Ординатура / Офтальмология / Английские материалы / Advances in Understanding Mechanisms and Treatment of Infantile Forms of Nystagmus_Leigh, Devereaux_2008

To convert the independent horizontal and vertical eye movement waveforms to a single radial waveform, we performed a simple vector summation of the form r = √(x2 + y2), while attempting to preserve the sign of the major component wherever possible.

RESULTS

The overall differences between the original and revised tau surfaces are generally quite small, averaging 2 milliseconds overall, and no more than 5.1 milliseconds in the worst case. The difference expressed as a percentage between the original and the revised tau surfaces is shown in Figure 18.1C. The mean across all points was 2.17%, with a maximum error reaching 10% at only a few locations. We tested the new surface by reevaluating 40 NAFX calculations that had been performed over a variety of foveation-window position and velocity limits. To compare the results, we plotted NAFXv1 along the x-axis and NFAXv2 along the y-axis, as shown in Figure 18.1D. A first-order fit gives a slope of 0.975 (1.0 is ideal) with an r2 value of 0.99, showing that the new function is in close agreement with the original. The average absolute error was 1.2%, with a standard deviation of 1.6%, demonstrating that the NAFX equation is relatively insensitive to the τ parameter.

Figure 18.2A shows the creation of a radial waveform from horizontal and vertical components. Here, the horizontal magnitude is approximately five times that of the vertical, and the resulting combined waveform remains recognizable as PPfs with the same magnitude and foveation characteristics as the horizontal component, as shown by the resulting radial NAFX of 0.46, compared to 0.45 for the horizontal and 0.85 for the vertical. The NAFX analyses of 22 (horizontal, vertical, radial) triplets are plotted in Figure 18.2B. Most of the time, as expected, the radial result was nearly the same as, or just slightly worse than, that of the component with the lower NAFX. However, on several occasions the radial analysis unexpectedly yielded a better result than the poorer component, for reasons that are discussed next.

DISCUSSION

The original NAFX has demonstrated its utility as a method to predict best potential visual acuity based solely on waveform and foveation characteristics. Our current work has further refined the technique by

(a) removing minor irregularities in the tau surface that is used to calculate the NAFX and (b) extending the algorithm to allow its application to multiplanar nystagmus analysis.

The latter part (b) is actually a more difficult problem than it would at first appear. We are attempting to map a three-dimensional waveform (h,v versus t) into two dimensions (r versus t) while being mathematically exact, and this is not always possible. There are cases where the mapping will distort the resulting waveform so that, for example, successive foveation periods will appear closer together than they really are, leading to a decreased standard deviation of foveation position, resulting in an artificially high NAFX. The other challenge is that we would like the resulting waveform to remain recognizable as a nystagmus waveform, so that the investigator can use personal expertise to select appropriate segments of data for analysis and guide (or override) the program’s operations as necessary. Unfortunately, these two directives can sometimes be in conflict.

ACKNOWLEDGMENT This research was supported by the Department of Veterans Affairs Merit Review (to Dr. Dell’Osso).

References

1.Dell’Osso LF, Jacobs JB. An expanded nystagmus acuity function: intraand intersubject prediction of best-corrected visual acuity. Doc Ophthalmol. 2002;104:249–276.

2.Sheth NV, Dell’Osso LF, Leigh RJ, Van Doren CL, Peckham HP. The effects of afferent stimulation on congenital nystagmus foveation periods. Vision Res. 1995;35:2371–2382.

3.Jacobs JB, Dell’Osso LF, Leigh RJ. Characteristics of braking saccades in congenital nystagmus. Doc Ophthalmol. 2003;107:137–154.

