Assessing visual abilities was already important in historic times (Bohigian, 2008), and the precise measurement of visual acuity, our ability to resolve fine spatial detail by eye, has great importance for many real-life scenarios and is up to this day the primary diagnostic tool to determine visual function in a clinical and optometric setting. Quite surprisingly, the widely-believed assumption that the packing density and arrangement of retinal photoreceptors at the foveal center set the limit to this ability has never been experimentally confirmed.

Fovealization, the morphological and functional specialization of the cellular architecture of the light-sensitive retina optimizes the human eye for high-acuity daytime vision (Caves et al., 2018; Tuten and Harmening, 2021). Within the central one-degree diameter of the fovea, termed foveola, postreceptoral neurons are displaced centrifugally and the area is free of potentially shadowing blood vessels and glia cells (Hendrickson and Yuodelis, 1984; Syrbe et al., 2018). The outer segments of foveolar cone photoreceptors are maximally thinned and densely packed for peak spatial sampling (Hirsch and Curcio, 1989; Rossi and Roorda, 2010; Williams and Coletta, 1987), which at the same time makes these cells the most difficult to study ex vivo (Curcio et al., 1987) as well as in vivo (Rossi et al., 2011). Each foveolar cone synapses to one ON- and one OFF-midget bipolar cell, which in turn synapse exclusively upon single ON- and OFF-midget ganglion cells, a circuit that is adult-like before birth (Zhang et al., 2020). This establishes an undisturbed private line from individual foveal receptors to central processing stages.

Based on indirect comparisons between histological and psychophysical data, the hypothesis that cone spacing imposes the fundamental limit for visual resolution has been put forward (Curcio et al., 1990; Rossi and Roorda, 2010). It is well established that cone spacing, especially in the central fovea, is highly variable between individuals (Cava et al., 2020; Curcio et al., 1990; Reiniger et al., 2021; Wang et al., 2019), making general comparisons between acuity measurements and foveolar density estimated from histological samples susceptible to error. One of the main reasons why the hypothesis lacks direct experimental proof is that because, under natural viewing conditions, both visual resolution and experimental access to foveal photoreceptors are blurred by the imperfect optics of the human eye (Campbell and Green, 1965; Marcos et al., 2008). Here, we have overcome the optical barrier of the human eye by employing adaptive optics cell-resolved in vivo retinal imaging in conjunction with micro-psychophysics to study directly whether the individual’s mosaic of foveolar cones determines visual performance in a high-acuity resolution task.

While acuity is assumed to be mainly limited by the resolving capacity of the eye’s optics and retinal mosaic, it is well established that, for different visual tasks, performance thresholds can be substantially lower than the sampling grain of photoreceptors. This phenomenon has been termed hyperacuity (Westheimer, 1975) and depends on the neural visual system’s ability to extract subtle differences within the spatial patterns of the optical image on the retina (Westheimer, 2012). Thus, the visual system already incorporates mechanisms to detect relative spatial offsets an order of magnitude smaller than the spatial granularity of the retina. To make use of those fine distinctions in a resolution task, the neuronal system needs to go beyond purely spatial coding of incoming signals.

Unlike a camera, the visual system depends on temporal transients arising in the receptor’s cellular signals. Neurons in the retina, thalamus, and later stages of the visual pathways respond strongly to temporal changes (Kaplan and Benardete, 2001; Nagano, 1980). Thus, the fovealized retinal architecture in humans is accompanied by a dynamic sampling behavior that, by quick and precise movements of the eye, brings retinal images of objects of interest to land in the foveola (Ko et al., 2010). Even during steady fixation, for example of a distant face or a single letter of this text, incessant fixational eye movements slide tens to hundreds of foveolar photoreceptors across the retinal image, thereby introducing temporal modulations that translate spatial activation patterns into the temporal domain (Kuang et al., 2012). Small and rapid gaze shifts known as microsaccades relocate the gaze within the foveola during periods of fixation (Ko et al., 2010), and between microsaccades, the eyes perform a more continuous, seemingly random motion termed fixational drift (Intoy and Rucci, 2020; Krauskopf et al., 1960). Computational work suggested that fixational eye motion would introduce noise and thus impair visual acuity (Burak et al., 2010; Pitkow et al., 2007). Contrarily, recent studies on human psychophysics demonstrated fixational eye motion to be beneficial for fine spatial vision (Intoy and Rucci, 2020; Rolfs, 2009; Rucci et al., 2007). Especially drift motion has been increasingly argued to not just be randomly refreshing neural activity, but rather structuring it (Clark et al., 2022; Hafed et al., 2021; Intoy and Rucci, 2020) and being under central control (Herrmann et al., 2017).

The incessant motion of the eye conveys fine spatiotemporal detail that requires deciphering of continuously changing photoreceptor signals, which are linked by the geometry of the photoreceptor array and by how the eye moves. For instance, luminance modulation in individual cones will scale with drift length. Larger luminance variations on single receptors also yields more neuronal activity within the range of temporal frequencies parvocellular ganglion cells are sensitive to. Selective spatial frequencies can thus be amplified by varying drift lengths (Intoy and Rucci, 2020). While the neuronal mechanisms that generate fixational drift are still not fully understood (Ben-Shushan et al., 2022), its consequence to visual perception has been demonstrated. Drift was shown to improve visual performance in resolution tasks (Intoy and Rucci, 2020; Ratnam et al., 2017; Rucci et al., 2007). Indeed, considerable differences in ocular drift between individuals exist (Cherici et al., 2012; Clark et al., 2022), and subjects exhibiting less drift were shown to have better acuity (Clark et al., 2022). If such differences are a consequence of an active, adaptive mechanism, however, and how drift behavior is related to the photoreceptors that sample the retinal image is unknown.

