective motion detectors tuned to the binocular plane of fixation. These neurons weight the different motion signals within their receptive field to implement an automatic motion segmentation process where local motions within the plane of fixation are selectively integrated together and local motions outside the plane of fixation are eliminated. A striking aspect of our results is that such disparitysensitive modulation was observed for the earliest phase of ocular following: antagonistic motion was eliminated immediately when disparity was introduced such that tracking was initiated in the direction of the motion presented in the plane of fixation. Similar results, albeit with a slightly different and less direct paradigm, were observed in monkeys [15]. They suggest that the earliest component of ocular following is sensitive to binocular disparity. As a consequence, 1D local motion measurements within the plane of fixation can be linearly integrated and motion outside the plane of fixation are eliminated. Within the framework outlined above, the fast and coarse representation of surface motion is nevertheless sensitive to binocular 3D cues. On its way to computing 2D surface motion, the visual system takes advantage of some low-level 3D cues to restrict the number of local motion signals being averaged. Therefore, 3D contextual integration occurs at the most elementary stages of motion processing, affecting the most preliminary and coarse neural representation of the pursued object. It is possible that others, contextual 3D cues are involved in surface motion integration and segregation but their dynamics is largely unknown. Psychophysical studies have suggested that the spatial integration of motion signals is gated by depth cues other than disparity. For instance, the perceived direction of a barberpole stimulus closely depends on the 3D interpretation of the visual scene based on occlusion cues [20,101]. When the grating is perceived as moving behind an occluding surface, global motion direction is predominantly perceived along the axis orthogonal to the grating orientation. A possible explanation is that, under these circumstances, line-endings are interpreted as being ‘‘extrinsic’’ to the moving surface and their contribution to the motion integration process is vetoed. On the contrary, when the moving surface is perceived as being in front of the background, line-endings are interpreted as being ‘‘intrinsic’’, their motion is integrated into the 2D integration and the perceived direction is largely consistent with the classical barber-pole illusion [101]. Castet et al. [20] demonstrated that the extrinsic/intrinsic interpretation of 2D features depends upon the relative disparity between the moving and the occluding surfaces but even more on the monocular unpaired regions. Interestingly, similar contextual weighting of motion G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 signals integration has been at the earliest level of cortical processing. Sugita [107] showed that some direction-selective neurones in macaque area V1 encode the motion of partially invisible edges when seen behind an occluding surface covering the centre of the receptive field but presented in front (i.e. crossed disparity) of the moving contours. Albright and co-workers presented another example of contextual depth-ordering. In monkey area MT, many neurones responses represented the perceived direction of a barber-pole, and not the direction orthogonal to its orientation. These cell responses were sensitive to depth-ordering cues present at the aperture edges which located outside the receptive field [27]. What would be the dynamics of such contextual modulation of motion integration? In none of these previous psychophysical or neurophysiological studies the temporal evolution of these contextual effects was analysed (for a review see [4]). Latency and temporal evolution analysis are sparse in the physiological literature despite the fact that, as shown above with plaid motion analysis in area MT, these information are critical to fully understand the processing performed by the visual cortex. Again, our behavioural approach could be very helpful to tackle this question. Our results suggest that motions of 2D features such as line-endings are extracted with a delay relative to grating motion. If so, then contextual effects should have profound effects only on the later tracking component. Moreover, occlusion and binocular disparity cues should have different impact of the responses, the later but not the former being symmetrical relative to the plane of fixation. To know whether such contextual effects can be seen at the earliest part of the late component or would be further delayed is of considerable importance. If contextual influences of 3D cues such as occlusion or 47 monocular unpaired regions are observed at the earliest phase of the late tracking component then most certainly 3D surface motion interpretation is embodied in the feed-forward motion processing implementing fast motion integration. Such a result would be similar to what have been found for the disparity sensitivity of the earliest ocular following. On the contrary, if occlusionbased contextual influences have a significant impact only on later tracking phases (>110 ms), it would then indicate that motion integration involves higher-order rules that cannot be explained in terms of filtering properties of the feed-forward motion processing. 