Abstract
A reliable unified metric for the effects of lateral masking on foveal visual acuity remains important and elusive due to the variety of optotypes and spacings used, especially for clinical testing of children. We sought to find a measure of lateral masking that is conserved across clinical optotypes. We asked: 1) Does a target surrounded by pictures or symbols produce similar effects to letters? 2) How do these relate to the effects of bars or a box? 3) Is any masking metric conserved across the different stroke:size ratios of HOTV (5:1), Lea Symbols (7:1), and Kay Pictures (10:1)? For three adults, the method of constant stimuli yielded psychometric functions of performance versus target size for three flanker conditions (box, bars, similar optotypes). Visual acuities and psychometric function slopes were estimated for eight target-flanker separations (0-10 stroke-widths) and for an isolated optotype. A clinical staircase was used separately on 16 adults to estimate acuity for an isolated optotype and the optimal flanker position for each of 3 metrics (stroke-width edge-to-edge; arcmin edge-to-edge; optotype-width centre-to-centre). A repeated measures ANOVA performed on laboratory data revealed that the visual acuity versus flanker separation (in stroke-widths) function was conserved across HOTV, Lea symbols, and Kay pictures. Psychometric function slopes (performance versus target size) were significantly steeper than for isolated targets when flankers were 2 stroke widths away. Lateral masking was estimated from clinical staircases. It was strongest when flankers were similar optotypes. It was conserved across optotypes when using units of either stroke-width or arcmin. When data from both groups were combined, lateral masking was best conserved when expressed as edge-to-edge spacing in units of stroke-width. Lateral masking effects on visual acuity measures are most consistent when surrounding flankers are similar optotypes and units of stroke-width are used to specify separation.
Meeting abstract presented at VSS 2017