The direct experimental access to the foveolar center, when other limiting factors like image blur or retinal motion are taken out of the equation or can be precisely measured, will allow to confirm or reject the long-standing hypothesis about the individual limits of vision. This will help to understand the fundamental physiological limitations of the visual system and will have important implications for clinical studies of retinal health.

By using synchronous adaptive optics imaging and visual stimulation of the foveola, we find that the human visual system is capable of resolving spatial orientation of E optotypes smaller than a single photoreceptor diameter and uncover a fixational eye motor behavior that optimizes retinal sampling in accordance with the individual photoreceptor mosaic.

Spatial vision, and in particular visual acuity, is the most tested and used performance metric with a close relation to everyday vision. It provides the main behavioral outcome for clinical studies of vision. Measured in daily routine or clinical studies, the best corrected visual acuity of young and healthy adults is usually between 20/20 and 20/12.5 (60 and 37.5 arcsec) (Reiniger et al., 2019; Rossi et al., 2007). Even if lower order aberrations are corrected by e.g., glasses or contact lenses, higher order aberrations inherently blur the retinal image, depending on their magnitude (Reiniger et al., 2019). Adaptive optics induce a close-to-diffraction limited optical correction, where the optical improvement is significantly correlated with an increase in visual acuity thresholds (Marcos et al., 2008). By correcting aberrations with AOSLO, we measured Snellen-E thresholds that were up to half the size (between 20/10 and 20/6.9; 30–20.6 arcsec) compared to the natural viewing condition. This is slightly lower than previously presented data (Rossi et al., 2007), very likely because of the different wavelengths used for experimentation (Figure 2c). It might be surprising to learn that the neural machinery of human vision is able to resolve such tiny stimuli, because natural viewing is blurred by the eye’s optics. Even though observers are, to some degree, adapted to their own aberrations (Artal et al., 2004), the best subjective image quality is seen when on average 88% of the aberrations are corrected (Chen et al., 2007). This may indicate that, under normal viewing conditions, optical aberrations and not cone topography may play the dominant role in limiting the eye’s acuity.

By removing most aberrations in our experiments, we can study in how far resolution thresholds are linked to or limited by the optimized but at the same time individual morphology of the human foveola. While in the periphery, midget retinal ganglion cell sampling dominates resolution, resolution of the foveal center was estimated to be governed by the cone sampling limit (Rossi and Roorda, 2010; Williams, 1985). By first-time direct experimental validation in the same participants, we here confirm the hypothesis that the individual spacing of cones can predict the resolution capacity of our foveola when optical influences are bypassed (Figure 2). We found that the individual spatial arrangement of cones was highly correlated to the visual acuity of participants and explains up to 45% of its variance (Figure 2d). Eyes with higher foveolar sampling capacity reached lower thresholds than eyes with less densely packed cone photoreceptors. Moreover, all participants reached resolution thresholds that exceeded the static Nyquist sampling limit when tested with near-infrared, 788 nm light. Natural vision is comprised of multiwavelength stimuli, thus, using 788 nm in isolation is at the top end of our retinal sensitivity. In the first part of our study, participants also performed experiments with 840 nm light. Thresholds were rather approximating the Nyquist limit with this longer near-infrared wavelength (Figure 2c). The L- and M-cone photopigment absorbance for 840 nm is about 1.4 log unit lower than for 788 nm (Stockman and Rider, 2023). The decreased cone sensitivity combined with a larger Airy-Disk size of about 7% are likely to be detrimental for the longer, 840 nm, wavelength. We would expect a potential for even lower thresholds for shorter wavelengths.

Otherwise, a potential for lower thresholds is only expected in eyes with higher angular cone densities. Perhaps contrary at first sight, this could potentially be the case for observers with higher myopia. Myopic eyes, despite retinal stretching, generally have a higher angular sampling density in and around the foveola, compared to emmetropes (Wang et al., 2019). Therefore, we would expect acuity thresholds to be lower for myopic participants, in the case that (a) angular cone density is increased like previously suggested and (b) AO correction and display resolution are still sufficient to completely resolve the foveolar cone mosaic. Psychophysical data for more participants with higher myopia and longer axial lengths would be needed to verify this assumption.