SCOTT H. SEIDMAN

ABSTRACT

The ability to determine the path of travel, or “path integration,” has been reported to be robust in humans, and has been attributed to an accurate double integration of acceleration signals transduced by the otolith organs to yield path information. Vestibulo-ocular responses to translational motion, however, are most robust at high frequencies of motion, and become somewhat feeble at frequencies typically used in the study of path integration; this observation puts the otolith origin of path-integration phenomena in doubt. To determine if path-integration behaviors might be mediated by cues of motion of a noninertial nature, such as vibration and noise that might accompany translational motion, we measured the perception of motion on two different apparatuses: one that minimized noninertial cues of motion and a second that dissociated noninertial cues from actual motion. When noninertial cues are minimized, we observe a variety of high-pass phenomena in the perception of translation, including a decay in magnitude during constant velocity travel and decreasing magnitude accompanied by increasing phase leads as the frequency of sinusoidal translation decreases. When cues are dissociated from motion, the perception of translation is heavily influenced by the noninertial cues. We conclude that otolith mechanisms are not sufficient to drive path-integration behaviors.

The otolith organs serve as the vestibular system’s detectors of linear accelerations, and are thus ambiguously responsive to accelerations associated with linear translation of the head and to reorientations of the head with respect to gravity (i.e., tilt). The reflexive responses that compensate for vestibulo-ocular or postural stimuli, and the perceptions generated by each type of stimulus, must clearly differ, but the mechanism by which the disambiguation of tilt and translational acceleration occurs is a matter of active investigation.

Many investigators correctly point out that systems employing otolith inputs to generate orientational and reflexive responses rarely have to distinguish tilt from translation in the absence of other information, and thus propose a variety of multisensory approaches to generate appropriate responses.1-5 Information from semicircular canal afferents seems particularly useful for achieving these goals, as tilts are normally accompanied by head rotations that would stimulate the canals. Indeed, there is no a priori reason to believe that sensory and sensorimotor systems do not use all of the information available to generate appropriate responses. However, there is evidence that suggests that the vestibular system makes efforts to distinguish tilt from translation even when there is no stimulusrelated response carried by the semicircular canals.

Specifically, studies of the vestibulo-ocular reflex (VOR) response to translation in isolation show translation-compensatory responses (e.g., horizontal eye movement in response to translation along the interaural axis) that become increasingly robust as stimulus frequency increases. Thus, responses to stimuli below

approximately 1 Hz show decreasing magnitude and increasing phase lead as stimulus frequency decreases, consistent with a high-pass filtered version of translational acceleration used to generate the eye movement response.6-8 Further, studies of tilt-compensatory VOR responses to acceleration (e.g., torsion during interaural acceleration and vertical eye movements during naso-occipital acceleration) show largest magnitude at low frequency, with decreasing magnitude and increasing phase lag with increased stimulus fre- quency.9-11 Such findings build a compelling case that, in the absence of canal signals, high-frequency otolith activity is responded to as if due to translation, and low frequency as if due to tilt.

Unfortunately, not all studies support this simplistic approach. Studies of the perception of travel during linear translation show that human subjects accurately perceive their path of travel, even during low-frequency stimuli with large periods of constant-velocity travel.12-15 This path-integration ability is often attributed to mechanisms employing a double-integration of acceleration information transduced by the otolith organs,16-17 and is thus not consistent with the simple frequencyparsing disambiguation scheme suggested above. This response etiology, however, is based on the assumption that only inertial cues are used to generate perception. In addition to otolith-transduced acceleration, there are other noninertial cues of motion present when subjects are translated, such as the vibration and noise associated with the apparatus used to generate motion, and the roles of such cues in the generation of translation perception have not been investigated.

In a series of two experiments, we study the relationship between these noninertial cues and the perception of translation. In one experiment, we employ a unique apparatus to dissociate noninertial cues from the actual acceleration profile, and we compare the responses to similar trials in which noninertial cues are highly correlated to actual motion.18 In a second experiment, we employ a large sled on air bearings to minimize noninertial cues of motion.

