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Measuring the brain's assumptions

An Erratum to this article was published on 01 July 2006

This article has been updated

A Bayesian model of visual motion perception describes how the brain combines assumptions with evidence. A new study in this issue tests and expands the model, building connections between perception, the environment and neural responses.*

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Figure 1: The Bayesian model of speed perception, and its predictions.

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  • 09 June 2006

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Notes

  1. *NOTE: In the version of this article originally published in print and online, the abstract contained an error. The abstract should read “A Bayesian model of visual motion perception describes how the brain combines assumptions with evidence. A new study in this issue tests and expands the model, building connections between perception, the environment and neural responses.” The error has been corrected in the HTML and PDF versions of the article.

References

  1. Thompson, P. Vision Res. 22, 377–380 (1982).

    Article  CAS  Google Scholar 

  2. Stone, L.S. & Thompson, P. Vision Res. 32, 1535–1549 (1992).

    Article  CAS  Google Scholar 

  3. Snowden, R., Stimpson, N. & Ruddle, R. Nature 392, 450 (1998).

    Article  CAS  Google Scholar 

  4. Frazor, R.A. & Geisler, W.S. Vision Res. 46, 1585–1598 (2006).

    Article  Google Scholar 

  5. Stocker, A. & Simoncelli, E.P. Nature Neuroscience 9, 578–585 (2006).

    Article  CAS  Google Scholar 

  6. Weiss, Y., Simoncelli, E.P. & Adelson, E. Nat. Neurosci. 5, 598–604 (2002).

    Article  CAS  Google Scholar 

  7. Weiss Y. & Adelson, E.H. AI Memo #1624, MIT (1998).

  8. Hürlimann, F., Kiper, D. & Carandini, M. Vision Res. 42, 2253–2257 (2002).

    Article  Google Scholar 

  9. Kara, P., Reinagel, P. & Reid, R.C. Neuron 27, 635–646 (2000).

    Article  CAS  Google Scholar 

  10. Carandini, M. PLoS Biol. 2, e264 (2004).

    Article  Google Scholar 

  11. Green, D.M. & Swets, J.A. Signal Detection Theory and Psychophysics (Wiley, New York, 1966).

    Google Scholar 

  12. Knill, D.C. & Pouget, A. Trends Neurosci. 27, 712–719 (2004).

    Article  CAS  Google Scholar 

  13. Liu, J. & Newsome, W.T. J. Neurosci. 25, 711–722 (2005).

    Article  CAS  Google Scholar 

  14. Pack, C.C., Hunter, J.N. & Born, R.T. J. Neurophysiol. 93, 1809–1815 (2005).

    Article  Google Scholar 

  15. Priebe, N.J. & Lisberger, S.G. J. Neurosci. 24, 1907–1916 (2004).

    Article  CAS  Google Scholar 

Download references

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Carandini, M. Measuring the brain's assumptions. Nat Neurosci 9, 468–470 (2006). https://doi.org/10.1038/nn0406-468

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