{"paper_id":"058f5986-330c-45a2-926d-37ebbc980a70","body_text":"1\n1 Visual blur disrupts the kinematic and temporal aspects of reach-\n2 grasp-lift movements.\n3 William E. A. Sheppard 1,2, Carlo Campagnoli1, Richard M. Wilkie1, Rigmor C. Baraas3, and Rachel. \n4 O. Coats 1\n5 Affiliations: 1 School of Psychology, Faculty of Medicine and Health, University of Leeds; 2 Sheffield \n6 Centre for Health and Related Research, School of Medicine and Population Health, Faculty of \n7 Health, The University of Sheffield; 3 National Centre for Optics, Vision and Eye Care, Faculty of \n8 Health and Social Sciences, University College of Southeast Norway, Kongsberg, Norway\n9 Corresponding Author: William Sheppard \n10 Corresponding Author email: w.e.sheppard@sheffield.ac.uk\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n2\n11 Abstract\n12 Degraded vision (caused by pathological reasons or monocular viewing) has been shown to affect fine \n13 motor control. However, there is a dearth of work examining the effects of “cataract-like” blur on \n14 reach-to-grasp performance. There is, however, a trend towards amblyopic blur being associated with \n15 deficits in reach-to-grasp performance, suggesting that timely intervention in treating cataracts is \n16 likely to be essential to maintain a functional ageing population. 18 participants performed a reach-to-\n17 grasp task. They reached for and precision grasped high and low-contrast cuboid targets under three \n18 visual conditions: binocular blur, monocular blur (full vision in the other eye) and full vision. They \n19 also performed contrast sensitivity, stereoacuity and visual acuity tests. Visual blur was associated \n20 with changes to the kinematics of prehensile movements' early/acceleration stage (maximum \n21 acceleration and maximum velocity) and maximum grip aperture. Visual blur also caused the period \n22 from first contact with the target to the time it was lifted (dwell time) to be elongated. These results \n23 suggest that changes in prehension associated with visual blur are linked to differences in the planning \n24 and online control of prehension movements.\n25\n26\n27\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n3\n28 Introduction \n29 The ability to move one's arm and hand towards an object to grasp it (prehension) is an essential \n30 component of many everyday actions, including reaching for a door handle or picking up a mug of \n31 tea. Therefore, factors affecting the execution of these movements can directly and significantly \n32 impact one's quality of life. The spatiotemporal characteristics of prehensile movements depend on \n33 three fundamental aspects: the visual system's internal state, the target object's visual characteristics \n34 and the interaction between the two (1). To complete a prehensile movement, the individual must use \n35 their representation of these three aspects to estimate the hand and target positions (2) before planning \n36 the path between the two, as well as the final orientation of the grip (3). Once an individual has \n37 initiated the movement, they must effectively control it to its completion, which ideally requires \n38 visual and proprioceptive feedback. \n39 Given the central role of vision in planning and executing prehensile movements, it is essential to \n40 understand how degraded vision affects reaching and grasping actions. There has been extensive \n41 research demonstrating the impact of monocular occlusion on both the planning and execution (online \n42 control) of prehensile movements (4–9) and a significant number of studies investigating prehension \n43 under monocular and/or binocular visual conditions, such as in the case of amblyopia (1,10–16), age-\n44 related macular degeneration (AMD (17–21)) and glaucoma (17,20,22–24). \n45 Grant and Conway (2019) documented the effect of monocular occlusion on the spatial and temporal \n46 aspects of prehension. Specifically, monocular occlusion was associated with decreased maximum \n47 velocity (MA), increased maximum grip aperture (MGA) and longer movement time, primarily \n48 caused by an extended deceleration phase and contact-to-lift duration (dwell time). Interestingly, the \n49 effect of monocular vision on MGA and dwell time disappeared when visual feedback during the \n50 movement was removed, suggesting that monocular vision directly impacts the online control of the \n51 movement. In contrast, the aspects of the movement related to planning (time to MV and the time to \n52 MGA) were not affected by the removal of visual feedback. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n4\n53 Similar research has been conducted in anisometropic amblyopia patients, although the findings were \n54 more heterogeneous. There is consensus that amblyopia is associated with an increase in total \n55 movement time (10,11,14) and dwell time (10,11), as well as a trend towards a decrease in MV \n56 (11,14). However, as shown in Table 1, other variables, including maximum acceleration (MA), \n57 duration of the acceleration phase, MV, duration of the deceleration phase, and MGA, do not show a \n58 consistent pattern or are inconsistently reported (10,11,14).\n59\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n5\n60 Table 1. A summary of the effects of amblyopia on kinematic markers in prehensile tasks. The effects \n61 in the table are those associated with amblyopia vs. controls, i.e. “Increased” suggests that amblyopia \n62 was associated with an increase in a variable relative to controls. \nBuckley et al. \n(2015)\nGrant et al. \n(2007)\nNiechwiej-\nSzwedo et al. \n(2011)\nMovement time Increased Increased Increased\nMaximum velocity\nNo significant \neffect\nDecreased Decreased\nMaximum acceleration Not reported Not reported Decreased\nDuration of the acceleration \nphase\nNo significant \neffect\nNot reported Decreased\nMaximum deceleration Not reported Not reported\nNo significant \neffect\nDuration of the deceleration \nphase\nNot reported Not reported\nNo significant \neffect\nMGA Decreased\nNo significant \neffect\nNot reported\nDwell time Increased Increased Not reported\n63\n64 Reduced contrast between stimuli and background also appears to magnify amblyopia's effects on \n65 prehension. For example, amblyopes showed a larger increase, relative to controls, in planning time \n66 and grip aperture at contact with low contrast targets relative to the high contrast targets (1). The \n67 performance deficits demonstrated in those with amblyopia did not correlate with reduced \n68 binocularity or VA, but reduced CS was associated with increased target localisation errors (1). This \n69 is not surprising as amblyopia and visual blur have been demonstrated to degrade CS by increasing \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n6\n70 spatial sensitivity thresholds across the spectrum of spatial frequencies (25–27), with the effect being \n71 particularly strong at higher spatial frequencies associated with fine depth perception (28). \n72 At the point of writing, there is little to no research on the effects of cataracts and cataract removal \n73 surgery on prehension. According to The Royal College of Ophthalmologists 2022 report, over \n74 450,000 cataract removal procedures were carried out in England in the financial year 2019/20, an \n75 11% increase from three years earlier. To complicate matters, the medical history of cataract patients \n76 is highly variable and heterogeneous, raising concerns about the generalizability of the findings. \n77 Therefore, in the present study, we aimed to systematically assess the effects of cataract-like visual \n78 blur on grasping, which, at the time of writing, has not been directly assessed in previous research. \n79 We manipulated healthy participants' vision by artificially blurring one or both eyes' visual field, since \n80 cataracts can develop in either eye (monocularly) or both eyes (binocularly), whilst they performed a \n81 reach-to-grasp task to high and low contrast targets. We predicted that increased blur would be \n82 associated with changes in the movement's spatial and temporal aspects, particularly in the scaling of \n83 MGA, the duration of overall MT, the duration of the deceleration phase, and the duration of dwell \n84 time. Additionally, the effects of degraded vision would likely be magnified when the participants \n85 reached for low-contrast targets. These findings have the potential to contribute to the growing body \n86 of evidence for the importance of timely cataract removal.