METHODS

Experiment 1

Velocity trapezoids of translational motion (acceleration, 10 to 100 [cm/s]/s; velocity, 10 to 45 cm/s) over a straight 75 cm path were generated using a multiaxis sled/rotator consisting of a linear sled sandwiched between two rotation axes. Motion generated using the linear sled was associated with a large amount of vibration and noise that was well correlated with the speed of the sled, while the rotation axes were largely free from vibration and noise. Motion along the interaural

(IA) axis was generated either using the sled alone (“sled only”) or by a combination of sled motion and counter-rotation of the two rotation axes to describe motion along the chord of a large circle (“R-theta”). During the latter form of motion, sled speed comes to a minimum halfway through the motion profile, and thus the majority of noninertial cues have a “two-humped” appearance.

Subjects (n = 12, ages 21 to 31 years) reported continuous perceptions of velocity magnitude and direction during travel in darkness by using a joystick that had a spring-loaded center detent, used to portray zero velocity. Subjects were initially trained to report velocity using the full set of sled-only stimuli in the light. Responses were compared to both actual velocity of travel and sled speed using “mutual information” techniques to assess similarity between responses and stimuli.

Experiment 2

Sinusoidal motion ([0.1 Hz, 0.06g] to [0.25 Hz, 0.17 g]) along the IA axis was generated using a 9 m sled riding on air bearings located at the NASA Vestibular Research Facility (Moffett Field, CA). Bungee cords were used to drive the sled. By virtue of the air bearings and absence of a motor, motion on this sled was very nearly silent and vibration free. Subjects (n = 12, ages 25 to 48 years) reported perceptions of translation

Subject A

5 seconds

Subject B

Figure 19.1 Mean perceptual response of 2 subjects to InterAural translation (25 [cm/s]/s acceleration to 25 cm/s peak velocity) generated by “sled-only” (left, shown for subject A only), and by R-theta stimulus techniques. Dashed lines show translational velocity, and solid lines the response (+/- one standard deviation). During R-theta motion, subjects’ perceptions include a mid-trial diminution following noninertial cues generated by the sled. Subject B shows a marked misperception of the direction of travel for the second half of the trial.

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using a target-stabilization task. A small laser target was projected via a mirror galvanometer onto a screen approximately 80 cm in front of the subjects at eye level, in an otherwise dark room. The horizontal position of this target was manipulated by the subject with a joystick (without a spring-loaded detent) so as to maintain its position at a perceived stable point in space. Magnitude and phase of the perceptual response was compared to the motion stimulus using random signal techniques.

RESULTS

Experiment 1

Mean responses of 2 subjects to a velocity trapezoid accelerating at 25 (cm/s)/s to a peak velocity of 25 cm/ s are shown in Figure 19.1. For all subjects, velocity percepts were trapezoidal in shape for sled-only stimuli. However, for R-theta stimuli, during which sled speed decreased to a minimum in the middle of the trial, thus producing noninertial cues with a bimodal morphology, percepts often showed a similar bimodal morphology, despite actual motion being quite similar for the two types of trials. This is most clear in subject A. A subset of response morphology, which included 7 of 12 subjects, is apparent in the responses of subject B, for which a directional confusion is apparent during the second half of the R-theta motion profile.

Mutual information analysis confirms this qualitative assessment, with a greater similarity found between sled speed and the response than between the velocity of travel and the perceptual response.

Experiment 2

Figure 19.2 shows that responses to sinusoidal stimuli were sinusoidal in nature. Responses were found to be closest to compensatory (i.e., the target maintained closest to stable in space) at the highest frequency of stimulation, with response magnitudes decreasing and phase leads increasing as stimulus frequency declined. Mean magnitude at 0.25 Hz was approximately 0.6, with a phase lead of less than 10°. Magnitude declined to less than 0.1 at 0.1 Hz, with a phase lead of approximately 40°.

DISCUSSION

In a sequence of two experiments, we investigated the relationship between noninertial cues and the perception of translation, and the perception of translation in the absence of such cues.

