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Jose Rivera-Aparicio, Chaz Firestone; Vision science at the bar: The role of closure in a powerful geometric illusion. Journal of Vision 2018;18(10):1318. doi: https://doi.org/10.1167/18.10.1318.
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© ARVO (1962-2015); The Authors (2016-present)
A notoriously tricky "bar bet" proceeds as follows: One patron wagers another that the distance around the rim of a standard pint glass is about twice the glass's height. Surprisingly, this patron is usually correct, owing to a powerful (but, to our knowledge, unexplained) visual illusion wherein we severely underestimate the circumferences of circles. Here, we characterize this illusion and test an explanation of it: We suggest that the difficulty in properly estimating the perimeters of circles and other shapes stems in part from the visual system's representation of such shapes as closed objects, rather than as open contours which might be easier to 'mentally unravel'. Subjects who saw circles of various sizes and adjusted a line to match the circles' circumferences greatly underestimated circumference — initially by a magnitude of over 35%. (Care was taken to exclude subjects who conflated circumference with diameter.) Estimates for these closed circles were then compared to estimates of the perimeter of a circle that was missing a continuous 18-degree segment of arc. We predicted that removing a portion of the circle's perimeter would, paradoxically, cause the circle's perimeter to appear longer, since this violation of closure would bias the visual system to process the stimulus as an open contour. Results revealed that, indeed, this manipulation reliably reduced the magnitude of this "pint glass illusion" by as much as 30%, such that a circle missing a portion of its circumference was judged to have a greater perimeter than a complete, closed circle of the same diameter. We suggest that the property of closure not only influences whether a stimulus is processed as an object, but also constrains how easily such a stimulus can be manipulated in the mind.
Meeting abstract presented at VSS 2018
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