OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 15, Iss. 2 — Feb. 1, 1998
  • pp: 289–296

Insights into motion perception by observer modeling

Roland Baddeley and Srimant P. Tripathy  »View Author Affiliations

JOSA A, Vol. 15, Issue 2, pp. 289-296 (1998)

View Full Text Article

Enhanced HTML    Acrobat PDF (335 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The statistical efficiency of human observers performing a simplified version of the motion detection task of Salzman Newsome [Science 264, 231 (1994)] is high but not perfect. This reduced efficiency may be caused by noise internal to the observers or by the observers’ using strategies that are different from that used by an ideal machine. We therefore investigated which of three simple models best accounts for the observers’ performance. The models compared were a motion detector that uses the proportion of dots in the first frame that move coherently (as would an ideal machine), a model that bases its decision on the number of dots that move, and a model that differentially weights motions that occur at different locations in the visual field (for instance, differentially weights the point of fixation and the periphery). We compared these models by explicitly modeling the human observers’ performance. We recorded the exact stimulus configuration on each trial together with the observer’s response, and, for the different models, we found the parameters that best predicted the observer’s performance in a least-squares sense. We then used N -fold cross validation to compare the models and hence the associated hypotheses. Our results show that the performance of observers is based on the proportion, not the absolute number, of dots that are moving and that there was no evidence of any differential spatial weighting. Whereas this method of modeling the observers’ response is demonstrated only for one simple psychophysical paradigm, it is general and can be applied to any psychophysical framework in which the entire stimulus can be recorded.

© 1998 Optical Society of America

OCIS Codes
(000.4920) General : Other life sciences
(000.5490) General : Probability theory, stochastic processes, and statistics
(100.3190) Image processing : Inverse problems
(330.4060) Vision, color, and visual optics : Vision modeling
(330.4150) Vision, color, and visual optics : Motion detection
(330.5020) Vision, color, and visual optics : Perception psychology

Roland Baddeley and Srimant P. Tripathy, "Insights into motion perception by observer modeling," J. Opt. Soc. Am. A 15, 289-296 (1998)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. D. Salzman and W. Newsome, “Neural mechanisms for forming a perceptual decision,” Science 264, 231–237 (1994). [CrossRef] [PubMed]
  2. S. Watamaniuk, “Ideal observer for discrimination of the global direction of dynamic random-dot stimuli,” J. Opt. Soc. Am. A 10, 16–28 (1993). [CrossRef] [PubMed]
  3. H. Barlow, S. Tripathy, and R. Baddeley, “The statistical efficiency for detecting coherent motion,” Perception 24, 1 (1995), European Conference for Visual Perception Suppl.
  4. H. Barlow and S. Tripathy, “Correspondence noise and signal pooling in the detection of coherent visual motion,” submitted to J. Neurosci.
  5. D. Williams and R. Sekuler, “Coherent motion percepts from stochastic local motions,” Vision Res. 24, 55–62 (1984). [CrossRef]
  6. C. Downing and J. Movshon, “Spatial and temporal summation in stochastic random dot displays,” Invest. Ophthalmol. Visual Sci. Suppl. 30, 72 (1989).
  7. R. Fredericksen, F. Verstraten, and W. Van de Grind, “Spatial summation and its interaction with the temporal integration mechanism in human motion perception,” Vision Res. 34, 3171–3188 (1994). [CrossRef] [PubMed]
  8. A. Smith, R. Snowden, and A. Milne, “Is global motion really based on spatial integration of local motion signal,” Vision Res. 34, 2425–2430 (1994). [CrossRef] [PubMed]
  9. A. van Doorn and J. Koenderink, “Spatiotemporal integration in the detection of coherent motion,” Vision Res. 24, 47–53 (1984). [CrossRef] [PubMed]
  10. W. van de Grind, J. Koenderink, A. van Doorn, M. Milders, and H. Voerman, “Inhomogeneity and anisotropies for motion detection in the monocular visual field of human observers,” Vision Res. 33, 1089–1107 (1993). [CrossRef] [PubMed]
  11. R. Scobey and P. van Kan, “A horizontal stripe of displacement sensitivity in the human visual field,” Vision Res. 31, 99–109 (1991). [CrossRef] [PubMed]
  12. C. Bishop, Neural Networks for Pattern Recognition (Clarendon, Oxford, 1995).
  13. W. Press, B. Flannery, S. Teukolsky, and W. Vetterling, Numerical Recipes in C, 2nd ed. Cambridge U. Press, Cambridge, 1992).
  14. H. Akaike, “A new look at the statistical model identification,” IEEE Trans. Autom. Control 19, 716–723 (1974). [CrossRef]
  15. D. Mackay, “Bayesian model comparison and backprop nets,” in Advances in Neural Information Processing Systems, J. Moody, S. Hanson, and R. Lippmann, eds. (Morgan Kaufmann, Los Altos, Calif., 1992), Vol. 4, pp. 839–846.
  16. P. Zhang, “Model selection via multifold cross-validation,” Ann. Statist. 21, 299–313 (1993).
  17. J. Bridle, “Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters,” in Neural Information Processing, D. Touretzky, ed. (Morgan Kaufmann, Los Altos, Calif., 1990), Vol. 2, pp. 211–217.
  18. D. Fotheringhame and R. Baddeley, “Nonlinear principal components analysis of neuronal spike train data shows no evidence of nonlinear structure,” Biol. Cybern. (to be published).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Fig. 1 Fig. 2 Fig. 3
Fig. 4 Fig. 5

Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited