Feature Counting Under Dichoptic Viewing in Anisometropic and Strabismic Amblyopia

Abstract

Purpose: While using their amblyopic eye, individuals with strabismic amblyopia count inaccurately and underestimate the number of features. These deficits are attributed to limitations in high-level cortical functions and attention. In the current study, we examined whether feature counting is affected in strabismic and anisometropic amblyopia during dichoptic viewing, a setup that can better capture binocular function disruptions.

Methods: Through a mirror stereoscope, Gabor patches were presented for 200 msec (Experiment 1) or 350 msec (Experiment 2) in both the left eye and the right eye of observers, who were required to combine the percepts and report the total number of patches. Counting performance and errors were compared across amblyopic groups and normal-sighted observers. The contribution and relation of each eye to performance was also evaluated.

Results: Anisometropic and strabismic amblyopia groups counted inaccurately and underestimated the number of features compared to the normal-sighted group. In both amblyopic groups, the amblyopic eye contributed less in comparison to the fellow eye. The strabismic group exhibited worse performance, and a more pronounced difference in eye contribution, in comparison to the anisometropic group.

Conclusions: Overall, our results support the view of higher-level cortical and binocular function deficits in amblyopia.

Translational relevance: The current study bridges the gap between research on high-cortical function deficits and clinical binocular function disruptions in amblyopia, which can help us better understand the neural mechanism of amblyopia and inform clinical therapeutic tasks and strategies.

Keywords: amblyopia; anisometropia; binocular disfunction; feature counting; strabismus.

Figure 1.

An example of the visual counting task stimuli is depicted in (a). The temporal sequence of a given trial is depicted in (b).

Figure 2.

Feature counting performance and error in Experiment 1. The mean feature counting performance across groups and individual mean performance is plotted in (a). The mean feature counting error across groups and individual mean errors is plotted in (b). Error bars: standard errors.

Figure 3.

The coefficients of set-size for each observer group in Experiment 1. The coefficient of set-size depicts the relationship between the reported number of features with the Gabor patch set-size. The coefficients for each observer group for the (a) feature counting regression analysis and (b) estimated counting error regression analysis. Error bars: confidence intervals. * = P < 0.01.

Figure 4.

Feature counting performance and error in Experiment 2. The mean feature counting performance across groups and individual mean performance is plotted in (a). The mean feature counting error across groups and individual mean errors is plotted in (b). Error bars: standard error.

Figure 5.

The coefficients of set-size for each observer group in Experiment 2. The coefficient of set-size depicts the relationship between the reported number of features with the Gabor patch set-size. The coefficients for each observer group for the (a) feature counting regression analysis and (b) estimated counting error regression analysis. Error bars: confidence intervals. * = P < 0.01.

Figure 6.

Correlations between visual counting error and clinical characteristics. Associations between visual acuity (a) and stereoacuity (b) and the mean visual counting error in amblyopes were assessed using Spearman's rank correlation. The correlation between stereoacuity and visual counting errors in strabismic amblyopes was not computable, since majority of this group had no measurable stereoacuity.