Motion perception

The dorsal stream (green) and ventral stream (purple) are shown. They originate from a common source in visual cortex. The dorsal stream is responsible for detection of location and motion.

Motion perception is the process of inferring the speed and direction of elements in a scene based on visual, vestibular and proprioceptive inputs. Although this process appears straightforward to most observers, it has proven to be a difficult problem from a computational perspective, and extraordinarily difficult to explain in terms of neural processing.

Motion perception is studied by many disciplines, including psychology (i.e. visual perception), neurology, neurophysiology, engineering, and computer science.



The inability to perceive motion is called akinetopsia and it may be caused by a lesion to cortical area V5 in the extrastriate cortex. It can also be caused as a side effect of certain antidepressant drugs,[citation needed] or due to damage by a stroke or certain brain surgeries. In some cases akinetopsia can be treated by brain surgery or discontinuation of antidepressants.

Neuropsychological studies of a patient who could not see motion, seeing the world in a series of static "frames" instead, suggested that visual area V5 in humans is homologous to motion processing area MT in primates.[1][2]

First-order motion perception

Example of Beta movement, often confused with phi phenomenon, in which a succession of still images gives the illusion of a moving ball.[3]

First-order motion perception refers to the perception of the motion of an object that differs in luminance from its background, such as a black bug crawling across a white page. This sort of motion can be detected by a relatively simple motion sensor designed to detect a change in luminance at one point on the retina and correlate it with a change in luminance at a neighbouring point on the retina after a delay. Sensors that work this way have been referred to as Hassenstein-Reichardt detectors after the scientists Bernhard Hassenstein, behaviour analyses and Werner Reichardt, who first modelled them.[4] motion-energy sensors,[5] or Elaborated Reichardt Detectors.[6] These sensors detect motion by spatio-temporal correlation and are plausible models for how the visual system may detect motion. There is still considerable debate regarding the exact nature of this process. First-order motion sensors suffer from the aperture problem, which means that they can detect motion only perpendicular to the orientation of the contour that is moving. Further processing is required to disambiguate true global motion direction.[7]

Second-order motion perception

Second-order motion is motion in which the moving contour is defined by contrast, texture, flicker or some other quality that does not result in an increase in luminance or motion energy in the Fourier spectrum of the stimulus.[8][9] There is much evidence to suggest that early processing of first- and second-order motion is carried out by separate pathways.[10] Second-order mechanisms have poorer temporal resolution and are low-pass in terms of the range of spatial frequencies to which they respond. Second-order motion produces a weaker motion aftereffect unless tested with dynamically flickering stimuli.[11] First and second-order signals appear to be fully combined at the level of Area V5/MT of the visual system.

Motion integration

Having extracted motion signals (first- or second-order) from the retinal image, the visual system must integrate those individual local motion signals at various parts of the visual field into a 2-dimensional or global representation of moving objects and surfaces.

The aperture problem

The aperture problem. The grating appears to be moving down and to the right, perpendicular to the orientation of the bars. But it could be moving in many other directions, such as only down, or only to the right. It is impossible to determine unless the ends of the bars become visible in the aperture.

Each neuron in the visual system is sensitive to visual input in a small part of the visual field, as if each neuron is looking at the visual field through a small window or aperture. The motion direction of a contour is ambiguous, because the motion component parallel to the line cannot be inferred based on the visual input. This means that a variety of contours of different orientations moving at different speeds can cause identical responses in a motion sensitive neuron in the visual system.

Individual neurons early in the visual system (LGN or V1) respond to motion that occurs locally within their receptive field. Because each local motion-detecting neuron will suffer from the aperture problem, the estimates from many neurons need to be integrated into a global motion estimate. This appears to occur in Area MT/V5 in the human visual cortex.

The same problem is found in mathematical optical flow estimation techniques. See also Barber's pole and the barberpole illusion.

Motion in depth

As in other aspects of vision, the observer's visual input is generally insufficient to determine the true nature of stimulus sources, in this case their velocity in the real world. In monocular vision for example, the visual input will be a 2D projection of a 3D scene. The motion cues present in the 2D projection will by default be insufficient to reconstruct the motion present in the 3D scene. Put differently, many 3D scenes will be compatible with a single 2D projection. The problem of motion estimation generalizes to binocular vision when we consider occlusion or motion perception at relatively large distances, where binocular disparity is a poor cue to depth. This fundamental difficulty is referred to as the inverse problem.

See also


  1. ^ Hess, Baker, Zihl (1989). "The "motion-blind" patient: low-level spatial and temporal filters". Journal of Neuroscience 9 (5): 1628–1640. PMID 2723744. 
  2. ^ Baker, Hess, Zihl (1991). "Residual motion perception in a "motion-blind" patient, assessed with limited-lifetime random dot stimuli". Journal of Neuroscience 11 (2): 454–461. PMID 1992012. 
  3. ^ Phi is not Beta slideshow
  4. ^ Reichardt, W. (1961). "Autocorrelation, a principle for the evaluation of sensory information by the central nervous system". W.A. Rosenblith (Ed.) Sensory communication (MIT Press): 303–317. 
  5. ^ Adelson, E.H., & Bergen, J.R. (1985). "Spatiotemporal energy models for the perception of motion". J Opt Soc Am A 2 (2): 284–299. doi:10.1364/JOSAA.2.000284. PMID 3973762. 
  6. ^ van Santen, J.P., & Sperling, G. (1985). "Elaborated Reichardt detectors". J Opt Soc Am A 2 (2): 300–321. doi:10.1364/JOSAA.2.000300. PMID 3973763. 
  7. ^ Todorović, Dejan (2002). "A NEW VARIANT OF THE BARBERPOLE EFFECT: PSYCHOPHYSICAL DATA AND COMPUTER SIMULATIONS". PSIHOLOGIJA (Serbia, Yugoslavia: Laboratory for Experimental Psychology, University of Belgrade) 35 (3-4): 209–223 UDC 159.937.075. Retrieved November 26, 2010.. .
  8. ^ Cavanagh, P & Mather, G (1989). "Motion: the long and short of it". Spatial vision 4 (2–3): 103–129. doi:10.1163/156856889X00077. PMID 2487159. 
  9. ^ Chubb, C & Sperling, G (1988). "Drift-balanced random stimuli: A general basis for studying non-Fourier motion perception". J Opt Soc Amer A 5 (11): 1986–2007. doi:10.1364/JOSAA.5.001986. 
  10. ^ Nishida, S., Ledgeway, T. & Edwards, M. (1997). "Dual multiple-scale processing for motion in the human visual system". Vision Research 37 (19): 2685–2698. doi:10.1016/S0042-6989(97)00092-8. PMID 9373668. 
  11. ^ Ledgeway, T. & Smith, A.T. (1994). "The duration of the motion aftereffect following adaptation to first- and second-order motion". Perception 23 (10): 1211–1219. doi:10.1068/p231211. PMID 7899037. 

External links

  • Interactive Reichardt Detector
  • B. Hassenstein and W. Reichardt, Structure of a mechanism of perception of optical movement, Proceedings of the 1st International Conference on Cybernetics, 1956, pp. 797–801.

Labs specialising in motion research

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