\n87 Methods\n88 Participants\n89 An opportunity sample of 19 participants completed the study between 24/10/2023 and 07/11/2023. \n90 One participant repeatedly failed to follow the task instructions, so the analysis did not include their \n91 data. \n92 The ages of the final sample of 18 participants (11 female, 7 male) ranged from 20 to 31 (mean = \n93 24.23 years, sd = 3.36 years) years old. All participants reported having normal or corrected-to-\n94 normal vision, and all participants reported being right-handed.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n7\n95 Participants were excluded if they were under 18, had persistent low vision that could not be corrected \n96 with glasses, had any disorder/condition that may affect their ability to grasp objects/impair their \n97 coordination, or were left-handed. Participants gave written consent for their participation in the \n98 study. Upon completing the study, the participants were entered into a prize draw to win a £30 \n99 Amazon voucher. The University of Leeds School of Psychology Ethics Committee granted ethical \n100 approval on 12/05/2023 (Ethics Reference Number: PSYC-899).\n101 Design\n102 The present study employed a within-subjects experimental design whereby participants completed \n103 four tasks (reach-grasp-lift [RGL], contrast sensitivity [CS], visual acuity [VA] and stereoacuity) \n104 under three visual conditions (full vision, monocular blur and binocular blur). Participants completed \n105 the RGL, CS, and VA tasks in the first testing session in that order, with the order of the visual \n106 conditions being counterbalanced. Due to an oversight by the researchers, stereoacuity data were not \n107 collected during the initial testing session and were obtained at a later testing session.\n108 Within each visual condition, the RGL task was varied across two levels of contrast (‘high’, a wooden \n109 target against a black background; ‘low’, a wooden target against a wooden background) and three \n110 distances (150 mm, 250 mm, 350 mm), giving rise to a 3 (visual condition) x 3 (distance) x 2 \n111 (contrast) within-subject design. The participants repeated each condition eight times, giving 144 \n112 trials split into three blocks of 48 trials by visual condition. The experiment lasted 60 to 90 minutes in \n113 total.\n114 Procedure\n115 Upon arrival, participants were presented with an information sheet outlining what would be involved \n116 in the study and allowed to ask the researcher any questions. The participants read and signed a \n117 consent form confirming their eligibility and willingness to participate in the study. Finally, \n118 participants completed the eye dominance test. The researcher then fitted the participants with the first \n119 pair of glasses (clear lenses over both eyes, a clear lens over the dominant eye and a blurred one over \n120 the other, or blurred lenses over both eyes), depending on the visual condition performed first. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n8\n121 Each participant sat as close to the table as possible with their navel in line with the midline of the \n122 table. They started each trial gripping the start point: a round wooden dowel at the near edge of the \n123 table, between their thumb and index finger, making sure all markers were visible to the cameras. \n124 Upon a go-signal, the participants reached for the target as quickly and accurately as possible, \n125 gripping the target dowels with their thumb and index finger before lifting it. The participant then held \n126 the target suspended in the air. The researcher stopped the recording before the participant replaced \n127 the target object on the table, and the trial ended. The participants repeated this procedure until they \n128 completed all the trials for that visual condition and were then taken to a separate room to complete \n129 the CS and VA tasks. This procedure was repeated for the three visual conditions before the \n130 participant was debriefed and allowed to leave. \n131 In the second testing session, the participants completed the stereoacuity task once for each visual \n132 condition, in the same order as they had completed the initial testing session. The participant was then \n133 debriefed and allowed to leave.\n134 Materials and stimuli\n135 The table used for the experiment was 1200 mm wide x 800 mm deep x 730 mm high and was painted \n136 black. A board was mounted on the table that was 608 mm wide x 826 mm deep x 8 mm high. The \n137 board was slid in the Y-plane to change the contrast condition without the participant moving. \n138 Runners positioned at 456 mm from the midline of the table limited the board's movement. The left \n139 half of the board was plain wood, and the right half was painted black. These created the low and high \n140 contrast conditions, respectively (the target was wooden). The layout of each half of the board was \n141 identical so that the midline of each half of the board was populated with a round wooden dowel of 27 \n142 mm diameter on the near edge (the starting point for each trial) and three raised rectangular grooves \n143 (target locations) at 177 mm, 277 mm and 377 mm from the near edge of the table (150 mm, 250 mm \n144 and 350 mm from the far edge of the starting point). See Figure 1 A for a photo of the experimental \n145 setup and Figure 1 B for a schematic of each half of the board.\n146\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n9\n147 Figure 1. A, photo of the experimental setup used in the present study. B, a schematic of the \n148 experimental setup used in the present study.\n149 [INSERT FIGURE 1 HERE]\n150 Lightweight infrared reflective markers (12.7 mm diameters) were attached to the index fingernail, the \n151 index knuckle, the thumbnail and the wrist (head of the radius). The instantaneous position of the four \n152 markers and one placed on the top of the target were recorded at 120 Hz with submillimetre spatial \n153 resolution by four motion capture cameras (Optitrack Flex 13 [NaturalPoint, Corvallis, OR, USA]) \n154 The target objects were cuboids (29 mm wide x 36 mm depth x 86 mm height) with dowels fitted to \n155 two opposite sides (7 mm length x 10 mm diameter), as shown in Figure 2. These were positioned on \n156 the board so that the front edge subtended an angle of 31 degrees to the front of the board. During a \n157 pilot study, this angle was determined to give participants a comfortable reach.\n158\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n10\n159 Figure 2. Target schematic from the front (left) and top (right) elevations.\n160 [INSERT FIGURE 2 HERE]\n161 Contrast sensitivity (CS) was measured using a right and left-eye Pelli-Robson CS chart (Pelli et al., \n162 1988) calibrated at a 1m distance. Visual acuity (VA) was measured using the Bailey Lovie Chart 4, \n163 calibrated at a 6 m distance (30). Stereoacuity was measured at 40 cm using the TNO stereoacuity test \n164 (31,32). Eye dominance was determined using an 'alignment test' (33). \n165 Statistical analysis\n166 The effect of the visual conditions on CS, stereoacuity, and VA was estimated using a standardised \n167 procedure after calculating the thresholds. A multilevel approach (using a Generalised Linear Mixed \n168 Model; GLMM) was taken (34). For each vision test score, the visual condition was entered into the \n169 model as a fixed factor, with a random intercept within each participant to model individual \n170 differences in each visual measure. \n171 A similar GLMM approach was taken for the RGL data. Each outcome measure (listed and defined \n172 below in Table 2) was modelled as a function of three fixed factors: visual condition, contrast and \n173 distance. The maximal model included the three main effects plus all the two-way and the three-way \n174 interactions and covariates (the CS, VA, stereoacuity and session number). If a fixed effect was not \n175 significant and not included in the minimal model (the main effects of visual condition, contrast, \n176 distance, covariates and the two-way interaction of visual condition and contrast), it was removed \n177 from the model in order to achieve a parsimonious model; these are represented by a “–” symbol in \n178 the model tables.  