Abstract
Patients suffering of foveal vision loss (e.g. macular degeneration) must rely on peripheral vision to perform most visual tasks. Often, this is insufficient, and corrective measures are necessary: (1) magnification, i.e. enlarging the stimulus (e.g. moving closer to the stimulus, using magnifying lens), and/or (2) stimulus optimization (e.g. controlling the spacing between letters, using a font optimized for peripheral vision).
The current focus in peripheral vision research in normal populations is to measure, at each eccentricity, the smallest stimuli that can maintain threshold performance (i.e. lower limit). However, using multiple scaling theory (MST; Poirier & Gurnsey, 2002, 2005), we argue that this information is incomplete. Researchers also need to measure, at each eccentricity, the largest stimuli that can maintain threshold performance (i.e. upper limit).
Upper limits are not uncommon in perception: (1) texture discrimination has an upper limit, where texture discrimination is impaired if textels on either side of a texture edge are spaced too far apart, and (2) reading has an upper bound, where normal scanning and reading span functions are disrupted when letters are too spaced apart.
The relationship between these two limits determines if the task can be solved peripherally, and if so, what corrective measures are required. We will also review various sampling strategies used in peripheral vision research, and for each provide concrete ways to detect lower and upper limits.
We discuss several applications of this research, including (1) guidelines for generalizing results from normal to clinical populations and vice-versa, (2) guidelines for generalizing results from incomplete data sets or sub-optimal sampling strategies, and (3) guidelines for identifying the proper corrective measures. These guidelines could play an essential role in bridging peripheral vision research across clinical and normal populations.
CIHR grant awarded to Martin Arguin, Frédéric Gosselin, and Dan Bub.