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Factors limiting sensitivity to binocular disparity in human vision: Evidence from a noise-masking approach.

Sara Alarcon Carrillo | Alex S Baldwin | Robert F Hess
Journal of vision | 2020

Our visual system uses the disparity between the images received by the two eyes to judge three-dimensional distance to surfaces. We can measure this ability by having subjects discriminate the disparity of rendered surfaces. We wanted to know the basis of the individual differences in this ability. We tested 53 adults with normal vision using a relative disparity detection task. Targets were wedge-shaped surfaces formed from random dots. These were presented in either crossed or uncrossed disparity relative to a random dot background. The threshold disparity ranged from 24 arc seconds in the most-able subject to 275 arc seconds in the least-able subject. There was a small advantage for detecting crossed-disparity targets. We used the noise-masking paradigm to partition subject performance into two factors. These were the subject's equivalent internal noise and their processing efficiency. The parameters were estimated by fitting the linear amplifier model. We found both factors contributed to the individual differences in stereoacuity. Within subjects, those showing an advantage for one disparity direction had enhanced efficiency for that direction. Some subjects had a higher equivalent internal noise for one direction that was balanced out by an increased efficiency. Our approach provides a more thorough account of the stereo-ability of our subjects compared with measuring thresholds alone. We present a normative set of results that can be compared with clinical populations.

Pubmed ID: 32232374

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SciPy (tool)

RRID:SCR_008058

A Python-based environment of open-source software for mathematics, science, and engineering. The core packages of SciPy include: NumPy, a base N-dimensional array package; SciPy Library, a fundamental library for scientific computing; and IPython, an enhanced interactive console.

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