Some non-significant predictors excluded from the minimal model can still be seen \n179 in Tables 3, 4 and 5; this is due to each interaction effect containing several comparisons; for \n180 example, if we look at the two-way interaction of contrast and distance, this includes two \n181 comparisons: contrast x near vs. mid distance and contrast x near vs. far distance. If contrast x near vs. \n182 mid distance produces a significant effect and contrast x near vs. far distance does not, both \n183 comparisons will remain in the final model table, with the non-significant results represented as NS. \n184\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n11\n185 Table 2. The definitions of the kinematic and timing measures and their abbreviations. \nParameter (abbreviation) Description (units)\nMovement planning\nMaximum acceleration (MA) Maximum wrist acceleration before contact (mm/s2).\nMaximum velocity (MV) Maximum wrist velocity before contact (mm/s).\nMaximum grip aperture \n(MGA)\nThe maximum distance between the thumb and the index finger \nmarkers before contact (mm).\nPath length (PL) The cumulative distance travelled by a given finger or marker \n(mm).\nTime from onset (O) to MA \n(OtoMA)\nThe time from movement onset to maximum acceleration as a \nproportion of MT (%).\nTime from MA to MV \n(MAtoMV)\nThe time from maximum acceleration to maximum velocity as a \nproportion of MT (%).\nTime from O to MGA \n(OtoMGA)\nThe time from movement onset to maximum acceleration as a \nproportion of MGA (%).\nOnline control\nMaximum deceleration (MD) Maximum wrist deceleration before contact (mm/s2).\nTime from MV to MD \n(MVtoMD)\nThe time from maximum velocity to maximum deceleration as a \nproportion of MT (%).\nTime from MD to first \ncontact (MDtoC)\nThe time from maximum deceleration to first contact as a \nproportion of MT (%).\nDwell time (DwT) The time from first contact to the object lift as a proportion of MT \n(%). \nMovement time\nMovement time (MT) The time from movement onset to first contact (s).\n186 Note: Movement onset was defined as the time point when the wrist velocity first exceeds 0.05m/s in \n187 the Z plane. First contact was defined as the first time point when the index finger was behind the \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n12\n188 target, and the velocity was less than 0.05m/s in the Z plane; if, once behind the target, the index \n189 velocity never fell below 0.05m/s, the time point of minimum velocity was used. The lift time was \n190 defined as the first time point at which the target exceeded 0.05m/s in the Y plane. These data were \n191 extracted using the kinesis package (35) in R Statistical Software, version 4.3.1 (36). \n192 The dependent variables were grouped into three categories to systematically examine how visual blur \n193 influences reach-to-grasp movements: movement planning, online control, and movement time. This \n194 classification reflects distinct phases of the prehensile movement, each associated with different \n195 aspects of motor execution and sensorimotor feedback. Movement planning variables primarily \n196 capture feedforward control mechanisms based on visual input before movement execution. They are \n197 labelled as follows: maximum acceleration [MA], maximum velocity [MV], maximum grip aperture \n198 [MGA], path length [PL], time from onset [O] to MA [OtoMA], time from MA to MV [MAtoMV], \n199 and time from O to MGA [OtoMGA]. Online control variables reflect adjustments made during \n200 movement execution. They are labelled as follows: maximum deceleration [MD], time from MV to \n201 MD [MVtoMD], time from MD to first contact [MDtoC], and dwell time [DwT]. Movement Time \n202 [MT] represents the overall temporal duration of the action and integrates aspects of movement \n203 planning and online control.\n204 A random intercept was estimated for each participant to account for differences in vision and \n205 coordination between participants, and random slopes for distance and session number to account for \n206 variability in participant arm length and learning rates, respectively. \n207 Regarding the time series analysis, six kinematic landmarks were selected to divide each reaching \n208 action: maximum acceleration (MA), maximum velocity (MV), maximum grip aperture (MGA), \n209 maximum deceleration (MD), first contact with the target (Contact) and when the participant lifts the \n210 target (Lift). These landmarks are shown in Figure 3 on an exemplar velocity curve. \n211\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n13\n212 Figure 3. An example velocity profile showing the landmarks used to break down each movement. \n213 [INSERT FIGURE 3 HERE]\n214 Note: The analyses will consider the time from the onset of movement to maximum acceleration \n215 (MAcc), the time from the onset to maximum grip aperture (MGA), the time from maximum \n216 acceleration to maximum velocity (MVel), the time from maximum velocity to maximum \n217 deceleration (MDec), the time from maximum velocity to contact (Contact) and the time from contact \n218 to lift (Lift, also known as dwell time) as a percentage of total movement time for that reaching \n219 action.\n220 The normally distributed outcome variables with no time component were modelled using a Gaussian \n221 distribution (MA, MV, and MD). In contrast, the remaining variables were modelled using the \n222 Gamma distribution because they have a zero-bound time component and are typically positively \n223 skewed, which aligns with the distribution's assumptions. These variables were initially also modelled \n224 using the Inverse Gaussian distribution. However, after the performance of each model was assessed \n225 by comparing the BIC (Bayesian Information Criterion), all models were fitted using the Gamma \n226 distribution. A BIC difference greater than 10 gives \"very strong\" evidence that the model with the \n227 lower BIC value best fits the data, favouring models with lower numbers of factors (37,38). As all \n228 outcome variables were scaled to improve model fit, a small constant was added to each value \n229 (modelled with the Gamma or Inverse-Gaussian distributions), which only shifts the intercept and \n230 does not affect regression slopes (i.e. β coefficients) or significance tests. These analysis scripts are \n231 stored in a GitHub repository [https://github.com/willsheppard9895/blurNprehension], and the data is \n232 stored in the figshare repository [https://figshare.com/articles/dataset/Prehension_Data/28877057].\n233 The outcome variables and numeric predictors (CS, stereoacuity, VA and session number) were \n234 centred and normalised. As the Inverse Gaussian and Gamma distributions only accept positive \n235 values, the outcome measures modelled using these distributions were further transformed to be \n236 positive (by adding the lowest integer to create a set of positive values; this was calculated for each \n237 variable), as per equation 1.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n14\nx.scaled =  x ― mean(x)\nsd(x) + minimum integer [1]\n238 Visual condition, contrast, and distance were entered into each model as categorical predictors. \n239 Regarding visual condition, the participant's performance was compared between binocular and \n240 monocular blur and between monocular blur and full vision to match the vision changes associated \n241 with first and second eye cataract surgery, respectively. Regarding distance, comparisons were made \n242 between near and mid distance and near and far distance. Significant three-way interactions are \n243 reported and plotted in the results section. Post-hoc tests were not conducted for significant three-way \n244 interactions due to the large number of pairwise comparisons (N = 153), which would greatly increase \n245 the likelihood of type I errors. Regarding significant two-way interactions, only those including visual \n246 condition will be reported and plotted in the results section. Any significant visual condition x \n247 contrast interactions will also be subject to post-hoc testing, as these interactions addressed the main \n248 aims of the study. Significant main effects of visual condition and contrast are also reported in the \n249 text. All MLM results are reported in Tables 3 (vision tests), 4 (movement planning), 5 (online \n250 control) and 6 (movement time). If one or more significant three-way interactions predict an outcome \n251 variable, the results section will not discuss any significant two-way interactions.