| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
EDITORIAL FOCUS
, in this issue of Journal of Neurophysiology (p. 1405–1414) the authors investigate pursuit of accelerating targets that disappear behind a virtual occluder. Here, they present novel data showing that the pursuit system can estimate where an accelerating target will appear and how fast it will be moving after the occlusion. In the paper, the authors entertain three hypotheses. The first is a "final velocity" hypothesis, in which observers use the velocity of the target immediately before it disappears to estimate its velocity when it reappears. This hypothesis is based on the prediction of pursuit models that an efference copy signal generated by recent pursuit of a target can maintain eye velocity for a short duration. The second is an "average velocity" hypothesis in which average target velocity over the preocclusion viewing period is used to estimate the speed of the target on reappearance. The third "acceleration" hypothesis states that the acceleration of the target is computed by the pursuit system and that this computation guides the eyes after the occlusion.
To test between these hypotheses, observers were presented with a target that moved at different initial speeds, but maintained a constant acceleration throughout each trial. The target was visible for either a brief interval (200 ms), or a longer one (500 or 800 ms), before disappearing from view behind a virtual occluder for 500 ms. The task was designed so that the different target velocities were independent of target acceleration at the time of target disappearance, and the mean target velocity during any interval of the initial visible ramp (except the final 50 ms) had an inverse relationship with target acceleration. Design of the experiment is clever, in that it disambiguates target acceleration, average target velocity, and final target velocity before target disappearance, allowing a clean test of the three hypotheses. When preocclusion target viewing was limited to 200 ms, subjects were unsuccessful at matching the appropriate acceleration after the occlusion. However, with the longer 500- or 800-ms viewing times, their pursuit eye movements discriminated between the different accelerations when the target reappeared. Not only did smooth velocity during pursuit contribute to estimating target acceleration, saccades were also used to correct for virtual position error that developed. Although the results support the acceleration hypothesis, even the combined response did not completely eliminate retinal image slip and position error at the time of target reappearance.
That the oculomotor system appeared to derive a coarse estimate of target acceleration in this study is important because the notion that acceleration can guide smooth pursuit is controversial. Reflecting this, one prominent model of pursuit incorporates acceleration analyzers (Krauzlis and Lisberger 1989), whereas another does not (Robinson et al. 1986). The motion-perception system is poor at determining the acceleration of moving objects, in that it can discriminate only between accelerations that differ by about 100% (Watamaniuk 2003). Because there is evidence that pursuit receives input from the same motion-processing substrate as the perceptual system (Beutter and Stone 1998; Madelain and Krauzlis 2003; Stone and Krauzlis 2003; Watamaniuk and Heinen 1999), the pursuit system potentially suffers a similar liability. In support of this, neurons in the middle temporal cortex (MT), a region specifically involved in motion processing (Dubner and Zeki 1971; Rodman and Albright 1987; Tootell et al. 1995) and motion perception (Britten et al. 1996; Liu and Newsome 2005; Newsome et al. 1989), do not show acceleration sensitivity (Lisberger and Movshon 1999; Price et al. 2005). However, it is possible to construct an acceleration signal from the population (Lisberger and Movshon 1999; Price et al. 2005). Previous work on acceleration sensitivity of the pursuit system demonstrated that it can detect whether an object is accelerating (Krauzlis and Lisberger 1994). However, the ability of this system to discriminate between different nonoccluded accelerations is poor (Watamaniuk and Heinen 2003). Results of the current study are consistent with this and show that, despite an occlusion, a rough estimate of acceleration can be accessed by the pursuit system.
Another important contribution of this study is that it describes the response of the saccadic system to accelerating target motion. Saccadic intrusions during smooth pursuit are common and are not simply driven by position error, but rather by a host of factors such as target and pursuit velocity (de Brouwer et al. 2002a,b; Keller and Johnsen 1990). Therefore coordination between the pursuit and saccadic systems during pursuit of constant velocity motion appears to be intricately choreographed. In support of this, there is evidence that these systems are privy to the same motion signals and are subserved by similar neuronal substrates during pursuit of constant velocity motion (for reviews see Krauzlis 2004; Krauzlis and Stone 1999). Therefore it is a natural extension of previous work to investigate the sensitivity of both systems to accelerating targets. Whereas some work has shown that pursuit and saccades can estimate target velocity after occlusion (Orban de Xivry et al. 2006), this paper demonstrates for the first time that the smooth and saccadic systems coordinate to estimate the acceleration of targets that are occluded.
Given that the pursuit system does not accurately extract acceleration from the motion of a target, how might it derive the coarse acceleration estimate it does in the current study? The authors suggest a reasonable explanation—that the system samples velocity in a sequential fashion by comparing a previous velocity sample with the current one. This idea has been shown to explain how anticipatory pursuit eye movements are generated (Barnes and Asselman 1991; Barnes and Schmid 2002; Collins and Barnes 2005). Such a mechanism is also capable of approximating the acceleration and deceleration of sinusoidal target motion (Barnes 1994; Barnes et al. 2000). Comparing successive velocity samples could be the method we use when orienting the eyes or other effectors to objects that accelerate in natural scenes.