Theoretical predictions of the Nyquist resolution limit imply stationary sampling. If the retinal image is under-sampled, aliasing occurs at the frequency of the receptor mosaic, which may obscure the original image, especially its orientation (compare example snapshots in Figure 1f). While prior knowledge of the stimulus has been shown to theoretically help to de-alias under-sampled signals even in a static condition (Ruderman and Bialek, 1992), we believe that retinal image motion plays a significant role in deciphering orientation at the limits of spatial sampling. Fixational eye movements continuously modulate the luminance flow on individual cones and postreceptoral neuronal activity. Drift motion has long been presumed as a random jitter, a result of the limited precision of the oculomotor system (Cornsweet, 1956; Ditchburn and Ginsborg, 1953). More recent work revealed that drift motion is neither random (Rucci and Poletti, 2015) nor detrimental due to the introduction of noise (Burak et al., 2010; Pitkow et al., 2007), but rather a fine-tuned motion, beneficial for psychophysical measures of visual acuity in the parafovea (Ratnam et al., 2017) as well as foveola (Intoy and Rucci, 2020). As also observed in other sensory organs (Ahissar and Arieli, 2001), neurons in the visual system are strongly selective not just for spatial patterns, but also for temporally changing stimuli (Ahissar and Arieli, 2012), a finding that is also supported by computational modeling, suggesting that the visual system may utilize principles comparable to those used in computational imaging for achieving super-resolution via camera motion (Anderson et al., 2020). Within the past decades, the interdisciplinary term ‘geometrical super-resolution’ which is devoted to the filtering properties of sensor systems has become common (Zalevsky, 2011). These resolution advantages may be achieved in the visual system by incorporating mechanisms that allow for the recognition of positional differences smaller than a single cell. That such mechanism exists is exemplified in a phenomenon known as hyperacuity. Fine localization discriminations of only a few seconds of arc are performed by identification of the centroid of the retinal light distributions (Westheimer and McKee, 1977) of the involved pattern components. In a diffraction-limited resolution task, the visual system seems to be able to translate the temporal luminance modulation in individual photoreceptors by ocular drift to additional spatial information about the stimulus position and shape. Contrary, the indirect suppression of natural fixational eye motion by retinal stabilization techniques impairs visual acuity outside the foveolar center (Intoy and Rucci, 2020; Rucci et al., 2007). For prolonged static stimulus presentations, retinal spiking decays over time, while drift motion keeps the luminance change active, continuously refreshes the receptive field input, and sustains neuronal activity (Kuang et al., 2012).

We found a significant correlation between drift motion and visual acuity thresholds between individuals, indicating that drift motion may be one of the key elements in reaching sub-cone resolution thresholds. Interestingly, acuity improved for smaller fixational drift and decreased in participants who exhibited larger drift motion, on average. The fact that less drift is beneficial to reach the lowest possible acuity thresholds reflects the characteristics of spatiotemporal luminance changes introduced by smaller or larger drift motion. Smaller drifts induce luminance changes with higher spatial frequencies and models of retinal ganglion cell activity suggest a higher contrast sensitivity for high spatial frequency motion and less for low spatial frequencies compared to a static retina (Kuang et al., 2012; Rucci and Victor, 2015). This is supported by other recent work which also showed that visual acuity thresholds can even be predicted from drift magnitudes measured in a sustained fixation task (Clark et al., 2022).

There is evidence that fixational eye motion might have systematic components in primates. A previous study in macaque monkeys revealed a systematic directional drift response only a few dozens of milliseconds after various visual transients (Malevich et al., 2020). In our study, we reveal that a certain drift directionality can not only be triggered by particular visual transients, but that human observers are capable of adapting their drift direction to enact an oculomotor strategy that takes advantage of the maximum resolution capacity provided within the retina. Our participants precisely moved their eyes to have the stimulus slip across the most densely packed cone cells within their foveola. We hereby shed light on a mechanism that is potentially particularly active during fine discrimination tasks. This confirms that drift can be quickly adjusted in a continuous closed-loop control (Gruber and Ahissar, 2020), while, as other recent work suggests, being at the same time able to quickly switch to an open-loop process, as specific task knowledge influences the dominant orientation of drift, even in the sudden absence of visual information (Lin et al., 2023). Yet, the underlying neuronal control of drift motion remains not fully understood. Recent work suggested, based on brainstem recordings in rhesus monkeys, that the origin can be found mostly upstream of the ocular motoneurons. It can likely be explained as diffusion in the oculomotor integrator which is mainly driven by noise, but additionally affected by mechanisms within the visual motor pathway (e.g. feedback mechanisms) (Ben-Shushan et al., 2022). An incorporation of a visual feedback loop to that model was shown to modulate the statistics of eye motion, given a time lag of about 100 ms (mainly due to synaptic processing delays, of order 60–80ms Malevich et al., 2020). This fits our results well. Our presentation time of 500 ms sufficed for a modulation of the fixational drift motion towards retinal areas of higher cone sampling (also see Figure 4—figure supplements 1–3). Our data supports the view that some aspects of the statistics of drift motion can be influenced by the visual task (Ben-Shushan et al., 2022; Intoy and Rucci, 2020; Malevich et al., 2020; Zhao et al., 2023). The superior colliculus seems to play a major role in modulating drift motion in a feedback loop to visual inputs (Hafed et al., 2021). It’s not only involved in controlling large eye motions (Bergeron et al., 2003) and microsaccades (Hafed et al., 2009), but also reflects neural responses to fixational drift that are likely a result of sensory input (C.-Y. Chen et al., 2019).