\n252 Inter-individual variation in each outcome measure was also assessed. For each outcome measure, the \n253 heterogeneity within the sample was assessed by comparing the relative size of the SD of the random \n254 intercepts allocated to each participant to the fixed intercept. As many of the intercepts were altered \n255 by the transformations applied to make them positive, this transformation was removed for this \n256 analysis. In the present case, this took the form \nσ \nβ , where σ is equal to the magnitude of the random \n257 intercept SD , and β is equal to the magnitude of the fixed intercept minus the transformation. When \n258 this value exceeds 0.25, we concluded that the data are heterogeneous, as a participant at the 2.5th \n259 percentile would have a score equivalent to 0.5 of the mean, and a participant at the 97.5th percentile \n260 would have a score 1.5 times the mean (39). These results were only reported if the effect was \n261 heterogeneous. The random effects of session number and distance were not subject to this analysis, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n15\n262 as these were entered to account for inter-individual variability in learning rate and arm length, \n263 respectively, and did not speak to the overall aims of the manuscript.\n264 All analyses were performed using R Statistical Software (36). Kinematic measures were extracted \n265 from the RGB data using the Kinesis package (35), GLMMs were estimated using the lme4 package \n266 (40), p-values were estimated using Satterthwaite's approximation through the lmerTest package (41), \n267 and estimated marginal means (EMM) for post-hoc testing were estimated using the emmeans \n268 package (42).\n269\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n16\n270 Results\n271 Vision tests\n272 To ensure that blurred conditions were affecting vision in a way similar to cataracts, an MLM analysis \n273 was carried out on the effects of visual conditions on contrast sensitivity (CS), stereoacuity, and visual \n274 acuity (VA) tasks. The β values in Table 3 predict the mean CS, stereoacuity and VA. \n275 Table 3. MLM estimates for fixed and random effects predicting performance on contrast sensitivity, \n276 stereoacuity and visual acuity tasks. \nEstimate Contrast sensitivity Stereoacuity Visual acuity\nIntercept 1.80***\n[1.77, 1.83]\n1.01***\n[0.69, 1.34]\n1.07***\n[0.94, 1.19]\nFixed effects (β)\nBinocular blur vs. monocular blur 0.18***\n[0.14, 0.21]\n−0.57**\n[−0.91, −0.24]\n−0.28***\n[−0.31, −0.25]\nMonocular blur vs. full vision 0.12***\n[0.08, 0.15]\n−0.39**\n[−0.62, −0.16]\n−0.18***\n[−0.21, −0.15]\nRandom effects (σ) Contrast sensitivity Stereoacuity Visual acuity\nParticipant ID 0.04 0.43 0.12\nModel fit\nR2 marginal 0.668 0.077 0.366\nR2 conditional 0.872 0.399 0.859\nNote: The participant's performance on the stereoacuity tasks was standardised (i.e., with a mean equal to \nzero and SD equal to one). None of the outcome variables were normally distributed and were, therefore, \nmodelled using the Gamma distribution. The Gamma distribution only accepts positive variables; thus, \nstereoacuity and visual acuity were made positive by adding 1. Due to the stereoacuity scores being centred, \nthe β values for the stereoacuity task are expressed as a proportion of the grand SD of stereoacuity (66.14 \narcsecs). NS = p  ≥ .05, ** = p < .01, *** = p < .001. The numbers in each cell represent the estimate [lower \n95% CI, upper 95% CI]. \n277\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n17\n278 MLM analysis found significant effects of visual condition on CS (see Figure 4A), stereoacuity (see \n279 Figure 4B) and VA (see Figure 4C); monocular blur (relative to binocular blur) and full vision \n280 (relative to monocular blur) improved CS by 0.18 and 0.12 log units, stereoacuity by 37.93 and 25.79 \n281 arc secs and VA by 0.28 and 0.18 logMAR.\n282\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n18\n283 Figure 4. Effects of visual conditions on each vision test. The central black line of each violin \n284 represents the mean, and the outer lines represent the range. A. CS (log units). B. Stereoacuity (arc \n285 secs). C. VA (logMAR).\n286 [INSERT FIGURE 4 HERE]\n287 The SD of the random intercepts was 4300.00% and 171.43% of the fixed intercepts for stereoacuity \n288 and VA, respectively. This indicates extremely high individual differences in visual functions and that \n289 group-level averages may obscure meaningful variability influencing prehensile performance. \n290 Movement planning\n291 The β values in Table 4 predict maximum acceleration, µ ma, maximum velocity, µmv, maximum grip \n292 aperture (MGA), µ mga, path length, µpl, the time from onset to maximum acceleration, µ2MA, the time \n293 from maximum acceleration to maximum velocity, µ MA2MV, and the time from onset to MGA, µ2MGA. \n294 As the outcome variables are centred, the β values are expressed as a proportion of the measure's \n295 overall SD, e.g. µ ma is expressed as a proportion of the overall SD of MA (SDma). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n19\n296 Table 4. MLM estimates for fixed and random effects predicting movement planning of the RGL task. \nEstimate\nMax. \nacceleration\nMax. velocity MGA Path length\nTime from \nonset to max. \nacceleration\nTime from \nmax. \nacceleration to \nmax. velocity\nTime from \nonset to \nmaximum grip \naperture\nIntercept\n−0.77*** \n[−0.99, −0.54]\n−1.09*** \n[−1.24, −0.94]\n3.20*** \n[2.79, 3.61]\n0.88*** \n[0.77, 0.98]\n1.89***\n[1.48, 2.30]\n3.22***\n[2.87, 3.56]\n3.77***\n[3.33, 4.21]\nThree-way interactions (β)\nMax. \nacceleration\nMax. velocity MGA Path length\nTime from \nonset to max. \nacceleration\nTime max. \nacceleration to \nthe max. \nvelocity\nTime from \nonset to \nmaximum grip \naperture\nBinocular blur vs. monocular blur * Contrast \n* Near vs. mid distance\n- -\n0.22*\n[0.01, 0.43]\n- NS - NS\nMonocular blur vs. full vision * Contrast * \nNear vs. mid distance\n- -\n0.24*\n[0.03, 0.45]\n- NS - NS\nBinocular blur vs. monocular blur * Contrast \n* Near vs. far distance\n- -\n0.27*\n[0.06, 0.48]\n-\n0.44*\n[0.07, 0.81]\n-\n0.39*\n[0.05, 0.72]\nMonocular blur vs. full vision * Contrast * \nNear vs. far distance\n- -\n0.39***\n[0.18, 0.60]\n- NS - NS\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n20\nTwo-way interactions (β)\nMax. \nacceleration\nMax. velocity MGA Path length\nTime from \nonset to max. \nacceleration\nTime max. \nacceleration to \nthe max. \nvelocity\nTime from \nonset to \nmaximum grip \naperture\nBinocular blur vs. monocular blur * Contrast NS NS NS NS NS NS NS\nMonocular blur vs. full vision * Contrast NS NS\n−0.21**\n[−0.36, −0.07]\nNS NS NS NS\nBinocular blur vs. monocular blur * Near vs. \nmid distance\n-\n0.06*\n[0.00, 0.12]\n−0.16*\n[−0.31, −0.01]\n- NS - NS\nMonocular blur vs. full vision * Near vs. \nmid distance\n- NS NS - NS - NS\nBinocular blur vs. monocular blur * Near vs. \nfar distance\n-\n0.06*\n[0.00, 0.12]\nNS - NS - NS\nMonocular blur vs. full vision * Near vs. far \ndistance\n- NS NS -\n−0.41** \n[−0.67, −0.15]\n- NS\nContrast * Near vs. mid distance - -\n0.13***\n[0.05, 0.20]\n- NS - NS\nContrast * Near vs. far distance - - NS - NS - NS\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n21\nMain effects (β)\nMax. \nacceleration\nMax. velocity MGA Path length\nTime from \nonset to max. \nacceleration\nTime max. \nacceleration to \nthe max. \nvelocity\nTime from \nonset to \nmaximum grip \naperture\nBinocular blur vs. monocular blur\n0.25**\n[0.08, 0.41]\nNS\n0.55***\n[0.31, 0.78]\n0.05*\n[0.01, 0.09]\nNS NS NS\nMonocular blur vs. full vision\n0.22***\n[0.09, 0.35]\nNS\n0.43***\n[0.26, 0.60]\n0.04*\n[0.01, 0.07]\nNS\n0.30*\n[0.06, 0.54]\nNS\nContrast\n−0.05**\n[−0.08, −0.01]\n−0.03**\n[−0.04, −0.01]\n−0.10***\n[−0.15, −0.05]\n−0.01*\n[−0.02, 0.00]\nNS NS NS\nNear vs. mid distance\n0.88***\n[0.74, 1.03]\n1.17***\n[1.09, 1.25]\nNS\n1.19***\n[1.17, 1.20]\n0.23*\n[0.01, 0.45]\nNS\n0.45*** \n[0.25, 0.65]\nNear vs. far distance\n1.52***\n[1.29, 1.75]\n2.13***\n[1.99, 2.26]\nNS\n2.36***\n[2.34, 2.38]\nNS NS\n0.66*** \n[0.40, 0.92]\nStereoacuity NS\n−0.05*\n[−0.09, −0.01]\n0.11*\n[0.03, 0.20]\nNS NS NS NS\nCS\n−0.05*\n[−0.10, 0.00]\nNS NS\n0.03***\n[0.02, 0.04]\n0.19***\n[0.10, 0.27]\nNS\n0.08* \n[0.01, 0.16]\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n22\nVA NS NS\n0.25**\n[0.09, 0.41]\n0.04*\n[0.01, 0.07]\nNS NS NS\nSession number NS NS NS\n−0.01***\n[−0.02, −0.01]\nNS NS NS\nRandom effects (σ)\nMax. \nacceleration\nMax. velocity MGA Path length\nTime to the \nmax. \nacceleration\nTime max. \nacceleration to \nthe