7. Parallel and hierarchical motion processing are revealed by ocular following The behavioural results that I presented herein suggest that several different motion processing play a role in the progressive build up of the neural representation of the 2D surface motion which is used for tracking initiation. Fig. 7 summarizes the different processing stages being involved. We have been able to identify an early tracking component with latencies 80 ms in humans and 55 ms in monkeys, which seems to be driven by local changes in luminance and therefore which are detected by first-order motion detectors. These detectors are binocular and disparity-selective so that first-order motion outside of the plane of fixation are filtered out. It seems plausible that a rapid integration of these local signals operates immediately after local motion sensing since tracking responses to either a single motion or a vector sum/average of several motions are indistinguishable. This linear mechanism implements a fast and coarse motion integration restricted to elements within the plane of fixation. A second tracking component Fig. 7. 1D, 2D and 3D cues for object motion integration. A complex image where a moving object is embedded in a noisy background such as a dense foliage is first processed by local, linear spatio-temporal filters. Motion signals located in the plane of fixation are automatically segregated because of the disparity sensitivity of local motion detectors. These local measurements are then processed within two independent, parallel pathways. A rapid pathway extracts 1D motion signals related to elongated edges and pools them to extract a quick, coarse estimate of the object motion. A slower pathway implements a texture grabber mechanism that can extract local 2D features and compute their motion. These local, nonambiguous motion signals are then feed into the linear solution to compute a correct, non-ambiguous estimate of the 2D object translation. 48 G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 emerges 20 ms later, which seems to be driven by local 2D features and, at least in monkeys, by pure secondorder motion as well. This later component is critical to perfectly align the tracking eye movements with the global motion direction of the pursued surface, independently of its shape. Whether or not this integration depends upon depth-ordering interpretation of local motion signals is currently being investigated. Early and late tracking components can be modulated independently suggesting that their respective inputs are processed in parallel. Finally, tracking direction undergoes a slow temporal evolution so that movements of the eyes slowly converge to the global motion direction, within a period of time of 100 ms after stimulus onset. As said above, these results render many of the key characteristics of a class of feed-forward models proposed for motion integration, which are known as the ‘‘two motion pathways models’’ (see [62,117]). These models postulate the existence of two, parallel processing streams: a Fourier (or linear) and a non-Fourier (or non-linear) pathway. The former can extract changes in luminance of elongated edges while the later acts as a non-linear texture grabber followed by a linear motion processing stage. Fourier and non-Fourier motion pathways are though to correspond to the direct and indirect inputs from areas V1 to MT (see [77] for a review). There are massive direct projections from spiny stellate neurones of area V1––layer 4B to area MT [102]. These V1-4B neurones receive a predominant magnocellular input from layer 4Ca [119] and have a broad spatial selectivity and a high temporal resolution [85]. Since there are MT neurones that are activated by moving random dots at latencies that precede ocular following by 10 ms in monkeys (see Fig. 1, [48]), I suggest that this direct input to area MT is responsible for the earliest initiation of ocular following, presumably via visual area MST. This hypothesis explains several experimental results about the role of linear motion processing. In particular, it is consistent with the high contrast sensitivity of the earliest phase of ocular following, which could be explained by the predominant magno-cellular input to this fast V1-MT route [99]. There is also a non-direct route from V1 to MT, which relays in area V2 [25,76]. This indirect route originates from V1––layer 4B pyramidal neurons that receive mixed M and P signals via inputs from both layer 4Ca and 4Cb and in turn project to the thick stripes of V2 [76,119]. Visual area V2 play a key role along this indirect pathway which is thought to correspond to the non-Fourier stream of the two motion pathways models. This role is supported by several facts. First, lesioning V2 produces strong deficits in texture discrimination but no deficit in orientation discrimination, contrast sensitivity or detection of low level, luminance-based visual cues [78]. Second, there is evidence for rapid, selective neuronal responses to different types of second-order stimuli such as illusory contours in macaque visual area V2 (see [6] for a review). Third, in cats area 18, there are neurones that respond to both first-order (i.e. luminance grating) and second-order (contrast-modulated gratings) motions [122]. However, cell responses to second-order motions are delayed relative to responses to first-order motion [64]. Similar evidence about the dynamics of neuronal responses to second-order motion in area V2 is still lacking in primate but it is known that a small subgroup of neurons in monkey area MT responds to various types of secondorder motion [3,22,90]. One hypothesis is that these MT cells are driven by inputs from visual area V2. Within this framework, our results suggest that early and late component of ocular following responses reflect the successive inputs of parallel, direct and indirect pathways onto area MT (Fig. 5), which in turn