So, even though the CDC is displaced from the PRL in a way to be beneficial for natural binocular vision (Reiniger et al., 2021), constant visual feedback allows to adapt the drift direction and, therefore, also the task-related PRL. Commonly, the term PRL is used for describing the retinal location that is preferably used in fixational tasks. It is still a matter of debate what factors drive the development of this very reproducible (Reiniger et al., 2021) retinal location and in how far it might provide enhanced visual function. Sensitivity to small light spots in the foveola seems to be rather plateau-like and not particularly pronounced at the PRL (Domdei et al., 2021). As recently shown, the PRL slightly differs between different tasks but has a larger interindividual variability (Bowers et al., 2021). The here shown results indicate that also when measuring visual resolution, the PRL is not necessarily the center of the sampling drift motion. The directional drift motion leads to a shift of the preferred retinal location for a resolution task towards the CDC (Figure 4—figure supplement 3 and Video 3). Previous work that compared active versus passive fixation did not show a systematic offset in a similar experimental setup. However, 5 out of 8 participants also shifted their PRL in a Snellen E task closer to the CDC compared to the PRL for fixating a static disk stimulus (Bowers et al., 2021), the conditions that are best comparable to our study. The main difference to our visual acuity experiments was that automatically paced random time intervals between presentations (0.5–1.5 s) were applied to not allow the participants to anticipate the next trial whereas in our study, participants self-paced the stimulus output to be able to prepare and focus for the next trial. It might be that this extremely fine-tuned usage of the visual feedback loop can only be kept active for rather short time intervals. By shifting the stimulus towards the CDC in 50% of cases, the potential sampling gain within individual eyes was exploited by 30%, on average, which goes along with a cone density increase of 3% or 285 cones/deg². Even though this increase in cone density alone would not account for the difference between acuity thresholds and the Nyquist limit, this and the simultaneous spatiotemporal luminance modulation contribute to achieving sub-cone visual acuity thresholds.

Between fellow eyes, we found very strong correlations for all the measured parameters. While drift lengths and directionality, as well as cone densities are very symmetric between dominant and non-dominant eyes (Figures 2e and 3e), significantly lower acuity thresholds of 1.5 arcsec, on average, were observed in the dominant eyes of participants (Figure 2e). The dominant eyes’ visual input has a tendency to be preferred during binocular viewing, but has not been shown to exhibit relevant differences in visual function in healthy eyes with low refractive errors (Ehrenstein et al., 2005; Zhou et al., 2017). Partially this may be due to limited accuracy in the mainly used clinical methods (e.g. Snellen Chart or projection have ~10 arcsec steps between optotype rows). This very fine binocular difference between eyes emphasizes that some remaining factors which especially comprise the neural postprocessing steps, also play an important role and may facilitate the slight functional advantage of dominant eyes.

For clinical studies of retinal health and in new therapeutical approaches, photoreceptor health and visual acuity can be related to other more standard clinical measures as OCT-derived measures of outer segment length or retinal thickness which have been shown to serve for estimates of cone density (Domdei et al., 2023). Therefore, building a larger dataset on photoreceptor-resolved foveolar maps and associated visual function measures may help to, on the one hand, better understand the interplay between structural and functional changes to draw conclusions about disease progression, intervention efficiency, or the interpretation of retinal imaging data in studies aimed at vision restoration. On the other hand, a detailed examination of psychophysical measures with knowledge about the exact neural sampling characteristics offers a great potential to answer further questions about e.g., resolution limits in myopia, the effect of image stabilization in the very center of the foveola, or implications for binocular viewing that could previously only be hypothesized. The awareness of the oculomotor system being able to finely adjust the drift motion behavior for a particular task may guide future interpretation of fixational eye motion.

The following data are publicly available for download at Mendeley Data: 1. All original cone mosaic images and cone coordinates. 2. Retinal locations of CDC, PRL and PCD. 3. Fixational eye motion trajectories. 4. MATLAB code that can be used to plot the data on the original image.

1. 1. Witten JL
   2. Lukyanova V
   3. Harmening W

   (2024) Mendeley Data

   Data from: Sub-cone visual resolution by active, adaptive sampling in the human foveola.

   https://doi.org/10.17632/zp6d5w8kdv.1

1. 1. Ahissar E
   2. Arieli A

   (2001) Figuring space by time

   Neuron 32:185–201.

   https://doi.org/10.1016/s0896-6273(01)00466-4

   • PubMed
   • Google Scholar
2. 1. Ahissar E
   2. Arieli A

   (2012) Seeing via miniature eye movements: a dynamic hypothesis for vision

   Frontiers in Computational Neuroscience 6:89.

   https://doi.org/10.3389/fncom.2012.00089

   • PubMed
   • Google Scholar
3. 1. Anderson AG
   2. Ratnam K
   3. Roorda A
   4. Olshausen BA

   (2020) High-acuity vision from retinal image motion

   Journal of Vision 20:34.

   https://doi.org/10.1167/jov.20.7.34

   • PubMed
   • Google Scholar
4. 1. Artal P
   2. Chen L
   3. Fernández EJ
   4. Singer B
   5. Manzanera S
   6. Williams DR

   (2004) Neural compensation for the eye’s optical aberrations

   Journal of Vision 4:281–287.

   https://doi.org/10.1167/4.4.4

   • PubMed
   • Google Scholar
5. 1. Ben-Shushan N
   2. Shaham N
   3. Joshua M
   4. Burak Y

   (2022) Fixational drift is driven by diffusive dynamics in central neural circuitry

   Nature Communications 13:1697.

   https://doi.org/10.1038/s41467-022-29201-y

   • PubMed
   • Google Scholar
6. 1. Bergeron A
   2. Matsuo S
   3. Guitton D

   (2003) Superior colliculus encodes distance to target, not saccade amplitude, in multi-step gaze shifts

   Nature Neuroscience 6:404–413.

   https://doi.org/10.1038/nn1027

   • Google Scholar
7. 1. Bohigian GM

   (2008) An ancient eye test--using the stars

   Survey of Ophthalmology 53:536–539.

   https://doi.org/10.1016/j.survophthal.2008.06.009

   • PubMed
   • Google Scholar
8. 1. Bowers NR
   2. Gautier J
   3. Lin S
   4. Roorda A