max. \nvelocity\nTime from \nonset to the \nmaximum grip \naperture\nParticipant ID 0.48 0.32 0.23 0.05 0.4 0.29 0.28\nNear vs. mid distance 0.29 0.17 0.09 - 0.22 0.23 0.18\nNear vs. far distance 0.49 0.3 0.15 - 0.29 0.34 0.24\nSession number 0.1 0.1 0.08 - 0.15 0.11 0.13\nModel fit\nMax. \nacceleration\nMax. velocity MGA Path length\nTime from \nonset to max. \nacceleration\nTime max. \nacceleration to \nthe max. \nvelocity\nTime from \nonset to \nmaximum grip \naperture\nR2 marginal 0.368 0.747 0.49 0.986 0.158 0.236 0.524\nR2 conditional 0.852 0.954 0.846 0.989 0.559 0.698 0.885\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n23\nNote: All outcome variables and numeric predictors (VA, CS, stereoacuity, and session number) were standardised (i.e., with a mean equal to zero and SD equal to one). \nNormally distributed variables (maximum velocity and maximum acceleration) were modelled using a Gaussian distribution. In contrast, the remaining variables were \nmodelled using a Gamma distribution. The Gamma only accepts positive values; these variables, therefore, are further transformed to be made positive (MGA + 3, path \nlength + 2, time from onset to the max. acceleration + 2, time from max. acceleration to the max. velocity + 3, and time from onset to the maximum grip aperture + 4). NS \n= p  ≥ .05 * = p < .05, ** = p < .01, *** = p < .001. The numbers in each cell represent the estimate [lower 95% CI, upper 95% CI]. Blank cells (-) indicate that this \npredictor was not included in the final model.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n24\n298 Maximum acceleration\n299 MLM analysis revealed significant main effects of visual condition and target contrast on maximum \n300 acceleration. Maximum acceleration increased progressively from binocular to monocular blur \n301 (67.09mm/s 2 higher) to full vision (59.34 mm/s2 higher) and by 12.54 mm/s2 with high contrast \n302 compared to low contrast targets.\n303 The SD of the random intercept was 62.34% of the fixed intercept, reflecting moderate variability in \n304 baseline MA across participants.\n305 Maximum velocity\n306 There were two significant two-way interactions between visual condition (binocular blur vs. \n307 monocular blur) x distance (near vs. mid) and visual condition (binocular blur vs. monocular blur) x \n308 distance (near vs. far). The increase in maximum velocity associated with monocular blur relative to \n309 binocular blur was greater at mid and far distances than near distances (see Figure 5). A significant \n310 main effect of contrast revealed that maximum velocity increased by 6.85mm/s with high contrast \n311 compared to low contrast targets.\n312\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n25\n313 Figure 5. The effect of distance and visual condition (monocular and binocular blur) on maximum \n314 velocity (m/s). Error bars represent standard error (SE).\n315 [INSERT FIGURE 5 HERE]\n316 Participants' baseline levels of MV showed variability, with the SD  of the random intercepts estimated \n317 as 29.36% of the fixed intercept. \n318 Maximum Grip Aperture (MGA)\n319 MLM analysis revealed four significant three-way interactions. When considering the interactions of \n320 visual condition (binocular blur vs. monocular blur) x contrast x distance, at both mid and far \n321 distances, monocular blur (relative to binocular blur) is associated with a decrease in MGA regardless \n322 of target contrast. However, at a near distance, monocular blur (relative to binocular blur) is \n323 associated with a reduction in MGA with low contrast targets and a slight increase or no difference in \n324 MGA with high contrast targets (Figure 6). Furthermore, when considering the interactions of visual \n325 condition (monocular blur vs. full vision) x contrast x distance, at both mid and far distances, full \n326 vision (relative to monocular blur) is associated with an increase in MGA regardless of target contrast. \n327 However, at near distance, full vision (relative to monocular blur)  is associated with increased MGA \n328 with high contrast targets and a slight increase or no difference in MGA with low contrast targets \n329 (Figure 6).\n330 Figure 6. The effect of visual condition (binocular blur vs. monocular blur and monocular blur vs. full \n331 vision), contrast and distance (near vs. mid and near vs. far) on MGA (mm).  Error bars represent SE .\n332 [INSERT FIGURE 6 HERE]\n333 MLM analysis revealed significant main effects of visual condition and contrast on MGA. MGA \n334 increased progressively from binocular to monocular blur (4 mm larger) to full vision (3.16 mm \n335 larger) and by 0.74 mm with high contrast compared to low contrast targets.\n336 The SD of the random intercepts was 115.00% of the fixed intercept, indicating high individual \n337 variability in base levels of MGA. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n26\n338 Path length \n339 MLM analysis revealed two significant main effects of visual condition on path length. Path length \n340 increased progressively from binocular to monocular blur (4.52 mm longer) to full vision (3.65 mm \n341 longer) and by 1.07 mm with high contrast compared to low contrast targets.\n342 Time to Maximum Acceleration (OtoMA)\n343 A significant three-way visual condition (binocular blur vs. monocular blur) x contrast x distance \n344 (near vs. far) interaction indicates that at near distance, with low contrast targets, monocular blur is \n345 associated with a slight decrease in OtoMA; however, at far distance with low contrast targets, \n346 monocular blur is associated with a slight increase in OtoMA. Monocular blur is not associated with a \n347 change in OtoMA with high contrast targets at either distance (see Figure 7).\n348\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n27\n349 Figure 7. The effect of visual condition (binocular vs. monocular blur), contrast and distance (near vs. \n350 far) on the time from onset to maximum acceleration (% of total movement time). Error bars represent \n351 SE.\n352 [INSERT FIGURE 7 HERE]\n353 The baseline level of OtoMA showed substantial variability across participants, with the SD of the \n354 random intercepts estimated at 363.64% of the fixed intercept. \n355 Time from maximum acceleration to maximum velocity (MAtoMV)\n356 A significant main effect of visual condition (monocular blur vs. full vision) indicated that full vision \n357 increased MAtoMV by 2.26% compared to monocular blur.\n358 The SD of the random intercepts was 131.82% of the fixed intercept, indicating high inter-individual \n359 variability in baseline levels of MAtoMV. \n360 Time from Onset to MGA (OtoMGA)\n361 A significant three-way visual condition (binocular blur vs. monocular blur) x contrast x distance \n362 (near vs. far) indicated that monocular blur was associated with a slight increase in OtoMGA at near \n363 distance with high contrast targets and at far distance with low contrast targets, whereas, monocular \n364 blur was associated with a slight decrease in OtoMGA at near distance with low contrast targets and \n365 no apparent change in OtoMGA at far distance with a high contrast target (see Figure 8).\n366\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n28\n367 Figure 8. The effect of visual condition (binocular blur vs. monocular blur), contrast and distance \n368 (near vs. far distance) on the time to maximum grip aperture (% of total movement time). Error bars \n369 represent SE.\n370 [INSERT FIGURE 8 HERE]\n371 Similar to MGA, baseline levels of OtoMGA also showed high individual differences (SD of the \n372 random intercepts equal to 121.74% of the fixed intercept). \n373 Online control\n374 The β values in Table 5 predict maximum deceleration, µ md, the time from maximum velocity to \n375 maximum deceleration, µ MV2MD, the time from maximum deceleration to contact with the target, \n376 µMD2C, and dwell time, µDwT. As the outcome variables are centred, the β values are expressed as a \n377 proportion of the measure's overall SD, e.g., µ md is expressed as a proportion of the overall SD of MD \n378 (SD md).