project to area MST and then to the brainstem oculomotor system (see Fig. 1). From a more general point of view, I believe that our ‘‘behavioural probe’’ illuminates the modern conceptions about parallel and serial processing in the visual system. Since the pioneering studies of single-unit recording in visual cortex, the functional models of cortical processing have oscillated back and forth between a serial (hierarchical) and a parallel conception (see [13,77]). In the hierarchical model, several processing steps are implemented by a cascade of neurones whose receptive fields are of increasing complexity [53]. Earliest models of feed-forward motion processing resonate with this conception, where a first stage of motion detection is followed by a second-stage of motion integration [86,103]. In parallel models, independent and simultaneous processing are done within modules specialised for different aspects of the visual input. The modern conception that motion processing is done within several pathways specialised for different motion cues offer a functional equivalent of these cortical parallel pathways [62]. As pointed out by Bullier and Nowak [14], timing is a critical feature for deciphering the functional organisation of the cortical visual system. From an extensive review of the literature, Lamme and Roelfsema [53] extracted a schematic temporal figure of the feed-forward sweep of visually driven activity in macaque cortex. Earliest activities (<40 ms) are seen in both V1 and MT, followed by MST and the frontal eye field (<50 ms). As pointed out above, results by Kawano et al. [48] indicate a strict correlation between this early activity in areas MT/MST and the initiation of ocular following in macaque (latencies: 55 ms). Of particular interest, earliest activities in area V2 are delayed by 20 ms relative to V1/MT onset (see [53]). I suggest that this 20 ms delay may explain the timing difference that we found between early and late ocular following components. Moreover, a more detailed analysis of the latency distribution of neurones in the different layers in areas G.S. Masson / Journal of Physiology - Paris 98 (2004) 35–52 V1 and V2 have revealed that the largest difference occurs between the magno-cellular and parvo-cellular streams running throughout them [88]. This observation is consistent with our finding that early and late tracking components have different contrast response functions, which look very similar to those reported from magnoand parvo-driven geniculo-cortical streams, respectively [99]. In summary, our results fit very well the view of the functional organisation of the cortical motion stream in which multiple parallel pathways process different local motion cues but converge onto an integrative stage, presumably MT/MST. Such parallel and hierarchical organisation unveil a more complex temporal structure where the integrative stage receives these parallel inputs with different delays. As a consequence, the neural representation of the object-target motion undergoes a progressive build-up going from a crude representation of 1D edges motion to a fine representation of 2D surface motion within a 3D complex visual scene. 49 behavioural levels. Lastly, we focused on the first 200 ms of tracking initiation. Using third-order motion cues and complex depth-ordering of moving and occluding surfaces we will be able to further investigate the buildup of surface motion and its relationship with attention and mid-level vision mechanisms which, presumably, operate with a longer time scale. Finally, many recent studies indicate that steady-state tracking eye movements share many of the properties of motion segmentation and integration for perception (e.g. [9,50, 106,112]). Altogether, these results call for the need of a more complex front-end motion processing in models of the visual tracking sensorimotor transformation. They also open the door to future research, first to unveil the ocular following-related neural activity within these various visual areas (and in particular visual area V2) and second, to decipher when and how such a neural representation is linked to higher order perceptual and cognitive processing. Thus, a more comprehensive view of how perception and action are coupled will progressively emerge. 8. Conclusion A growing body of evidence suggests that complex motion processing are involved in the visual stabilisation of gaze. How is local velocity encoded by MT cells and then used for smooth pursuit initiation in monkeys has been carefully investigated by the group of Steve Lisberger (e.g. [23,55,93]). How global target velocity is reconstructed for tracking eye movements is under investigation in several labs (e.g. [41,68,106]). Although I focused on reflexive tracking initiation in the present article, there is also strong evidence for similar dynamics of motion integration for voluntary smooth pursuit initiation in primates (e.g. [68,91]). For instance, in humans, we found that voluntary pursuit of line-drawing objects always start in the vector average of edges motion. It takes more than 150 ms before the tracking direction is perfectly aligned with the true object motion. Once again, temporal evolution of smooth pursuit eye movement unveil a dynamics of the neural representation of object motion which is in close agreement with what we have found with reflexive, ocular following eye movements [68]. In brief, it appears now that initiation of smooth eye movements depends on a complex global visual motion build-up with a rather slow temporal dynamics. Throughout this review article, I indicated what are the missing pieces of the puzzle. 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