   (2021) Fixational eye movements in passive versus active sustained fixation tasks

   Journal of Vision 21:1–16.

   https://doi.org/10.1167/jov.21.11.16

   • Google Scholar
9. 1. Brainard DH

   (1997) The psychophysics toolbox

   Spatial Vision 10:433–436.

   https://doi.org/10.1163/156856897X00357

   • PubMed
   • Google Scholar
10. 1. Burak Y
    2. Rokni U
    3. Meister M
    4. Sompolinsky H

    (2010) Bayesian model of dynamic image stabilization in the visual system

    PNAS 107:19525–19530.

    https://doi.org/10.1073/pnas.1006076107

    • Google Scholar
11. 1. Campbell FW
    2. Green DG

    (1965) Optical and retinal factors affecting visual resolution

    The Journal of Physiology 181:576–593.

    https://doi.org/10.1113/jphysiol.1965.sp007784

    • PubMed
    • Google Scholar
12. 1. Cava JA
    2. Allphin MT
    3. Mastey RR
    4. Gaffney M
    5. Linderman RE
    6. Cooper RF
    7. Carroll J

    (2020) Assessing interocular symmetry of the foveal cone mosaic

    Investigative Ophthalmology & Visual Science 61:23.

    https://doi.org/10.1167/iovs.61.14.23

    • PubMed
    • Google Scholar
13. 1. Caves EM
    2. Brandley NC
    3. Johnsen S

    (2018) Visual acuity and the evolution of signals

    Trends in Ecology & Evolution 33:358–372.

    https://doi.org/10.1016/j.tree.2018.03.001

    • Google Scholar
14. 1. Chen L
    2. Artal P
    3. Gutierrez D
    4. Williams DR

    (2007) Neural compensation for the best aberration correction

    Journal of Vision 7:9.

    https://doi.org/10.1167/7.10.9

    • PubMed
    • Google Scholar
15. 1. Chen CY
    2. Hoffmann KP
    3. Distler C
    4. Hafed ZM

    (2019) The foveal visual representation of the primate superior colliculus

    Current Biology 29:2109–2119.

    https://doi.org/10.1016/j.cub.2019.05.040

    • PubMed
    • Google Scholar
16. 1. Cherici C
    2. Kuang X
    3. Poletti M
    4. Rucci M

    (2012) Precision of sustained fixation in trained and untrained observers

    Journal of Vision 12:1–16.

    https://doi.org/10.1167/12.6.31

    • PubMed
    • Google Scholar
17. 1. Clark AM
    2. Intoy J
    3. Rucci M
    4. Poletti M

    (2022) Eye drift during fixation predicts visual acuity

    PNAS 119:e2200256119.

    https://doi.org/10.1073/pnas.2200256119

    • PubMed
    • Google Scholar
18. 1. Cornsweet TN

    (1956) Determination of the stimuli for involuntary drifts and saccadic eye movements

    Journal of the Optical Society of America 46:987–993.

    https://doi.org/10.1364/josa.46.000987

    • PubMed
    • Google Scholar
19. 1. Cunefare D
    2. Fang L
    3. Cooper RF
    4. Dubra A
    5. Carroll J
    6. Farsiu S

    (2017) Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks

    Scientific Reports 7:6620.

    https://doi.org/10.1038/s41598-017-07103-0

    • PubMed
    • Google Scholar
20. 1. Curcio CA
    2. Packer O
    3. Kalina RE

    (1987) A whole mount method for sequential analysis of photoreceptor and ganglion cell topography in a single retina

    Vision Research 27:9–15.

    https://doi.org/10.1016/0042-6989(87)90137-4

    • PubMed
    • Google Scholar
21. 1. Curcio CA
    2. Sloan KR
    3. Kalina RE
    4. Hendrickson AE

    (1990) Human photoreceptor topography

    The Journal of Comparative Neurology 292:497–523.

    https://doi.org/10.1002/cne.902920402

    • PubMed
    • Google Scholar
22. 1. Ditchburn RW
    2. Ginsborg BL

    (1953) Involuntary eye movements during fixation

    The Journal of Physiology 119:1–17.

    https://doi.org/10.1113/jphysiol.1953.sp004824

    • PubMed
    • Google Scholar
23. 1. Domdei N
    2. Reiniger JL
    3. Holz FG
    4. Harmening WM

    (2021) The relationship between visual sensitivity and eccentricity, cone density and outer segment length in the human foveola

    Investigative Ophthalmology & Visual Science 62:31.

    https://doi.org/10.1167/iovs.62.9.31

    • PubMed
    • Google Scholar
24. 1. Domdei N
    2. Ameln J
    3. Gutnikov A
    4. Witten JL
    5. Holz FG
    6. Wahl S
    7. Harmening WM

    (2023) cone density is correlated to outer segment length and retinal thickness in the human foveola

    Investigative Ophthalmology & Visual Science 64:11.

    https://doi.org/10.1167/iovs.64.15.11

    • PubMed
    • Google Scholar
25. 1. Ehrenstein WH
    2. Arnold-Schulz-Gahmen BE
    3. Jaschinski W

    (2005) Eye preference within the context of binocular functions

    Graefe’s Archive for Clinical and Experimental Ophthalmology 243:926–932.