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n29\n379 Table 5. MLM estimates for fixed and random effects predicting online control characteristics of the \n380 RGL task. \nEstimate\nMax. \ndeclaration\nTime from max. \nvelocity to the \nmax. \ndeceleration\nTime from max. \ndeceleration to \ncontact\nTime from \ncontact to lift \n(dwell time)\nIntercept\n0.79*** \n[0.59, 1.00]\n3.29*** \n[2.90, 3.68]\n2.55*** \n[2.24, 2.86]\n2.21*** \n[1.76, 2.66]\nTwo-way interactions (β)\nMax. \ndeclaration\nTime max. \nvelocity to the \nmax. \ndeceleration\nTime from max. \ndeceleration to \ncontact\nTime from \ncontact to lift\nBinocular blur vs. monocular \nblur * Contrast\nNS NS NS NS\nMonocular blur vs. full vision * \nContrast\nNS NS NS NS\nBinocular blur vs. monocular \nblur * Near vs. mid distance\n- NS NS -\nMonocular blur vs. full vision * \nNear vs. mid distance\n-\n−0.33**\n[−0.57, −0.10]\n0.29**\n[0.08, 0.50]\n-\nBinocular blur vs. monocular \nblur * Near vs. far distance\n- NS NS -\nMonocular blur vs. full vision * \nNear vs. far distance\n- NS NS -\nContrast * Near vs. mid distance\n0.11*\n[0.02, 0.19]\nNS - -\nContrast * Near vs. far distance NS\n−0.24**\n[−0.40, −0.08]\n- -\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n30\nMain effects (β)\nMax. \ndeclaration\nTime max. \nvelocity to the \nmax. \ndeceleration\nTime from max. \ndeceleration to \ncontact\nTime from \ncontact to lift\nBinocular blur vs. monocular \nblur\nNS NS NS\n−0.53***\n[−0.78, −0.29]\nMonocular blur vs. full vision NS NS NS\n−0.39***\n[−0.57, −0.20]\nContrast NS NS NS NS\nNear vs. mid distance\n−0.99***\n[−1.15, −0.83]\nNS\n0.51***\n[0.28, 0.74]\n−0.15 [−0.21, \n−0.09]***\nNear vs. far distance\n−1.50***\n[−1.73, −1.27]\n−0.38**\n[−0.67, −0.10]\n1.01***\n[0.68, 1.33]\n−0.26***\n[−0.32, −0.20]\nStereoacuity\n−0.09*\n[−0.17, −0.01]\n−0.15*\n[−0.29, 0.00]\nNS NS\nCS NS NS NS NS\nVA NS NS NS\n−0.19**\n[−0.33, −0.05]\nSession number NS NS\n0.08*** \n[0.05, 0.12]\n−0.11***\n[−0.14, −0.09]\nRandom effects (σ)\nMax. \ndeceleration\nTime max. \nvelocity to the \nmax. \ndeceleration\nTime max. \ndeceleration to \ncontact\nTime from \ncontact to lift\nParticipant ID 0.43 0.32 0.25 0.37\nNear vs. mid distance 0.32 0.22 0.22 -\nNear vs. far distance 0.48 0.26 0.31 -\nSession number 0.12 0.08 - -\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n31\nModel fit\nMax. \ndeclaration\nTime max. \nvelocity to the \nmax. \ndeceleration\nTime from max. \ndeceleration to \ncontact\nTime from \ncontact to lift\nR2 marginal 0.381 0.406 0.559 0.203\nR2 conditional 0.817 0.712 0.787 0.614\nNote: All outcome variables and numeric predictors (VA, CS, stereoacuity, and session number) were \nstandardised (i.e., with a mean equal to zero and SD equal to one). Maximum deceleration was normally \ndistributed and was modelled using a Gaussian distribution. In contrast, the remaining variables were \nmodelled using a Gamma distribution. The Gamma distribution only accepts positive values; therefore, these \nvariables are further transformed to be positive (time from maximum velocity to maximum deceleration + 3, \ntime from maximum deceleration to contact + 3 and time from contact to lift +2). None of the final models \nfor the variables presented in this table contained a significant three-way interaction, so three-way \ninteractions are not included. The numbers in each cell represent the estimate [lower 95% CI, upper 95% CI]. \nBlank cells (-) indicate that this predictor was not included in the final model.  NS = p  ≥ .05 * = p < .05, ** = \np < .01, *** = p < .001.\n381 Maximum deceleration\n382 No significant interactions or main effects were relevant to the study's aims. \n383 Time from maximum velocity to maximum deceleration (MVtoMD)\n384 A significant two-way visual condition (monocular blur vs. full vision) x distance (near vs. mid) \n385 interaction indicates that full vision is associated with an increase in MVtoMD at a near distance and a \n386 decrease at a mid distance (see Figure 9). \n387\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n32\n388 Figure 9. The effect of visual condition (monocular blur vs. full vision) x distance (near vs. mid) on \n389 the time from maximum velocity to maximum deceleration (% of total movement time). Error bars \n390 represent SE.\n391 [INSERT FIGURE 9 HERE]\n392 The SD of the random intercepts for MVtoMD showed high individual variability (110.34% of the \n393 fixed intercept). \n394\n395\n396\n397\n398\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n33\n399 Time from maximum deceleration to contact with the target (MDtoC)\n400 A significant two-way visual condition (monocular blur vs. full vision) x distance (near vs. mid \n401 distance) interaction indicated that full vision was associated with a decrease in MDtoC, relative to \n402 monocular blur, at a near distance and an increase in MDtoC at a mid distance (see Figure 10).\n403 Figure 10. The effect of visual condition (monocular blur vs. full vision) and distance (near vs. mid) \n404 on the time from maximum deceleration to contact (% of total movement time). Error bars represent \n405 SE.\n406 [INSERT FIGURE 10 HERE]\n407 The SD of the random intercepts was 55.56% of the fixed intercept, indicating that the base level of \n408 MDtoC was moderately heterogeneous between participants. \n409 Time from contact to lift (dwell time [DwT])\n410 MLM analysis revealed significant main effects of visual condition on DwT, which decreased \n411 progressively from binocular to monocular blur (5.08% lower) to full vision (3.68% lower) and by \n412 12.54 mm/s 2 and showed high individual differences in its baseline levels as indicated by the SD of \n413 the random intercepts being equal to 176.19% of the fixed intercept. \n414\n415\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n34\n416 Movement time\n417 The β values in Table 6 predict movement time, µ mt. As movement time is centred, the β values are \n418 expressed as a proportion of movement time's overall SD, SD mt. \n419 Table 6. MLM estimates for fixed and random effects predicting online control characteristics of the \n420 RGL task. \nEstimate Movement time\nIntercept\n2.77*** \n[2.35, 3.19]\nThree-way interactions (β) Movement time\nBinocular blur vs. monocular blur * Contrast * Near vs. mid distance NS\nMonocular blur vs. full vision * Contrast * Near vs. mid distance NS\nBinocular blur vs. monocular blur * Contrast * Near vs. far distance NS\nMonocular blur vs. full vision * Contrast * Near vs. far distance\n-0.23*\n[-0.45, -0.02]\nTwo-way interactions (β) Movement time\nBinocular blur vs. monocular blur * Contrast NS\nMonocular blur vs. full vision * Contrast NS\nBinocular blur vs. monocular blur * Near vs. mid distance NS\nMonocular blur vs. full vision * Near vs. mid distance NS\nBinocular blur vs. monocular blur * Near vs. far distance NS\nMonocular blur vs. full vision * Near vs. far distance\n0.16*\n[0.01, 0.31]\nContrast * Near vs. mid distance NS\nContrast * Near vs. far distance NS\nMain effects (β) Movement time\nBinocular blur vs. monocular blur\n−0.27**\n[−0.47, −0.06]\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n35\nMonocular blur vs. full vision\n−0.34***\n[−0.49, −0.19]\nContrast NS\nNear vs. mid distance\n0.53***\n[0.43, 0.63]\nNear vs. far distance\n1.09***\n[0.96, 1.22]\nStereoacuity NS\nCS NS\nVA NS\nSession number NS\nRandom effects (σ) Movement time\nParticipant ID 0.21\nNear vs. mid distance 0.09\nNear vs. far distance 0.11\nSession number 0.1\nModel fit Movement time\nR2 marginal 0.736\nR2 conditional 0.933\nNote: Movement time and numeric predictors (VA, CS, stereoacuity, and session number) were standardised \n(i.e., with a mean equal to zero and SD equal to one). Movement time was modelled using a Gamma \ndistribution. The Gamma distribution only accepts positive values; therefore, movement time was further \ntransformed to be positive (movement time + 3). The numbers in each cell represent the estimate [lower 95% \nCI, upper 95% CI]. Blank cells (-) indicate that this predictor was not included in the final model. NS = p  ≥ \n.05, ** = p < .01, *** = p < .001.\n421\n422\n423\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n36\n424 A significant three-way visual condition (monocular blur vs. full vision) x contrast x distance (near vs. \n425 far) indicated that full vision was associated with the largest decrease in MT at a far distance with low \n426 contrast targets (see Figure 11). There were also two main effects of visual condition: monocular blur \n427 reduces movement time by 32.80ms compared to binocular blur, and full vision reduces movement \n428 time by 41.93ms relative to monocular blur. \n429 Figure 11. The effect of visual condition (monocular blur vs. full vision), contrast and distance (near \n430 vs. far) on movement time (s). Error bars represent SE .