The first experiment dissociated noninertial cues from actual translational motion, and results indicate that there is a powerful influence of such cues on the perception of motion. This should not be surprising to anyone with experience driving an automobile with substantially different noise and vibration characteristics from a car to which one is habituated; such a situation will often result in a misestimation of velocity. Indeed, the safest assumption regarding the incorporation of sensory cues for the achievement of any task would be to assume that any and all cues available will be used. An assertion that sensory information in any way correlated with the goals of a task is unimportant must be substantiated.

The second experiment attempts to minimize noninertial cues and demonstrates that in their near absence, perceptual responses to translation are indeed highpass in nature, similar to what is observed in the translational vestibulo-ocular reflex. This experiment thus reconciles an important inconsistency in the vestibular literature. Previously, perceptual systems and the VOR response to translation were thought to process otolith information in two very different ways, with the VOR using high-pass otolith information to generate a response and perceptual mechanisms having access to broadband information. We now show that both perceptual and VOR mechanisms use high-pass information to generate translational responses. While we cannot be certain that the otolith information is processed in precisely the same way by both systems, processing is clearly not as dramatically different as previously held. Comparison of ocular motor and perceptual responses to translation remains an active area of investigation.

ACKNOWLEDGMENTS This work was supported by National Institutes of Health grants DC-04153, DC-01935, DC-005409, EY-01389 (Center for Visual Science), and NASAARC LifeSciDiv (Task 199–97– 62–14).

References

1.Zupan LH, Merfeld DM, Darlot C. Using sensory weighting to model the influence of canal, otolith and visual cues on spatial orientation and eye movements. Biological Cybernetics. 2002;86:209– 230.

2.Merfeld DM, Zupan LH, Gifford CA. Neural processing of gravito-inertial cues in humans. II. Influence of the semicircular canals during eccentric rotation. J Neurophysiol. 2001;85:1648–1660.

3.Merfeld DM, Zupan LH. Neural processing of gravitoinertial cues in humans. III. Modeling tilt and translation responses. J Neurophysiol. 2002;87:819–833.

4.Merfeld DM, Park S, Gianna-Poulin C, Black FO, Wood S. Vestibular perception and action employ qualitatively different mechanisms. I. Frequency response of VOR and perceptual responses during translation and tilt. J Neurophysiol. 2005;94(1):186–198.

5.Angelaki DE, McHenry MQ, Dickman JD, Newlands SD, Hess BJ. Computation of inertial motion: neural strategies to resolve ambiguous otolith information. J Neurosci. 1999;19:316–327.

6.Telford L, Seidman SH, Paige GD. Linear vestibuloocular reflex as a function of frequency and amplitude. Soc Neurosci Abstr. 1994;20:567.

7.Paige GD, Tomko DL. Eye movement responses to linear head motion in the squirrel monkey. I. Basic characteristics. J Neurophysiol. 1991;65: 1170–1182.

8.Paige GD, Telford L, Seidman SH, Barnes GR. Human vestibuloocular reflex and its interactions with vision and fixation distance during linear and angular head movement. J Neurophysiol. 1998;80:2391–2404.

9.Zupan LH, Merfeld DM. Human ocular torsion and perceived roll responses to linear acceleration. J Vestib Res. 2005;15:173–183.

10.Seidman SH, Telford L, Paige GD. Rolland pitch-tilt perception and eye movement during low-frequency linear acceleration. Soc Neurosci Abstr. 1996;22:1094.

11.Wada Y, Goltz H, Seidman SH, Paige GD. Human vestibulo-ocular reflex during low frequency translation. Soc Neurosci Abstr. 1998; 24:416.

12.Berthoz A, Israël I, Vieville T, Zee D. Linear head displacement measured by the otoliths can be reproduced through the saccadic system. Neurosci Lett. 1987;82:285–290.

13.Israël I, Berthoz A. Contribution of the otoliths to the calculation of linear displacement. J Neurophysiol. 1989;62:247–263.