    https://doi.org/10.1007/s00417-005-1128-7

    • Google Scholar
26. 1. Gruber LZ
    2. Ahissar E

    (2020) Closed loop motor-sensory dynamics in human vision

    PLOS ONE 15:e0240660.

    https://doi.org/10.1371/journal.pone.0240660

    • PubMed
    • Google Scholar
27. 1. Hafed ZM
    2. Goffart L
    3. Krauzlis RJ

    (2009) A neural mechanism for microsaccade generation in the primate superior colliculus

    Science 323:940–943.

    https://doi.org/10.1126/science.1166112

    • PubMed
    • Google Scholar
28. 1. Hafed ZM
    2. Chen CY
    3. Tian X
    4. Baumann MP
    5. Zhang T

    (2021) Active vision at the foveal scale in the primate superior colliculus

    Journal of Neurophysiology 125:1121–1138.

    https://doi.org/10.1152/jn.00724.2020

    • PubMed
    • Google Scholar
29. 1. Hendrickson AE
    2. Yuodelis C

    (1984) The morphological development of the human fovea

    Ophthalmology 91:603–612.

    https://doi.org/10.1016/s0161-6420(84)34247-6

    • PubMed
    • Google Scholar
30. 1. Herrmann CJJ
    2. Metzler R
    3. Engbert R

    (2017) A self-avoiding walk with neural delays as a model of fixational eye movements

    Scientific Reports 7:12958.

    https://doi.org/10.1038/s41598-017-13489-8

    • PubMed
    • Google Scholar
31. 1. Hirsch J
    2. Curcio CA

    (1989) The spatial resolution capacity of human foveal retina

    Vision Research 29:1095–1101.

    https://doi.org/10.1016/0042-6989(89)90058-8

    • PubMed
    • Google Scholar
32. 1. Hofmann J
    2. Domdei L
    3. Jainta S
    4. Harmening WM

    (2022) Assessment of binocular fixational eye movements including cyclotorsion with split-field binocular scanning laser ophthalmoscopy

    Journal of Vision 22:5.

    https://doi.org/10.1167/jov.22.10.5

    • PubMed
    • Google Scholar
33. 1. Intoy J
    2. Rucci M

    (2020) Finely tuned eye movements enhance visual acuity

    Nature Communications 11:795.

    https://doi.org/10.1038/s41467-020-14616-2

    • PubMed
    • Google Scholar
34. 1. Intoy J
    2. Mostofi N
    3. Rucci M

    (2021) Fast and nonuniform dynamics of perisaccadic vision in the central fovea

    PNAS 118:e2101259118.

    https://doi.org/10.1073/pnas.2101259118

    • PubMed
    • Google Scholar
35. 1. Kaplan E
    2. Benardete E

    (2001) The dynamics of primate retinal ganglion cells

    Progress in Brain Research 134:17–34.

    https://doi.org/10.1016/s0079-6123(01)34003-7

    • PubMed
    • Google Scholar
36. 1. Ko HK
    2. Poletti M
    3. Rucci M

    (2010) Microsaccades precisely relocate gaze in a high visual acuity task

    Nature Neuroscience 13:1549–1553.

    https://doi.org/10.1038/nn.2663

    • PubMed
    • Google Scholar
37. 1. Krauskopf J
    2. Cornsweet TN
    3. Riggs LA

    (1960) Analysis of eye movements during monocular and binocular fixation

    Journal of the Optical Society of America 50:572–578.

    https://doi.org/10.1364/josa.50.000572

    • PubMed
    • Google Scholar
38. 1. Kuang X
    2. Poletti M
    3. Victor JD
    4. Rucci M

    (2012) Temporal encoding of spatial information during active visual fixation

    Current Biology 22:510–514.

    https://doi.org/10.1016/j.cub.2012.01.050

    • PubMed
    • Google Scholar
39. 1. Lin YC
    2. Intoy J
    3. Clark AM
    4. Rucci M
    5. Victor JD

    (2023) Cognitive influences on fixational eye movements

    Current Biology 33:1606–1612.

    https://doi.org/10.1016/j.cub.2023.03.026

    • PubMed
    • Google Scholar
40. 1. Malevich T
    2. Buonocore A
    3. Hafed ZM

    (2020) Rapid stimulus-driven modulation of slow ocular position drifts

    eLife 9:e57595.

    https://doi.org/10.7554/eLife.57595

    • PubMed
    • Google Scholar
41. 1. Marcos S
    2. Sawides L
    3. Gambra E
    4. Dorronsoro C

    (2008) Influence of adaptive-optics ocular aberration correction on visual acuity at different luminances and contrast polarities

    Journal of Vision 8:1.

    https://doi.org/10.1167/8.13.1

    • PubMed
    • Google Scholar
42. 1. McAnany JJ

    (2014) The effect of exposure duration on visual acuity for letter optotypes and gratings

    Vision Research 105:86–91.

    https://doi.org/10.1016/j.visres.2014.08.024

    • PubMed
    • Google Scholar
43. 1. Nagano T

    (1980) Temporal sensitivity of the human visual system to sinusoidal gratings

    Journal of the Optical Society of America 70:711–716.

    https://doi.org/10.1364/josa.70.000711

    • PubMed
    • Google Scholar
44. 1. Pelli DG

    (1997)

    The videotoolbox software for visual psychophysics: transforming numbers into movies

    Spatial Vision 10:437–442.

