August 2010
Volume 10, Issue 7
Vision Sciences Society Annual Meeting Abstract  |   August 2010
Investigating the relationship between actual speed and perceived visual speed in humans
Author Affiliations
  • John A. Perrone
    The University of Waikato, New Zealand
  • Peter Thompson
    The University of York, UK
  • Richard J. Krauzlis
    The Salk Institute for Biological Sciences, U.S.A.
Journal of Vision August 2010, Vol.10, 818. doi:
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      John A. Perrone, Peter Thompson, Richard J. Krauzlis; Investigating the relationship between actual speed and perceived visual speed in humans. Journal of Vision 2010;10(7):818.

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

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A number of models have been proposed over the years that are able to estimate the speed of a moving image feature such as an edge but it is not obvious how these models should be assessed in terms of their performance. Over what range of speeds should a model's estimates of image velocity be veridical in order for it to be classed as effective? There is currently a lack of data that can directly inform us as to what the function looks like that links human estimates of speed (v′) to actual speed (v), i.e., v′ = f(v), f = ? On a plot of v′ versus v, it is difficult to establish the absolute location of the function but we will show that there already exists a range of psychophysical data which constrain the form it can take. For example, the U-shaped, speed discrimination (Weber fraction) curves obtained by a number of researchers (e.g., McKee, Vis Res., 1981; De Bruyn & Orban, Vis Res.1988) suggest that the v′= f(v) function for moving edges is s-shaped with the maximum slope occurring at intermediate speeds (approx 4 – 16 deg/s). We have discovered that this s-shape is also predicted by models of speed estimation that feature speed-tuned Middle Temporal (MT) neurons and which incorporate a weighted vector average (centroid) stage (e.g., Perrone & Krauzlis, VSS, 2009). Because the range of speed tunings in MT is naturally constrained at both the high and low speed ends, the centroid estimate of the MT activity distribution is biased as a result of ‘truncation effects’ caused by these lower and upper bounds; speed estimates in the model are overestimated at slow input speeds and underestimated at high input speeds producing an s-shaped, v′= f(v) function similar to that predicted by the speed discrimination data.

Perrone, J. A. Thompson, P. Krauzlis, R. J. (2010). Investigating the relationship between actual speed and perceived visual speed in humans [Abstract]. Journal of Vision, 10(7):818, 818a,, doi:10.1167/10.7.818. [CrossRef]
 JP & RK supported by a Royal Society of New Zealand Marsden Fund grant.

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