The Smith-Kettlewell Eye Research Institute, San Francisco, California
Address for reprint requests and other correspondence: S. J. Heinen, The Smith-Kettlewell Eye Research Institute, 2318 Fillmore St., San Francisco, CA 94115 (E-mail: heinen{at}ski.org)
REFERENCES
Barnes GR. A model of predictive processes in oculomotor control based on experimental results in humans. In: Information Processing Underlying Gaze Control (Pergamon Studies in Neuroscience, vol. 12), edited by Delgado-Garcia JM, Godaux E, Vidal P-P. Oxford, UK: Pergamon Press, 1994, p. 279–290.
Barnes GR, Asselman PT. The mechanism of prediction in human smooth pursuit eye movements. J Physiol 439: 439–461, 1991.
Barnes GR, Barnes DM, Chakraborti SR. Ocular pursuit responses to repeated, single cycle sinusoids reveal behavior compatible with predictive pursuit. J Neurophysiol 84: 2340–2355, 2000.
Barnes GR, Schmid AM. Sequence learning in human ocular smooth pursuit. Exp Brain Res 144: 322–335, 2002.[CrossRef][Web of Science][Medline]
Bennett SJ, Orban de Xivry J-J, Barnes GR, Lefèvre P. Target acceleration can be extracted and represented within the predictive drive to ocular pursuit. J Neurophysiol 98: 1405–1414, 2007.
Beutter BR, Stone LS. Human motion perception and smooth eye movements show similar directional biases for elongated apertures. Vision Res 38: 1273–1286, 1998.[CrossRef][Web of Science][Medline]
Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis Neurosci 13: 87–100, 1996.[Web of Science][Medline]
Collins CJS, Barnes GR. Scaling of anticipatory smooth eye velocity in response to sequences of discrete target movements in humans. Exp Brain Res 20: 1–10, 2005.[Medline]
de Brouwer S, Missal M, Barnes G, Lefèvre P. Quantitative analysis of catch-up saccades during sustained pursuit. J Neurophysiol 87: 1772–1780, 2002b.
de Brouwer S, Yuksel D, Blohm G, Missal M, Lefèvre P. What triggers catch-up saccades during visual tracking? J Neurophysiol 87: 1646–1650, 2002a.
Dubner R, Zeki SM. Response properties and receptive fields of cells in an anatomically defined region of the superior temporal sulcus in the monkey. Brain Res 35: 528–532, 1971.[CrossRef][Web of Science][Medline]
Keller E, Johnsen SDS. Velocity prediction in corrective saccades during smooth-pursuit eye movements in monkey. Exp Brain Res 80: 525–531, 1990.[Web of Science][Medline]
Krauzlis RJ. Recasting the smooth pursuit eye movement system. J Neurophysiol 91: 591–603, 2004.
Krauzlis RJ, Lisberger SG. A control systems model of smooth pursuit eye movements with realistic emergent properties. Neural Comput 1: 116–122, 1989.[CrossRef]
Krauzlis RJ, Lisberger SG. Temporal properties of visual motion signals for the initiation of smooth pursuit eye movements in monkeys. J Neurophysiol 72: 150–162, 1994.
Krauzlis RJ, Stone LS. Tracking with the mind's eye. Trends Neurosci 22: 544–550, 1999.[CrossRef][Web of Science][Medline]
Lisberger SG, Movshon JA. Visual motion analysis for pursuit eye movements in area MT of macaque monkeys. J Neurosci 19: 2222–2246, 1999.
Liu J, Newsome WT. Correlation between speed perception and neural activity in the middle temporal visual area. J Neurosci 25: 711–722, 2005.
Madelain L, Krauzlis RJ. Pursuit of the ineffable: perceptual and motor reversals during the tracking of apparent motion. J Vision 3: 642–653, 2003.
Newsome WT, Britten KH, Movshon JA. Neuronal correlates of a perceptual decision. Nature 341: 52–54, 1989.[CrossRef][Medline]
Orban de Xivry J-J, Bennett SJ, Lefèvre P, Barnes GR. Evidence for synergy between saccades and smooth pursuit during transient target disappearance. J Neurophysiol 95: 418–427, 2006.
Price NS, Ono S, Mustari MJ, Ibbotson MR. Comparing acceleration and speed tuning in macaque MT: physiology and modeling. J Neurophysiol 94: 3451–3464, 2005.
Robinson DA, Gordon JL, Gordon SE. A model of the smooth pursuit eye movement system. Biol Cybern 55: 43–57, 1986.[CrossRef][Web of Science][Medline]
Rodman HR, Albright TD. Coding of visual stimulus velocity in area MT of the macaque. Vision Res 27: 2035–2048, 1987.[CrossRef][Web of Science][Medline]
Stone LS, Krauzlis RJ. Shared motion signals for human perceptual decisions and oculomotor actions. J Vision 3: 725–736, 2003.
Tootell RB, Reppas JB, Kwong KK, Malach R, Born RT, Brady TJ, Rosen BR, Belliveau JW. Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. J Neurosci 15: 3215–3230, 1995.[Abstract]
Watamaniuk SNJ. Motion perception. In: The Encyclopedia of Optical Engineering, edited by Johnson RB, Driggers RG. New York: Marcel Dekker, 2003, p. 1380–1389.
Watamaniuk SNJ, Heinen SJ. Human smooth pursuit direction discrimination. Vision Res 39: 59–70, 1999.[CrossRef][Web of Science][Medline]
Watamaniuk SNJ, Heinen SJ. Perceptual and oculomotor evidence of limitations on processing accelerating motion. J Vision 3: 698–709, 2003.
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
