What a difference a parameter makes: A psychophysical comparison of random dot motion algorithms

Random dot motion (RDM) displays have emerged as one of the standard stimulus types employed in psychophysical and physiological studies of motion processing. RDMs are convenient because it is straightforward to manipulate the relative motion energy for a given motion direction in addition to stimulus parameters such as the speed, contrast, duration, density, aperture, etc. However, as widely as RDMs are employed so do they vary in their details of implementation. As a result, it is often difficult to make direct comparisons across studies employing different RDM algorithms and parameters. Here, we systematically measure the ability of human subjects to estimate motion direction for four commonly used RDM algorithms under a range of parameters in order to understand how these different algorithms compare in their perceptibility. We find that parametric and algorithmic differences can produce dramatically different performances. These effects, while surprising, can be understood in relationship to pertinent neurophysiological data regarding spatiotemporal displacement tuning properties of cells in area MT and how the tuning function changes with stimulus contrast and retinal eccentricity. These data help give a baseline by which different RDM algorithms can be compared, demonstrate a need for clearly reporting RDM details in the methods of papers, and also pose new constraints and challenges to models of motion direction processing.

[1]  Gregory C. DeAngelis,et al.  A neural representation of depth from motion parallax in macaque visual cortex , 2008, Nature.

[2]  Brian J. Rogers,et al.  Effects of dot density, patch size and contrast on the upper spatial limit for direction discrimination in Random-dot Kinematograms , 1997, Vision Research.

[3]  K. H. Britten,et al.  Responses of neurons in macaque MT to stochastic motion signals , 1993, Visual Neuroscience.

[4]  W. Newsome,et al.  Motion selectivity in macaque visual cortex. III. Psychophysics and physiology of apparent motion. , 1986, Journal of neurophysiology.

[5]  Curtis L. Baker,et al.  Eccentricity-dependent scaling of the limits for short-range apparent motion perception , 1985, Vision Research.

[6]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[7]  Robert Sekuler,et al.  Coherent global motion percepts from stochastic local motions , 1984, Vision Research.

[8]  O. Braddick,et al.  What is Noise for the Motion System? , 1996, Vision Research.

[9]  V. Lollo,et al.  Effects of adapting luminance and stimulus contrast on the temporal and spatial limits of short-range motion , 1990, Vision Research.

[10]  Randolph Blake,et al.  Perceptual consequences of centre–surround antagonism in visual motion processing , 2003, Nature.

[11]  T. Pasternak,et al.  Directional Signals in the Prefrontal Cortex and in Area MT during a Working Memory for Visual Motion Task , 2006, The Journal of Neuroscience.

[12]  William Curran,et al.  The dependence of perceived speed upon signal intensity , 2009, Vision Research.

[13]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[14]  K. H. Britten,et al.  Neuronal plasticity that underlies improvement in perceptual performance. , 1994, Science.

[15]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

[16]  D. Bradley,et al.  Neural population code for fine perceptual decisions in area MT , 2005, Nature Neuroscience.

[17]  C. Law,et al.  Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area , 2008, Nature Neuroscience.

[18]  R. Freeman,et al.  Direction selectivity of neurons in the striate cortex increases as stimulus contrast is decreased. , 2006, Journal of neurophysiology.

[19]  Aaron R. Seitz,et al.  Interactions between contrast and spatial displacement in visual motion processing , 2008, Current Biology.

[20]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[21]  Praveen K. Pilly,et al.  Temporal dynamics of decision-making during motion perception in the visual cortex , 2008, Vision Research.

[22]  O. J. Braddick,et al.  Extension of displacement limits in multiple-exposure sequences of apparent motion , 1989, Vision Research.

[23]  Bevil R. Conway,et al.  Contrast affects speed tuning, space-time slant, and receptive-field organization of simple cells in macaque V1. , 2007, Journal of neurophysiology.

[24]  M. Shadlen,et al.  The effect of stimulus strength on the speed and accuracy of a perceptual decision. , 2005, Journal of vision.

