Cone-resolved adaptive optics micro-psychophysics.

a, Schematic of cell-resolved visual acuity testing in the human foveola with an adaptive optics scanning laser ophthalmoscope (AOSLO). Stimuli were dark Snellen-E optotypes presented at variable size and four orientations in the center of the 788 nm AOSLO imaging raster. Participants responded by indicating stimulus orientation during natural viewing, i.e. unrestricted eye motion. b, Exemplary single trial retinal motion trace and strip-wise image stabilization of a single AOSLO frame (shown here during a microsaccade for better visibility). Trials containing microsaccades or blinks during the 500 ms stimulus presentation (gray shaded area) were excluded. The x-axis grid represents individual video frames (33 ms). c, Foveolar retinal cone mosaic with exemplary single trial retinal motion across the stimulus. Time is represented by color from stimulus onset to offset (purple to yellow). The cone density centroid (CDC) is shown as a red circle with white fill. d, Typical psychophysical data of 5 consecutive runs in one eye. Each run followed a QUEST procedure with 20 trials. e, Psychometric function fit to the data (about 100 trials). Acuity thresholds were estimated at 62.5 % correct responses. f, Exemplary retinal images (upper rows) and corresponding cone activation patterns (lower rows) of one experimental run (20 trials from top left to bottom right). Cone activation patterns are shown for a representative single frame. See Supplementary Movie 1 and 2 for a real-time video representation.

Visual acuity depends on foveolar sampling capacity.

a, Foveolar cone mosaics of the two eyes with highest and lowest cone densities, overlayed with the physical stimulus at an average threshold size (24 arcsec). b, Nyquist limit: critical details equaling or larger than the spacing of cones are resolvable. c, Visual acuity thresholds measured with 788 or 840 nm infrared light, normalized to the eyes’ Nyquist limits. d, Correlation between participants individual visual acuity thresholds and cone density. Thresholds exceeded the Nyquist sampling limit and were significantly lower in eyes with higher cone densities. Dominant eyes are shown as filled, non-dominant eyes as open markers. The gray horizontal and vertical bars at each point represent standard deviations of sampling cone density and the 95 % confidence intervals for acuity thresholds. The theoretical Nyquist limit is represented by a dashed green line. e, Correlation between dominant and non-dominant eyes in visual acuity (top) and cone density (bottom). Dominant eyes reached, on average, 1.5 arcmin lower thresholds than non-dominant eyes, whereas cone density (at the retinal locations that sampled the stimulus) was very similar between fellow eyes.

Fixational drift and the contribution to visual acuity.

a, Ocular drift during stimulus presentation (participant 16, left eye). Single AOSLO frame captured during Snellen E presentation (top left) and all single stimulus positions (colored dots) of 5 experimental runs shown on the corresponding cone mosaic (panel 2-6). White iso-lines delimit cone density percentile areas (90th to 50th percentile visible). Time is represented by color from stimulus onset to offset (purple to yellow). b, Individual motion traces highlighting intra- and inter-subject drift variability. Traces are from one run in the participant with the lowest (upper rows) and highest (lower rows) average drift amplitudes. c, Computation of drift amplitude as a sum of interframe motion vectors (top) and the relative frequency of occurrences among all participants and trials (bottom). d, Median drift amplitude and drift amplitude range showed a moderate correlation in dominant as well as non-dominant eyes (top). The minimum drift amplitude was similar between participants (3.8 ± 0.8 arcmin) whereas the maximum amplitude varied about three times as much (12.0 ± 2.7 arcmin). e, Drift amplitudes in fellow eyes had a very strong correlation. f, Cone density and drift amplitude did not show a significant correlation in dominant or non-dominant eyes. g, The median drift amplitude had a moderate correlation with visual acuity threshold in dominant as well as non-dominant eyes. Dominant eyes are indicated by filled, non-dominant eyes by open markers.

Drift moves stimuli to higher cone density areas.

a, Five exemplary motion traces relative to CDC, PRL and PCD location on the Voronoi tessellated cone mosaic of one participant. b, All single trial motion traces of one eye shown on the corresponding cone mosaic (95 trials containing drift only). One-SD isoline areas (ISOA) are shown for all stimulus onset (blue) and offset (yellow) locations, indicating a trend of directional drift towards higher cone densities during 500 ms stimulus presentation. c, Polar histogram of all individual motion traces (n = 2739) shows the relative frequency of motion angles, θRetina, between start (coordinate center) and end of motion in retinal coordinates. The inset indicates θ sign. d, Same data as in c, where θCDC was computed relative to the line connecting drift start location and CDC, see inset. The pink quarter indicates the angular space used for the computation of the tuning ratio. e, The difference between acuity threshold and Nyquist limit showed a significant trend to be larger for stronger directionality tuning. The tuning ratio was computed as the ratio between the relative frequency of intra-participant drift motion towards the CDC (± 45 deg) and the average of drift motion towards the remaining 3 quadrants. f, Relative frequency of drift direction relative to CDC (top), PRL (middle) and PCD (bottom), respectively. g, Across all participants and trials, drift amplitude correlated with stimulus onset distance from CDC. There was no clear effect of stimulus onset distance on motion directionality (data color corresponding to θCDC). h, The achieved sampling gain due to the performed drift motion is significantly correlated to the potential sampling gain in individuals. In both dominant and non-dominant eyes the potential sampling gain is on average exploited by 30 %, respectively.