Active vision in sight recovery individuals with a history of long-lasting congenital blindness

Abstract

What we see is intimately linked to how we actively and systematically explore the world through eye movements. However, it is unknown to what degree visual experience during early development is necessary for such systematic visual exploration to emerge. The present study investigated visual exploration behavior in ten human participants whose sight had been restored only in childhood or adulthood, after a period of congenital blindness due to dense bilateral congenital cataracts. Participants freely explored real-world images while their eye movements were recorded. Despite severe residual visual impairments and gaze instability (nystagmus), visual exploration patterns were preserved in individuals with reversed congenital cataract. Modelling analyses indicated that similar to healthy controls, visual exploration in individuals with reversed congenital cataract was based on the low-level (luminance contrast) and high-level (object components) visual content of the images. Moreover, participants used visual short-term memory representations for narrowing down the exploration space. More systematic visual exploration in individuals with reversed congenital cataract was associated with better object recognition, suggesting that active vision might be a driving force for visual system development and recovery. The present results argue against a sensitive period for the development of neural mechanisms associated with visual exploration.SIGNIFICANCE STATEMENTHumans explore the visual world with systematic patterns of eye movements, but it is unknown whether early visual experience is necessary for the acquisition of visual exploration. Here, we show that sight recovery individuals who had been born blind demonstrate highly systematic eye movements while exploring real-world images, despite visual impairments and pervasive gaze instability. In fact, their eye movement patterns were predicted by those of normally sighted controls and models calculating eye movements based on low- and high-level visual features, and they moreover took memory information into account. Since object recognition performance was associated with systematic visual exploration it was concluded that eye movements might be a driving factor for the development of the visual system.

Keywords: congenital cataracts; eye movements; nystagmus; sensitive period; sight-restoration.

Figure 1.

Eye movement kinematics during the visual exploration of an example image. a, Examples of eye movement recordings of one participant from each group. Images were explored for 4 s. The left panels depict the gaze traces overlaid on a line-drawing sketch of the original photographic grayscale image; note that participants watched the original grayscale images. The right panels show eye movement traces as they progress over time and space along the horizontal (dark lines) and vertical (light lines) dimension. Extended Data Figures 1-1 and 1-2 show two other examples of eye movement recordings. b, Distribution of the magnitude of instantaneous gaze velocity. Light lines indicate each participant’s distribution, and dark lines each group’s average distribution. Colored circles display each participant’s median value, and the yellow dots and error bars display the group’s mean and SEM (Extended Data Fig. 1-3, statistics). c, Distribution of instantaneous gaze velocity (bin size, 16°/s; densities were individually generated for each participant and then averaged across the participants of each group). The color scale indicates the probability of a given gaze velocity in log₁₀ scale. Yellow and white contours indicate areas that span ∼75% and 90% of the distribution. In all figures, significant contrasts among groups are indicated as follows: *p < 0.01, **p < 0.001, ***p < 0.0001, respectively.

Figure 2.

Examples of visual exploration by group. The subpanels show, for different images and the four groups of participants (Fig. 1, description), the spatial distributions of the probabilities to gaze different locations (pooled across participants and smoothed with a 2D Gaussian unit kernel), superimposed over line-drawing sketches of the original images. Warmer colors indicate a higher probability to gaze a location. Yellow contours indicate areas that span the top 50%, 75%, and 95% of the spatial distribution. As this distribution is constructed from all gaze eye-tracking samples (each occurring every 2 ms), these maps are equivalent to the spatial distributions of dwell time. The mean of entropy and AUC values for each of the four images are indicated by the corresponding symbol (star, square, and left and right pointing triangles) in Figure 3, b and f. The last column shows the DG-II and ICF predictor maps for each image. Extended Data Figure 2-1 shows the grand average of the spatial distributions of the probability to gaze a certain location across all images separately for each of the four groups. In addition, the corresponding grand average DG-II and ICF predictor maps are displayed.

Figure 3.

Spatial spread and predictability of visual exploration patterns. a, Mean gaze entropy for each group (yellow dot with error bars, indicating the SEM) as well as for individual participants (colored dots; Extended Data Fig. 3-1, statistics). b, CC participants gaze entropy per image compared with the gaze entropy values of the other three control groups. Colored continuous lines indicate a linear regression line for entropy values of the CC group (x-axis) and each one of the three control groups (SC, blue; DC, green; NC, orange). The top left inset depicts the corresponding Pearson’s correlation values (in a red scale, top right corner) and the corresponding p-values (in green, lower left corner). Asterisks indicate significant correlations after controlling for multiple comparisons (α = 0.05/6). c, AUC values of the SC predictor map per participant and group. Dark-colored dots indicate AUC values for individual participants as derived by the predictor maps of the SC group to classify gaze and nongazed location. Light-colored circles from the corresponding AUC values for the control analysis in which image correspondence was shuffled. Bottom, Colored stars indicate that actual and control analysis values significantly differed. The control analysis values were not different from 0.5 (chance level). Extended Data Figure 3-2 shows statistics, and Extended Data Figure 3-3 shows the relationship between AUC values and different CC participants’ characteristics. d, AUC values of the SC predictor map across time. Curves show, for each group, AUC values calculated from consecutive 500 ms data partitions (Extended Data Fig. 3-4, statistics). e, AUC values of the SC predictor map as a function of instantaneous gaze velocity. SC predictor maps were used to calculate gaze in CC individuals separately for 10 quantiles of instantaneous gaze velocity (Extended Data Fig. 3-5, statistics). Extended Data Figure 3-6 shows the relationship between gaze velocity during fixations (SC and DC groups) and CC and NC participants’ first and second instantaneous gaze velocity quantiles. f, Correlations of entropy and AUC values across all images for the CC group. Different object categories are color coded. Extended Data Figure 3-7 shows the same correlation for SC, NC, and DC groups.

Figure 4.

Degree of explained visual exploration behavior for low-level and high-level visual information and context. a, AUC values resulting from the low-level ICF predictor maps (Extended Data Fig. 4-1, statistics). b, AUC values resulting from the high-level DG-II predictor maps (Extended Data Fig. 4-2, statistics). c, Ratio between ICF and DG-II AUC values (Extended Data Fig. 4-3, statistics). Extended Data Figures 4-4, 4-5, and 4-6 show AUC values of the ICF and DG-II predictor map across time and the corresponding statistics. Extended Data Figure 4-7 shows the ratio between ICG and DG-II AUC values obtained from low-pass-filtered versions of the images and the AUC values obtained from the nonfiltered images (Extended Data Figs. 4-8, 4-9, statistics).

Figure 5

Effect of stimulus repetition and object recognition performance. a, Gaze entropy for the first versus the second presentation of the same image (si), different images from the same object category (soc), and different images from different object categories (doc; Extended Data Fig. 5-1, 5-2, 5-3, statistics. b, Percentage of correct images recognized for in each group (mean group performance in black with error bars indicating the SEM; Extended Data Fig. 5-4, statistics). c, Recognition performance, visual acuity (logMar) and AUC values (obtained using SC predictor maps) for each CC individual. The blue shade mesh depicts the generalized logistic fit. Black lines starting at the red dots indicate the discrepancy between actual performance of a CC participant and model predictions (Extended Data Fig. 5-5, statistics). Extended Data Figures 5-6 and 5-7 show the relationship between performance and age at testing.