14.Berthoz A, Israël I, Georges-Francois P, Grasso R, Tsuzuku T. Spatial memory of body linear displacement: what is being stored? Science. 1995;269: 95–98.

15.Israël I, Grasso R, Georges-Francois P, Tsuzuku T, Berthoz A. Spatial memory and path integration studied by self-driven passive linear displacement. I. basic properties. J Neurophysiol. 1997;77: 3180–3192.

16.Mittelstaedt H. The role of the otoliths in perception of the vertical and in path integration. Ann N Y Acad Sci. 1999;871:334–344.

17.Mittelstaedt ML, Mittelstaedt H. Idiothetic navigation in humans: estimation of path length. Exp Brain Res. 2001;139:318–332.

18.AuYong N, Paige GD, Seidman SH. Multiple sensory cues underlying the perception of translation and path. J Neurophysiol. 2007;97:1100–1113.

ABSTRACT

Motor systems are required to perform coordinate transformations in order to accurately convert the visual information that will be utilized in the execution of a desired task. These transformations can be affected by the presence of a visual illusion. The Duncker illusion, also known as induced motion, is an illusion of motion that occurs in the presence of background movement. Previous studies have shown that certain aspects of the ocular motor system are influenced by this illusion in the same manner in which the perceptual system is influenced. The present study examines the effect of the Duncker illusion on hand-tracking movements. Eight subjects were required to track with their hand the trajectory of a smoothly moving target without the benefit of visual feedback. Our results show that in a significant number of trials, the hand-tracking trajectory differed significantly from the trajectory observed during the control condition. However, this was not due to the influence of the illusion, because in the overwhelming majority of these trials the hand moved in the direction opposite that of the illusion. This is explainable by positing that ocular- following-type eye movements were generated by the flow-field movement, which in turn influenced the arm tracking. This presents another instance of a motor system with a control system that is impervious to influence by the Duncker visual illusion.

The Duncker illusion, also known as induced motion, is the illusory component of motion perceived when the

background against which a target is seen moves.1 For example, if an object moves leftward and its background moves upward, then the object will be perceived to be moving diagonally, downward and to the left. Note that the illusionary component is in the direction opposite to that of the background motion. A common example of the Duncker illusion in the “real world” is when, on a partially cloudy night, the moon appears to slowly move in the direction opposite that of the slowly drifting clouds. The Duncker illusion is easily created in a laboratory setting and neatly dissociates true from perceived motion, thus providing a tool for probing the possible dissociation between perception and motor responses.

The visual system is required to provide the information used to generate one’s conscious, internal representation of the external world. In addition, it is tasked with the role of generating the information used to program motor commands. According to a current hypothesis, the primate higher visual system is divided into two subsystems that are distinguishable both anatomically and functionally.2 The “what” stream is responsible for object identification and conscious perception. This ventral stream projects from the primary visual cortex (V1) to the inferior temporal lobe. The “how” stream is in charge of visually guided actions. This dorsal stream projects from V1 to the posterior parietal cortex, where it processes the visual information that is aimed at planning and executing action. Dorsal processes are assumed to be fast and not necessarily conscious, and to encode spatial features using egocentric frames of reference. Ventral processes are hypothesized to operate on a slower time scale, to require consciousness, and to use allocentric coding.

It is usually agreed that demonstrating that “action resists visual illusions” supports the “two visual system” theory. According to this theory, the visuomotor mechanism (dorsal) should be able to access accurate spatial information even when that information is not available at levels mediating symbolic decisions (ventral). One way to show the dissociation between perception and motor action is through the study of visual illusions. For instance, in the Müller-Lyer illusion, the same segment appears wider when surrounded by out- ward-pointing arrows and narrower when surrounded by arrows pointing inward. An action such as picking up a bar should involve egocentric coding of spatial features by the dorsal subsystem, and therefore motor measures of bar length should be unaffected by spatial relations with the nearby elements, even if conscious perception (ventral) is affected.