    • PubMed
    • Google Scholar
45. 1. Pitkow X
    2. Sompolinsky H
    3. Meister M

    (2007) A neural computation for visual acuity in the presence of eye movements

    PLOS Biology 5:e331.

    https://doi.org/10.1371/journal.pbio.0050331

    • PubMed
    • Google Scholar
46. 1. Poonja S
    2. Patel S
    3. Henry L
    4. Roorda A

    (2005) Dynamic visual stimulus presentation in an adaptive optics scanning laser ophthalmoscope

    Journal of Refractive Surgery 21:S575–S580.

    https://doi.org/10.3928/1081-597X-20050901-30

    • PubMed
    • Google Scholar
47. 1. Putnam NM
    2. Hofer HJ
    3. Doble N
    4. Chen L
    5. Carroll J
    6. Williams DR

    (2005) The locus of fixation and the foveal cone mosaic

    Journal of Vision 5:632–639.

    https://doi.org/10.1167/5.7.3

    • PubMed
    • Google Scholar
48. 1. Ratnam K
    2. Domdei N
    3. Harmening WM
    4. Roorda A

    (2017) Benefits of retinal image motion at the limits of spatial vision

    Journal of Vision 17:30.

    https://doi.org/10.1167/17.1.30

    • PubMed
    • Google Scholar
49. 1. Reiniger JL
    2. Lobecke AC
    3. Sabesan R
    4. Bach M
    5. Verbakel F
    6. de Brabander J
    7. Holz FG
    8. Berendschot T
    9. Harmening WM

    (2019) Habitual higher order aberrations affect landolt but not vernier acuity

    Journal of Vision 19:11.

    https://doi.org/10.1167/19.5.11

    • PubMed
    • Google Scholar
50. 1. Reiniger JL
    2. Domdei N
    3. Holz FG
    4. Harmening WM

    (2021) Human gaze is systematically offset from the center of cone topography

    Current Biology 31:4188–4193.

    https://doi.org/10.1016/j.cub.2021.07.005

    • PubMed
    • Google Scholar
51. 1. Rolfs M

    (2009) Microsaccades: small steps on a long way

    Vision Research 49:2415–2441.

    https://doi.org/10.1016/j.visres.2009.08.010

    • Google Scholar
52. 1. Roorda A
    2. Romero-Borja F
    3. Donnelly Iii W
    4. Queener H
    5. Hebert T
    6. Campbell M

    (2002) Adaptive optics scanning laser ophthalmoscopy

    Optics Express 10:405–412.

    https://doi.org/10.1364/oe.10.000405

    • PubMed
    • Google Scholar
53. 1. Rossi EA
    2. Weiser P
    3. Tarrant J
    4. Roorda A

    (2007) Visual performance in emmetropia and low myopia after correction of high-order aberrations

    Journal of Vision 7:14.

    https://doi.org/10.1167/7.8.14

    • PubMed
    • Google Scholar
54. 1. Rossi EA
    2. Roorda A

    (2010) The relationship between visual resolution and cone spacing in the human fovea

    Nature Neuroscience 13:156–157.

    https://doi.org/10.1038/nn.2465

    • PubMed
    • Google Scholar
55. 1. Rossi EA
    2. Chung M
    3. Dubra A
    4. Hunter JJ
    5. Merigan WH
    6. Williams DR

    (2011) Imaging retinal mosaics in the living eye

    Eye 25:301–308.

    https://doi.org/10.1038/eye.2010.221

    • PubMed
    • Google Scholar
56. 1. Rucci M
    2. Iovin R
    3. Poletti M
    4. Santini F

    (2007) Miniature eye movements enhance fine spatial detail

    Nature 447:851–854.

    https://doi.org/10.1038/nature05866

    • PubMed
    • Google Scholar
57. 1. Rucci M
    2. Poletti M

    (2015) Control and functions of fixational eye movements

    Annual Review of Vision Science 1:499–518.

    https://doi.org/10.1146/annurev-vision-082114-035742

    • PubMed
    • Google Scholar
58. 1. Rucci M
    2. Victor JD

    (2015) The unsteady eye: an information-processing stage, not a bug

    Trends in Neurosciences 38:195–206.

    https://doi.org/10.1016/j.tins.2015.01.005

    • PubMed
    • Google Scholar
59. 1. Ruderman DL
    2. Bialek W

    (1992) Seeing beyond the nyquist limit

    Neural Computation 4:682–690.

    https://doi.org/10.1162/neco.1992.4.5.682

    • Google Scholar
60. 1. Schütt HH
    2. Harmeling S
    3. Macke JH
    4. Wichmann FA

    (2016) Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

    Vision Research 122:105–123.

    https://doi.org/10.1016/j.visres.2016.02.002

    • PubMed
    • Google Scholar
61. 1. Stevenson SB
    2. Roorda A

    (2005) Correcting for miniature eye movements in high-resolution scanning laser ophthalmoscopy

    Ophthalmic Technologies XV 1:145–151.

    https://doi.org/10.1117/12.591190

    • Google Scholar
62. 1. Stockman A
    2. Rider AT

    (2023) Formulae for generating standard and individual human cone spectral sensitivities

    Color Research and Application 48:818–840.

    https://doi.org/10.1002/col.22879

    • PubMed
    • Google Scholar
63. 1. Syrbe S
    2. Kuhrt H
    3. Gärtner U
    4. Habermann G
    5. Wiedemann P
    6. Bringmann A
    7. Reichenbach A

    (2018) Müller glial cells of the primate foveola: an electron microscopical study