[25]  K. Nakayama,et al.  Temporal and spatial characteristics of the upper displacement limit for motion in random dots , 1984, Vision Research.

[26]  Scott N. J. Watamaniuk,et al.  Temporal and spatial integration in dynamic random-dot stimuli , 1992, Vision Research.

[27]  W. Newsome,et al.  Motion selectivity in macaque visual cortex. II. Spatiotemporal range of directional interactions in MT and V1. , 1986, Journal of neurophysiology.

[28]  M. J. M. Lankheet,et al.  PII: S0042-6989(00)00187-5 , 2000 .

[29]  O J Braddick,et al.  Temporal Properties of the Short-Range Process in Apparent Motion , 1985, Perception.

[30]  H. Barlow,et al.  The mechanism of directionally selective units in rabbit's retina. , 1965, The Journal of physiology.

[31]  Stephen Grossberg,et al.  Neural dynamics of motion integration and segmentation within and across apertures , 2001, Vision Research.

[32]  K. H. Britten,et al.  A relationship between behavioral choice and the visual responses of neurons in macaque MT , 1996, Visual Neuroscience.

[33]  Bart Krekelberg,et al.  Interactions between Speed and Contrast Tuning in the Middle Temporal Area: Implications for the Neural Code for Speed , 2006, The Journal of Neuroscience.

[34]  M. Shadlen,et al.  Response of Neurons in the Lateral Intraparietal Area during a Combined Visual Discrimination Reaction Time Task , 2002, The Journal of Neuroscience.

[35]  Stephen T. Hammett,et al.  Speed can go up as well as down at low contrast: Implications for models of motion perception , 2006, Vision Research.

[36]  J. Movshon,et al.  The analysis of visual motion: a comparison of neuronal and psychophysical performance , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[37]  Lynne Kiorpes,et al.  Development of sensitivity to visual motion in macaque monkeys , 2004, Visual Neuroscience.

[38]  Aaron R. Seitz,et al.  Psychophysics: Is subliminal learning really passive? , 2003, Nature.

[39]  Stephen Grossberg,et al.  Neural dynamics of motion grouping: from aperture ambiguity to object speed and direction , 1997 .

[40]  Steven R. Holloway,et al.  Seeing what is not there shows the costs of perceptual learning. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[41]  C. Baker,et al.  The basis of area and dot number effects in random dot motion perception , 1982, Vision Research.

[42]  Nicholas J. Priebe,et al.  Comparison of the spatial limits on direction selectivity in visual areas MT and V1. , 2005, Journal of neurophysiology.

[43]  R. Sekuler,et al.  A specific and enduring improvement in visual motion discrimination. , 1982, Science.

[44]  Xin Huang,et al.  Responses of neurons in the medial superior temporal visual area to apparent motion stimuli in macaque monkeys. , 2007, Journal of neurophysiology.

[45]  R. Andersen,et al.  Integration of motion and stereopsis in middle temporal cortical area of macaques , 1995, Nature.

[46]  Bevil R. Conway,et al.  Spatiotemporal Structure of Nonlinear Subunits in Macaque Visual Cortex , 2006, The Journal of Neuroscience.

[47]  Christopher C. Pack,et al.  Contrast dependence of suppressive influences in cortical area MT of alert macaque. , 2005, Journal of neurophysiology.

[48]  W. Reichardt,et al.  Autocorrelation, a principle for the evaluation of sensory information by the central nervous system , 1961 .

[49]  José E. Náñez,et al.  Greater plasticity in lower-level than higher-level visual motion processing in a passive perceptual learning task , 2002, Nature Neuroscience.

[50]  Harriet A Allen,et al.  Visual mechanisms of motion analysis and motion perception. , 2004, Annual review of psychology.

[51]  O. Braddick.  A short-range process in apparent motion. , 1974, Vision research.

[52]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[53]  William T Newsome,et al.  Middle Temporal Visual Area Microstimulation Influences Veridical Judgments of Motion Direction , 2002, The Journal of Neuroscience.