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A comparison of visuomotor cue integration strategies for object placement and prehension

Published online by Cambridge University Press:  01 January 2009

HAL S. GREENWALD  and
DAVID C. KNILL
HAL S. GREENWALD*
Affiliation:
Center for Visual Science, University of Rochester, Rochester, New York
DAVID C. KNILL
Affiliation:
Center for Visual Science, University of Rochester, Rochester, New York
*
*Address correspondence and reprint requests to: Hal Greenwald, Center for Visual Science, University of Rochester, 274 Meliora Hall, Box 270270, Rochester, NY 14627-0270. E-mail: hgreenwald@bcs.rochester.edu
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Abstract

Visual cue integration strategies are known to depend on cue reliability and how rapidly the visual system processes incoming information. We investigated whether these strategies also depend on differences in the information demands for different natural tasks. Using two common goal-oriented tasks, prehension and object placement, we determined whether monocular and binocular information influence estimates of three-dimensional (3D) orientation differently depending on task demands. Both tasks rely on accurate 3D orientation estimates, but 3D position is potentially more important for grasping. Subjects placed an object on or picked up a disc in a virtual environment. On some trials, the monocular cues (aspect ratio and texture compression) and binocular cues (e.g., binocular disparity) suggested slightly different 3D orientations for the disc; these conflicts either were present upon initial stimulus presentation or were introduced after movement initiation, which allowed us to quantify how information from the cues accumulated over time. We analyzed the time-varying orientations of subjects’ fingers in the grasping task and those of the object in the object placement task to quantify how different visual cues influenced motor control. In the first experiment, different subjects performed each task, and those performing the grasping task relied on binocular information more when orienting their hands than those performing the object placement task. When subjects in the second experiment performed both tasks in interleaved sessions, binocular cues were still more influential during grasping than object placement, and the different cue integration strategies observed for each task in isolation were maintained. In both experiments, the temporal analyses showed that subjects processed binocular information faster than monocular information, but task demands did not affect the time course of cue processing. How one uses visual cues for motor control depends on the task being performed, although how quickly the information is processed appears to be task invariant.

Keywords

Cue integrationReaching3D orientationTask dependenceStereopsis
Type
Natural Tasks
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 63 - 72
Copyright
Copyright © Cambridge University Press 2009