\n431 [INSERT FIGURE 11 HERE]\n432 The SD of the random intercepts was equal to 91.30% of the fixed intercept, indicating that the base \n433 level of MT was highly heterogeneous between participants. \n434 Discussion\n435 The present study investigated the effects of monocular and binocular cataract-like visual blur and the \n436 interaction between visual blur and target contrast on the kinematic and timing characteristics of a \n437 reach-to-grasp task, marking a first in the literature. A priori, it was predicted that reducing visual blur \n438 would affect the scaling of the maximum grip aperture (MGA) and overall movement time (MT). \n439 These effects would be coupled with increased deceleration time and dwell time (from target contact \n440 to lifting the target [DwT]). We also predicted that some, but not all, changes to performance \n441 associated with visual blur would be magnified by reducing the contrast of the targets. To match the \n442 progression of an individual undergoing cataract surgery, the effect of visual condition was estimated \n443 as the effect of monocular blur relative to binocular blur (as per first-eye cataract surgery) and the \n444 effect of full vision relative to monocular blur (as per second-eye cataract surgery). \n445 To assess the effect of the visual condition on participants' vision, contrast sensitivity (CS), \n446 stereoacuity, and visual acuity (VA) were tested under each condition. Modelling confirmed that \n447 monocular blur (compared to binocular blur) and full vision (compared to monocular blur) were \n448 associated with improvements in all three vision tests. This effect was largest for the transition from \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n37\n449 binocular blur to monocular blur, relative to the transition from monocular blur to full vision. The \n450 effect of visual condition on CS and VA was substantial (R 2 marginal = 0.668 and 0.366, \n451 respectively); however, the effect of visual condition on stereoacuity was minimal (R 2 marginal = \n452 0.077; [Cohen, 1988]). At the time of writing, there appears to be no multilevel analysis of the visual \n453 outcomes of cataract surgery for comparison. However, these outcomes follow a similar pattern to \n454 those of previously published literature. For example, bilateral cataract removal was associated with \n455 the largest effect for CS (r 2 = 0.46), followed by VA (r2 = 0.41) and the smallest effect for \n456 stereoactuity (r 2 = 0.21; 44).\n457 In line with research investigating the effects of amblyopia and monocular vision on prehension \n458 (1,6,10,11,14), superior vision (in this case, monocular blur relative to binocular blur, and full vision \n459 relative to monocular blur) was associated with a decrease in MT and with increased maximum \n460 acceleration (MA). No differences in maximum deceleration (MD) were found. This pattern replicates \n461 previous findings showing that control groups exhibit significantly greater MA but similar MD \n462 compared to amblyopia patients (14). Full vision relative to monocular blur did not affect maximum \n463 velocity (MV), while monocular blur relative to binocular blur increased MV at mid and far distances. \n464 A trend towards increased MV at a near distance did not reach significance, likely due to the lower \n465 speeds of smaller movements, in accordance with Fitts' Law (45,46), where reduced movement \n466 magnitude limits the effect. These findings suggest that the improvement from binocular to monocular \n467 blur (analogous to first-eye cataract removal; FES) is sufficient to increase the MV in prehension \n468 movements, while the transition from monocular blur to full vision (second-eye cataract removal; \n469 SES) appears to provide no additional increase in MV. \n470 Although the transition from monocular blur to full vision (SES) resulted in subtler improvements in \n471 prehension compared to the more dramatic effects of the transition from binocular to monocular blur \n472 (FES), gains in kinematic markers, such as reduced DwT and increased MA (which explain the \n473 reduction in movement time despite no change in MV), highlight that SES may be associated with \n474 critical benefits and enhancements in safety, efficiency, and independence during daily tasks.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n38\n475 The grip aperture (the distance between the thumb and index finger) is planned before movement \n476 initiation based on the object's perceived size (47). Therefore, changes to grip apertures due to visual \n477 blur likely reflect changes in prehensile movements' planning. In this study, monocular blur relative to \n478 binocular blur and full vision relative to monocular blur were associated with increases in MGA of \n479 4.00mm and 3.16mm, respectively. These results contradict prior research showing that superior \n480 vision leads to no change in MGA in control participants compared to amblyopia patients (Grant et \n481 al., 2007) or with binocular vision compared with monocular vision (4,5,8). Similarly, the pattern \n482 contrasts with other studies reporting increased MGA with poorer vision, suggesting a greater safety \n483 margin (6,8,9). The increase in MGA associated with improved vision found in the present study may \n484 result from visual blur impairing the participants' ability to accurately judge the target's distance. \n485 Servos et al. (1992) proposed that impaired vision (such as monocular vision compared to binocular \n486 vision) leads to participants underestimating target distance, which in turn causes the target to be \n487 perceived as smaller, resulting in smaller grip apertures. In the present study, the results suggest that \n488 improved vision was associated with increased estimates of target distance and, consequently, \n489 increased estimates of target size. This led to a larger MGA under monocular blur compared to \n490 binocular blur, and under full vision compared to monocular blur. This effect can be further evidenced \n491 by comparing MV with binocular and monocular blur. According to Fitts's law, an increase in \n492 estimated distance should be associated with an increase in MV (45,46), which can be seen in Figure \n493 5. Therefore, it seems reasonable to infer that participants estimated the target distance to be greater \n494 with monocular blur vs binocular blur, and scaled the target size accordingly at movement planning.\n495 A point of note regarding the effect of visual condition on MGA is that, while the modelling suggests \n496 that monocular blur relative to binocular blur is associated with an increase in MGA, this is not \n497 represented in Figure 6, except for high contrast targets at a near distance. We considered the \n498 hypothesis that this effect might result from the multilevel structure of the analysis creating false \n499 positives or erroneous estimates. The effects of this multilevel structure were investigated by \n500 conducting a supplementary analysis (not reported in the results section). The simplest model of the \n501 effect of visual condition on MGA was calculated; it only included a main effect of visual condition \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n39\n502 and a random intercept for each participant. In this case, neither monocular blur relative to binocular \n503 blur nor full vision relative to monocular blur was associated with any change in MGA (p > .05). \n504 However, when stereoacuity or VA was added to the model, the main effects of both monocular blur \n505 relative to binocular blur and full vision relative to monocular blur emerged, both associated with \n506 increases in MGA (p < .001, see \n507 https://github.com/willsheppard9895/blurNprehension/mgaModels.html for the model summary). \n508 The effect of visual condition only emerged when individual differences in stereoacuity and VA were \n509 accounted for. This highlights two important considerations: first, that inter-individual differences in \n510 visual functions, such as VA and stereoacuity, evidenced by the analysis of random effects, may be \n511 masking the effects of visual impairment - in this case, visual blur. This demonstrates the need to \n512 consider each individual rather than applying a “one size fits all” methodology in eye care. Further \n513 support comes from the heterogeneity of performance on almost all motor outcomes in the RGL task, \n514 demonstrated by the analysis of the random intercepts. Second, this also suggests that other aspects of \n515 vision contribute to grip scaling beyond clinical measures. One possible explanation is vergence, \n516 which is crucial for grip aperture adjustments in prehension tasks (8). Unlike stereoacuity and VA, \n517 vergence behaviour is, at least in part, driven by visual blur, meaning that blur manipulation could \n518 impair vergence calibration, having a knock-on effect on depth perception (49) and potentially on \n519 visuomotor functions, as the observed changes in MGA strongly suggest.