A substantial body of experimental work has attempted to test this prediction, but there remains a great deal of disagreement on the status of the “two visual system” theory. The purpose of the present study was to determine whether there is dissociation between open-loop hand-tracking movements and perception by investigating whether hand tracking is influenced by the Duncker illusion in the same way that perception is.

METHODS

Eight subjects, 4 males and 4 females, participated in the study. Mean age was 33.8 years (SD: 12.8). All were right-handed and used their dominant hand in the experiment.

Three-dimensional motion of the finger was recorded with a Polhemus FASTRAK system. This spatial tracking system (STS) uses an electromagnetic field to determine the three-dimensional position (x, y, and z Cartesian coordinates) and orientation (in azimuth, elevation, and roll Euler angles) of a receiver (marker) relative to a stationary system (transmitter). One STS marker was used at a sampling rate of 120 Hz, with a static accuracy of 0.08 cm root mean square (RMS) for the marker position and 0.150 RMS for the marker orientation.3

Stimuli were presented using a projector and a series of mirrors as shown in Figure 20.1. The visual display was projected using a large (100 × 62 cm) overhead mirror that was facing down toward a semitranslucent screen. It was seen by the subject via a second horizontal mirror (40 × 33 cm), at the approximate height of the subject’s neck and facing up toward the screen. The stimulus thus appeared to the subject to be located below the surface of this second mirror. The second mirror was situated in an opaque black frame that prevented visual feedback of the arm movement.

The STS transmitter was placed on a stand beneath the lower mirror. The marker was attached to the dorsal surface of the subject’s index finger by adhesive tape. The wrist was immobilized by means of a splint.

The “target” was a large (8°) white dot superimposed against a “background” consisting of 600 white, random dots on an otherwise black background. The room was darkened. All trials started with the subject viewing a static target near either the right or left edge of the screen. After 1.5 seconds, both the background and the target started moving synchronously for 3 seconds at 45 deg/s. The background moved either upward or downward or, in control trials, remained static. The target moved either right or left. Each subject saw all six conditions, and each condition was repeated 10 times, for a total of 60 trials per subject. The order of presentation was randomized.

The subjects were instructed to track the target with their right index finger. The tracking was done beneath the mirror, upon which the visual display was projected. Subjects were also instructed to return their hand to its natural position, near the body, at the end of each trial.

Data Analysis

In the coordinate system used, the target moved horizontally parallel to the x-axis, and, based on smoothpursuit eye movement data,4 it was expected that arm movement parallel to the x-axis would accurately track the target. If the illusion affects the tracking, it should result in an unwarranted movement parallel to the y-axis, perpendicular to the subject’s body, which should otherwise show no change during the trial because there was no target movement in that plane. If this y-axis motion were due to the influence of the illusion, it would be in the direction opposite that of background motion. Arm movement in the y-axis in the other direction would not be explainable as an influence of the illusion. No motion was expected parallel to the z-axis (i.e., elevation).

In order to look for the y-axis motion, the middle 1.675 seconds (i.e., 200 of the middle data points out of 360 samples) of each of the 10 trials for each condition were plotted versus time and then linearized. The angle this line formed with the y-axis was then calculated, and provided a measure of the magnitude of the movement in that direction. Finally, a t-test was used to compare the two illusory conditions (background up and background down) of each target direction (right and left) with its respective controls (background stationary).

RESULTS

In the x-axis direction, the subjects all tracked the target accurately both with and without the illusion.

Five of the subjects never showed a difference between the control and experimental conditions in the x-axis direction, and the other 3 showed a statistically significant difference half of the time. Those differences were minute, but they were significant due to the extremely small standard deviation in these data, demonstrating how repeatable and accurate the tracking was. In five out of six cases, the hand velocity was greater in the control condition.