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References

Alais, D. & Burr, D. (2004). The ventriloquist effect results from near-optimal bimodal integration. Current Biology 14, 257–262.CrossRefGoogle ScholarPubMed
Blavier, A., Gaudissart, Q., Cadière, G.-B. & Nyssen, A.-S. (2006). Impact of 2D and 3D vision on performance of novice subjects using da Vinci robotic system. Acta Chirurgica Belgica 106, 662–664.CrossRefGoogle Scholar
Bradshaw, M.F., Elliott, K.M., Watt, S.J., Hibbard, P.B., Davies, I.R.L. & Simpson, P.J. (2004). Binocular cues and the control of prehension. Spatial Vision 17, 95–110.Google ScholarPubMed
Deneve, S., Latham, P.E. & Pouget, A. (2001). Efficient computation and cue integration with noisy population codes. Nature Neuroscience 4, 826–831.CrossRefGoogle ScholarPubMed
Ernst, M.O. & Banks, M.S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433.CrossRefGoogle Scholar
Ernst, M.O., Banks, M.S. & Bülthoff, H.H. (2000). Touch can change visual slant perception. Nature Neuroscience 3, 69–73.CrossRefGoogle ScholarPubMed
Falk, V., Mintz, D., Grünenfelder, J., Fann, J.I. & Burdon, T.A. (2001). Influence of three-dimensional vision on surgical telemanipulator performance. Surgical Endoscopy 15, 1282–1288.CrossRefGoogle ScholarPubMed
Ghahramani, Z., Wolpert, D.M. & Jordan, M.I. (1997). Computational models of sensorimotor integration. In Self-Organization, Computational Maps and Motor Control, ed. Morasso, P.G. & Sanguineti, V., pp. 117–147. Amsterdam, Elsevier Press.CrossRefGoogle Scholar
Glover, S. (2004). Separate visual representations in the planning and control of action. Behavioral and Brain Sciences 27, 3–78.Google ScholarPubMed
Goodale, M.A., Jakobson, L.S. & Keillor, J.M. (1994). Differences in the visual control of pantomimed and natural grasping movements. Neuropsychologia 32, 1159–1178.CrossRefGoogle ScholarPubMed
Greenwald, H.S., Knill, D.C. & Saunders, J.A. (2005). Integrating visual cues for motor control: A matter of time. Vision Research 45, 1975–1989.CrossRefGoogle ScholarPubMed
Hillis, J.M., Watt, S.J., Landy, M.S. & Banks, M.S. (2004). Slant from texture and disparity cues: Optimal cue combination. Journal of Vision 4, 967–992.CrossRefGoogle ScholarPubMed
Huang, X., Blau, S. & Paradiso, M.A. (2005). Background changes delay the perceptual availability of form information. Journal of Neurophysiology 94, 4331–4343.CrossRefGoogle ScholarPubMed
Huang, X. & Paradiso, M.A. (2005). Background changes delay information represented in macaque V1 neurons. Journal of Neurophysiology 94, 4314–4330.CrossRefGoogle ScholarPubMed
Jackson, S.R., Jones, C.A., Newport, R. & Pritchard, C. (1997). A kinematic analysis of goal-directed prehension movements executed under binocular, monocular, and memory-guided viewing conditions. Visual Cognition 42, 113–142.CrossRefGoogle Scholar
Jacobs, R.A. (1999). Optimal integration of texture and motion cues to depth. Vision Research 39, 3621–3629.CrossRefGoogle ScholarPubMed
Knill, D.C. (2005). Reaching for visual cues to depth: The brain combines cues differently for motor control and perception. Journal of Vision 5, 103–115.CrossRefGoogle ScholarPubMed
Knill, D.C. & Saunders, J.A. (2003). Do humans optimally integrate stereo and texture information for judgments of surface slant? Vision Research 43, 2539–2558.CrossRefGoogle ScholarPubMed
Landy, M.S., Maloney, L.T., Johnston, E.B. & Young, M. (1995). Measurement and modeling of depth cue combination: In defense of weak fusion. Vision Research 35, 389–412.CrossRefGoogle ScholarPubMed
Loftus, G.R. & Masson, M.E. (1994). Using confidence intervals in within-subject designs. Psychonomic Bulletin & Review 1, 476–490.CrossRefGoogle ScholarPubMed
Mamassian, P. (1997). Prehension of objects oriented in three-dimensional space. Experimental Brain Research 114, 235–245.CrossRefGoogle ScholarPubMed
Marotta, J.J., Behrmann, M. & Goodale, M.A. (1997). The removal of binocular cues disrupts the calibration of grasping in patients with visual form agnosia. Experimental Brain Research 116, 113–121.CrossRefGoogle ScholarPubMed
McKee, S.P., Levi, D.M. & Bowne, S.F. (1990). The imprecision of stereopsis. Vision Research 30, 1763–1779.CrossRefGoogle ScholarPubMed
Melmoth, D.R. & Grant, S. (2006). Advantages of binocular vision for the control of reaching and grasping. Experimental Brain Research 171, 317–388.CrossRefGoogle ScholarPubMed
Paulignan, Y., Jeannerod, M., Mackenzie, C. & Marteniuk, R. (1991 a). Selective perturbation of visual input during prehension movements. 2. The effects of changing object size. Experimental Brain Research 87, 407–420.CrossRefGoogle ScholarPubMed
Paulignan, Y., Mackenzie, C., Marteniuk, R. & Jeannerod, M. (1991 b). Selective perturbation of visual input during prehension movements. 1. The effects of changing object position. Experimental Brain Research 83, 502–512.CrossRefGoogle ScholarPubMed
Prablanc, C. & Martin, O. (1992). Automatic-control during hand reaching at undetected 2-dimensional target displacements. Journal of Neurophysiology 57, 455–469.CrossRefGoogle Scholar
Roitman, J.D. & Shadlen, M.N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience 22, 9475–9489.CrossRefGoogle ScholarPubMed
Schrater, P.R. & Kersten, D. (2000). How optimal depth cue integration depends on the task. International Journal of Computer Vision 40, 73–91.CrossRefGoogle Scholar
Servos, P., Goodale, M.A. & Jakobson, L.S. (1992). The role of binocular vision in prehension: A kinematic analysis. Vision Research 32, 1513–1521.CrossRefGoogle ScholarPubMed
Smeets, J.B. & Brenner, E. (1999). A new view on grasping. Motor Control 3, 237–271.CrossRefGoogle ScholarPubMed
Stevens, K.A. (1983). Slant-tilt: The visual encoding of surface orientation. Biological Cybernetics 46, 183–195.CrossRefGoogle ScholarPubMed
Watt, S.J. & Bradshaw, M.F. (2000). Binocular cues are important in controlling the grasp but not the reach in natural prehension movements. Neuropsychologia 38, 1473–1481.CrossRefGoogle Scholar
Witkin, A.P. (1981). Recovering surface shape and orientation from texture. Artificial Intelligence 17, 17–45.CrossRefGoogle Scholar