\n520 Regarding the time course of prehensile movements, the present study showed that during the \n521 early/acceleration phase, monocular blur relative to binocular blur had no significant effect on the \n522 proportion of movement time spent reaching maximum acceleration (OtoMA) or transitioning from \n523 maximum acceleration to maximum velocity (MAtoMV). Similarly, full vision relative to monocular \n524 blur was also associated with no overall change in OtoMA. However, we found a significant 2.26% \n525 increase in MAtoMV, suggesting that SES might be associated with improved planning of prehensile \n526 movements. The increase in the percentage of overall movement time in the early stages of the \n527 movement (OtoMA and MAtoMV) suggests that full vision reduces the number of corrections being \n528 made in the later stages of the movement. These results contrast with studies that found no effect or an \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n40\n529 absolute decrease in acceleration time associated with superior vision (see 14 for an example). On the \n530 other hand, the increase in MAtoMV observed here is comparable in scale to previous findings when \n531 expressed as a percentage of total movement time, 1.62% (9).\n532 In the late/deceleration phase, there were no main effects of visual condition on either the time \n533 between max velocity and max deceleration (MVtoMD) or from MD to first contact with the target \n534 (MDtoC). The impact of visual condition interacted with near versus mid distance, whereby full \n535 vision relative to monocular blur was associated with an increase in MVtoMD at a near distance and a \n536 decrease at mid distance (see Figure 9), compared with a decrease in MDtoC at a near distance and an \n537 increase in MDtoC at mid distance (see Figure 10). However, these effects are small (~1% difference) \n538 and not coupled with a main effect, so they seem inconsequential practically. As reducing visual blur \n539 has a limited effect on the time course of the late/deceleration phase of the movement, this suggests \n540 that reducing visual blur is associated with lesser changes to the online control of prehensile \n541 movements (50) compared to the significant impact of reducing visual blur on metrics associated with \n542 movement planning (such as MA, MV and MGA).\n543 Although there was no main effect of visual condition on the time from onset to MGA (OtoMGA), a \n544 significant three-way interaction suggested that full vision (relative to monocular blur) increased \n545 OtoMGA under the most challenging conditions (far distance, low contrast). However, considering \n546 the variability in previous findings from the literature (-7.80% to +5.11% change in OtoMGA with \n547 binocular vs. monocular vision) and the overall positive effects of full vision on movement planning \n548 (e.g., increased MA, MV, and reduced dwell time), this isolated interaction is unlikely to indicate a \n549 meaningful impairment in planning.\n550 As per previous research, monocular blur and full vision (relative to binocular and monocular blur, \n551 respectively) were associated with decreased dwell time (9,10,14). A decrease in dwell time suggests \n552 that improving vision improves the representation of the object's position and physical parameters, \n553 reducing the reliance on somatosensory feedback from the finger and thumb and allowing the lift to \n554 be executed more quickly, analogous to reduced online control errors (10). This result is particularly \n555 compelling when coupled with the increases in MA (monocular blur and full vision), MV (full vision \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n41\n556 only), and MGA (monocular blur and full vision). It seems reasonable to conclude that reducing \n557 visual blur leads to better planning and online control of prehensile movements. Reducing visual blur \n558 allows individuals to plan better prehensile movements, most likely due to changes in the perception \n559 of the shape/size of the target. This is coupled with an improved ability to make in-flight adjustments, \n560 facilitating the efficient and safe performance of the movement. \n561 Future studies could benefit from using liquid crystal glasses to control the precise timing of visual \n562 input. Grant and Conway (2019) demonstrated that removing visual feedback after movement \n563 initiation removed any binocular advantage associated with full vision compared to amblyopia; it \n564 would be beneficial to know if this also applies to visual blur. This, in turn, would help us better \n565 understand the situations in which patients with conditions such as cataracts may face the greatest \n566 difficulties with prehension. Furthermore, it would be good to manipulate visual cues to delineate \n567 effects outside of CS, VA and stereoacuity (the effect of visual blur on stereoacuity was very small, R 2 \n568 = 0.077, see Table 3). For example, Loftus et al. (2004) manipulated the availability of vergence \n569 information, allowing us to test the theory suggested earlier in the discussion. Furthermore, future \n570 research may want to manipulate blur levels to gain an estimate of the point at which visual blur and \n571 visual conditions such as cataracts may begin to make prehensile tasks increasingly difficult.\n572 A critical limitation of this study is that our setup did not effectively control for monocular depth \n573 cues: without a chinrest, participants might compensate for visual blur by adjusting their head position \n574 and viewpoint (although this is not entirely atypical, see 9). This ability to adapt is directly relevant to \n575 real-world cataract patients, particularly those who have undergone FES and retain one unimpaired \n576 eye. FES patients can partially mitigate the functional limitations of monocular blur through \n577 compensatory head movements and behavioural adjustments, reducing their reliance on degraded \n578 stereo and vergence cues. However, this compensatory strategy does not replicate the experience of \n579 true binocular clarity. The results of this study reinforce that even when individuals have access to \n580 monocular adaptation strategies, movement planning and control continue to improve with full \n581 binocular vision. SES provides functional advantages that extend beyond behavioural compensation, \n582 reinforcing its clinical importance.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n42\n583 The results proposed here, along with previously published work, suggest that reducing visual blur \n584 and, by proxy, timely FES and SES improve the planning and, to a lesser extent, the online control of \n585 prehensile movements. These findings motivate a future programme of work to investigate the effects \n586 of FES and SES in cataract patients as they move through their surgery journey, thus providing a \n587 better understanding of the potential benefits of cataract surgery on skilled action. \n588\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n43\n589 Acknowledgements \n590 Thanks to everyone that helped.\n591 References \n592 1. Grant S, Conway ML. Reach-to-precision grasp deficits in amblyopia: Effects of object contrast \n593 and low visibility. Vision Res. 2015;114:100–10. \n594 2. Crawford JD, Medendorp WP, Marotta JJ. Spatial Transformations for Eye–Hand Coordination. J \n595 Neurophysiol. 2004 Jul;92(1):10–9. \n596 3. Flanagan JR, Bowman MC, Johansson RS. Control strategies in object manipulation tasks. Curr \n597 Opin Neurobiol. 2006 Dec 1;16(6):650–9. \n598 4. Bradshaw MF, Elliott KM, Watt SJ, Hibbard PB, Davies IRLL, Simpson PJ. Binocular cues and \n599 the control of prehension. Spat Vis. 2004;17(1–2):95–110. \n600 5. Gnanaseelan R, Gonzalez DA, Niechwiej-Szwedo E. 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It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted June 8, 2025. ; https://doi.org/10.1101/2025.06.05.658186doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}