In the y-axis direction, there was a statistically significant difference in 13 of the 32 comparisons. Of these, 10 were in the direction opposite that predicted by an effect of the illusion (i.e., they were in the direction of the background motion), and three were in the direction predicted by the illusion (i.e., opposite to the background motion) (Table 20.1). Two of these three were observed in the same subject. Because there should have been no movement in the y-axis direction, any difference due to random movements or motor

noise should have yielded between one and two differences when using a 95% confidence level. There seems to be a subject-dependent effect. Three of the subjects showed differences in three of the four comparisons, while the other subjects showed fewer differences, although all of the subjects reported having perceived the illusion. It is known that there is large intersubject variability in the Duncker illusion.5

DISCUSSION

Our results indicate that the Duncker illusion does not influence tracking movements of the hand in the predicted direction in an open-loop condition. This dissociation between what was perceived and what the motor system did lends additional support to the “two visual systems” model. However, our results did show that motion of a large background flow field can

influence arm-tracking movements compared to a control condition when the background is stationary.

Previous experiments have shown that the Duncker illusion is a robust illusion perceived to varying degrees by all subjects.5 However, its effect on motor tasks has been shown to vary. Smooth-pursuit eye movements, whether sustained or open loop, track the veridical target and not the illusion. However, in combined eye– head tracking, the head movement is influenced by the illusory trajectory.4,6

Tasks that use memory have been shown to be particularly sensitive to the influence of the Duncker illusion. Predictive saccadic tracking of a regular stepping target displays errors. It is as if the remembered location were that of the illusory position, and saccades to remembered targets where there was an illusion during the memory period display errors consistent with correcting for the illusory motion during that period.4,7

How is the lack of influence of the illusion on the hand-tracking task to be explained? Eye movements were not measured in this experiment, but it is plausible that there was an ocular following-type response in which the eyes are dragged in the direction of the flow field (i.e., the direction opposite that of the illusion), similar to what is seen in open-loop smooth pursuit.7 Furthermore, in an experiment where subjects were presented with a moving target that disappeared behind a field of moving random dots and were asked to point to where the target would reemerge, pointing errors were influenced by gaze position, which in turn was influenced by the illusory trajectory.8 Thus, it is possible that the ocular following-type eye movements postulated to have existed in our experiment in turn influenced the arm movements, producing the observed results.

In summary, our results demonstrate a lack of influence of the Duncker illusion on arm tracking and present another instance in which it appears that the

motor system is not influenced by a visual illusion. On the other hand, the arm tracking does appear to be influenced by the motion of the large background. These results should be treated as preliminary, and further investigation is warranted.

References

1.Duncker K. Uber induzierts Bewegung [Induced motion]. In: Ellis WD, ed. A Source Book on Gestalt Psychology. New York, NY: Humanities Press; 1967.

2.Milner AD, Goodale MA. The Visual Brain in Action. Oxford, UK: Oxford University Press; 1995.

3.Jordan K, Dziedzic K, Jones PW, Ong BN, Dawes PT. The reliability of the three-dimensional FASTRAK measurement system in measuring cervical spine and shoulder range of motion in healthy subjects. Rheumatology. 2000;39:382–388.

4.Zivotofsky AZ, Averbuch-Heller L, Thomas CW, Das VE, Discenna AO, Leigh RJ. Tracking of illusory target motion: differences between gaze and head responses. Vision Res. 1995;35:3029–3035.

5.Zivotofsky AZ. The Duncker Illusion: inter-subject variability, brief exposure, and the role of eye movements in its generation. Invest Ophthalmol Vis Sci. 2004;45:2867–2872.

6.Zivotofsky AZ. A dissociation between perception and action in open-loop smooth-pursuit ocular tracking of the Duncker Illusion. Neurosci Lett. 2005;376:81–86.

7.Zivotofsky AZ, Rottach KG, Averbuch-Heller L, et al. Saccades to remembered targets: the effects of smooth pursuit and illusory stimulus motion. J Neurophysiol. 1996;76:3617–3632.

8.Soechting JF, Engel KC, Flanders M. The Duncker illusion and eye-hand coordination. J Neurophysiol. 2001;85:843–854.