    Experimental Eye Research 167:110–117.

    https://doi.org/10.1016/j.exer.2017.12.004

    • PubMed
    • Google Scholar
64. 1. Tuten WS
    2. Harmening WM

    (2021) Foveal vision

    Current Biology 31:R701–R703.

    https://doi.org/10.1016/j.cub.2021.03.097

    • PubMed
    • Google Scholar
65. 1. Wang Y
    2. Bensaid N
    3. Tiruveedhula P
    4. Ma J
    5. Ravikumar S
    6. Roorda A

    (2019) Human foveal cone photoreceptor topography and its dependence on eye length

    eLife 8:e47148.

    https://doi.org/10.7554/eLife.47148

    • PubMed
    • Google Scholar
66. 1. Watson AB
    2. Pelli DG

    (1983) QUEST: a bayesian adaptive psychometric method

    Perception & Psychophysics 33:113–120.

    https://doi.org/10.3758/BF03202828

    • Google Scholar
67. 1. Wells-Gray EM
    2. Choi SS
    3. Bries A
    4. Doble N

    (2016) Variation in rod and cone density from the fovea to the mid-periphery in healthy human retinas using adaptive optics scanning laser ophthalmoscopy

    Eye 30:1135–1143.

    https://doi.org/10.1038/eye.2016.107

    • PubMed
    • Google Scholar
68. 1. Westheimer G

    (1975)

    Editorial: visual acuity and hyperacuity

    Investigative Ophthalmology 14:570–572.

    • PubMed
    • Google Scholar
69. 1. Westheimer G
    2. McKee SP

    (1977) Spatial configurations for visual hyperacuity

    Vision Research 17:941–947.

    https://doi.org/10.1016/0042-6989(77)90069-4

    • PubMed
    • Google Scholar
70. 1. Westheimer G

    (2012) Optical superresolution and visual hyperacuity

    Progress in Retinal and Eye Research 31:467–480.

    https://doi.org/10.1016/j.preteyeres.2012.05.001

    • PubMed
    • Google Scholar
71. 1. Wilk MA
    2. Dubis AM
    3. Cooper RF
    4. Summerfelt P
    5. Dubra A
    6. Carroll J

    (2017) Assessing the spatial relationship between fixation and foveal specializations

    Vision Research 132:53–61.

    https://doi.org/10.1016/j.visres.2016.05.001

    • PubMed
    • Google Scholar
72. 1. Williams DR

    (1985) Aliasing in human foveal vision

    Vision Research 25:195–205.

    https://doi.org/10.1016/0042-6989(85)90113-0

    • PubMed
    • Google Scholar
73. 1. Williams DR
    2. Coletta NJ

    (1987) Cone spacing and the visual resolution limit

    Journal of the Optical Society of America A 4:1514.

    https://doi.org/10.1364/JOSAA.4.001514

    • Google Scholar
74. 1. Wynne N
    2. Cava JA
    3. Gaffney M
    4. Heitkotter H
    5. Scheidt A
    6. Reiniger JL
    7. Grieshop J
    8. Yang K
    9. Harmening WM
    10. Cooper RF
    11. Carroll J

    (2022) Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy

    Biomedical Optics Express 13:4445–4454.

    https://doi.org/10.1364/BOE.460821

    • PubMed
    • Google Scholar
75. Book

    1. Zalevsky Z

    (2011) Exceeding the diffraction and the geometric limits of imaging systems: A review

    In: Dolev S, Oltean M, editors. Optical Supercomputing 2010. Lecture Notes in Computer Science. Springer-Verlag. pp. 119–130.

    https://doi.org/10.1007/978-3-642-22494-2_13

    • Google Scholar
76. 1. Zhang T
    2. Godara P
    3. Blanco ER
    4. Griffin RL
    5. Wang X
    6. Curcio CA
    7. Zhang Y

    (2015) Variability in human cone topography assessed by adaptive optics scanning laser ophthalmoscopy

    American Journal of Ophthalmology 160:290–300.

    https://doi.org/10.1016/j.ajo.2015.04.034

    • Google Scholar
77. 1. Zhang C
    2. Kim YJ
    3. Silverstein AR
    4. Hoshino A
    5. Reh TA
    6. Dacey DM
    7. Wong RO

    (2020) Circuit reorganization shapes the developing human foveal midget connectome toward single-cone resolution

    Neuron 108:905–918.

    https://doi.org/10.1016/j.neuron.2020.09.014

    • PubMed
    • Google Scholar
78. 1. Zhao Z
    2. Ahissar E
    3. Victor JD
    4. Rucci M

    (2023) Inferring visual space from ultra-fine extra-retinal knowledge of gaze position

    Nature Communications 14:269.

    https://doi.org/10.1038/s41467-023-35834-4

    • PubMed
    • Google Scholar
79. 1. Zhou D
    2. Ni N
    3. Ni A
    4. Chen Q
    5. Hu DN
    6. Zhou J

    (2017) Association of visual acuity with ocular dominance in 2045 myopic patients

    Current Eye Research 42:1155–1159.

    https://doi.org/10.1080/02713683.2017.1297464

    • PubMed
    • Google Scholar

Author details

1. Jenny L Witten

   Department of Ophthalmology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   jennylwitten@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7500-7053

2. Veronika Lukyanova

   Department of Ophthalmology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

   Contribution

   Resources, Software, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0008-3682-7225

3. Wolf M Harmening

   Department of Ophthalmology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7053-1198

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