August 2010
Volume 10, Issue 7
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2010
A recurrent Bayesian model of dynamic motion integration for smooth pursuit
Author Affiliations
  • Amarender Bogadhi
    Team DyVA, INCM, CNRS & Université de la Méditerranée, Marseille, France
  • Anna Montagnini
    Team DyVA, INCM, CNRS & Université de la Méditerranée, Marseille, France
  • Pascal Mamassian
    LPP, CNRS & Paris Descartes, Paris, France
  • Laurent Perrinet
    Team DyVA, INCM, CNRS & Université de la Méditerranée, Marseille, France
  • Guillaume Masson
    Team DyVA, INCM, CNRS & Université de la Méditerranée, Marseille, France
Journal of Vision August 2010, Vol.10, 545. doi:10.1167/10.7.545
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      Amarender Bogadhi, Anna Montagnini, Pascal Mamassian, Laurent Perrinet, Guillaume Masson; A recurrent Bayesian model of dynamic motion integration for smooth pursuit. Journal of Vision 2010;10(7):545. doi: 10.1167/10.7.545.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The quality of the estimate of an object's global motion, over time is not only affected by the noise in motion information but also by the spatial limitation of the local motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that during the initial stages of the motion information processing, 1D motion cues related to the objects edges have a dominating influence over the estimate of the objects global motion. However, during the later stages, 2D motion cues related to terminators (edge-endings) progressively take over leading to a final correct estimate of the objects global motion. Here, we propose a recursive extension to the Bayesian framework to describe the dynamic integration of 1D and 2D motion information. In the recurrent Bayesian framework, the prior defined in the velocity space is combined with the two independent measurement likelihood functions (Likelihood functions representing edge-related and terminator-related information) to obtain the posterior. The prior is updated with the posterior at the end of each iteration step. The recurrent Bayesian network is cascaded with a first order filter to mimic the oculomotor dynamics in the final output of the model. This oculomotor dynamics was tuned with single blobs moving in 8 different directions. The model parameters were fitted to human smooth pursuit recordings for different stimulus parameters (speed, contrast) across three subjects. The model results indicate that for a given velocity with increase in contrast, the latency decreases and for a given contrast with increase in velocity, the acceleration increases similar to what is being observed in smooth pursuit recordings. Also, The latency for a tilted line is shorter compared to the latency for the blob.

Bogadhi, A. Montagnini, A. Mamassian, P. Perrinet, L. Masson, G. (2010). A recurrent Bayesian model of dynamic motion integration for smooth pursuit [Abstract]. Journal of Vision, 10(7):545, 545a, http://www.journalofvision.org/content/10/7/545, doi:10.1167/10.7.545. [CrossRef]
Footnotes
 CODDE project (EU Marie Curie ITN), CNRS.
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