Abstract
Objective:
Rehabilitation from motor system dysfunction relies on learning deliberate motor corrections through practice and feedback. This is called explicit motor adaptation. One key source of feedback for this adaptation is the visual error signal between the intended movement and the achieved movement. As people age, both motor dysfunction and visual impairment become more common, potentially compromising the visual feedback signal. Previous work has shown that visual impairment can disrupt the implicit, automatic adjustments made by the sensorimotor system. But how visual impairment influences explicit motor adaptation, a cornerstone of rehabilitation, remains unknown.
Methods:
To address this gap, we recruited individuals with low vision (LV), defined as uncorrectable visual impairment resulting in functional vision loss, and age-matched controls to complete a visuomotor task designed to isolate 2 components of explicit motor adaptation: discovering a new deliberate sensorimotor strategy and recalling a previously learned one.
Results:
Surprisingly, LV was not associated with a measurable impact on either component. Individuals with LV were as effective as controls in both discovering and retrieving successful explicit sensorimotor strategies.
Conclusion:
Together, these findings identify explicit strategies as a resilient learning mechanism that can be potentially leveraged in motor rehabilitation, even when visual input is degraded.
Introduction
Sensorimotor adaptation—the process of correcting movement errors through feedback and practice—is a fundamental human capacity that keeps our actions well-calibrated amid changes to the body and environment.1,2 Motor adaptation does not rely on a single mechanism but instead recruits multiple learning processes.3-6 A key distinction is between implicit adaptation,7,8 which automatically recalibrates the sensorimotor system, and explicit strategies, which involve deliberate motor corrections to achieve task success.9-11 For instance, a basketball player may automatically compensate for arm fatigue to preserve shot stability (implicit adaptation) while deliberately alter his shot trajectory to avoid a defender (explicit strategy).
Extensive laboratory research has shown that increases in visual feedback uncertainty weaken implicit adaptation.12-18 This weakening likely occurs because uncertain visual feedback reduces the reliability of the visual error signal (the mismatch between perceived and intended movement).19-23 A natural question is whether organic disruptions to visual function might also meaningfully impair motor adaptation. Thus, we previously investigated implicit adaptation in individuals with low vision (LV, defined clinically as uncorrectable visual impairment that results in functional vision loss). We found that implicit adaptation in this group was attenuated for small error signals but not large ones, consistent with the general effects of feedback uncertainty. 24
In contrast, the impact of LV, and visual uncertainty more broadly, on explicit sensorimotor strategies remains unknown. This gap is surprising given that, like implicit adaptation, explicit strategies also heavily rely on visual error signals to correct errors in everyday tasks such as adjusting to an unfamiliar computer trackpad or compensating for wind conditions during outdoor sports. In explicit adaptation, these signals are essential for formulating and implementing deliberate motor corrections. While this learning should in principle be sensitive to degraded visual function, the extent of this sensitivity is unknown.
This lack of insight into how LV affects explicit adaptation carries important clinical implications. As people age, their probability of both sensorimotor dysfunction and visual impairment increase.25-28 Explicit adaptation is foundational to rehabilitation, where patients are often taught to deliberately compensate for sensorimotor deficits—such as muscle weakness or limb disuse—following neurological injury.29,30 We hypothesized that LV would also impair explicit adaptation, reducing participants’ ability to form and implement deliberate compensatory strategies. Testing this hypothesis is essential for advancing our mechanistic understanding of motor adaptation and for optimizing interventions that rely on strategic compensation to help individuals with LV regain motor control lost due to degraded central vision (eg, age-related macular degeneration) or peripheral vision (eg, glaucoma and retinitis pigmentosa).
To address this gap, we recruited individuals with LV and age-matched controls to perform a visuomotor adaptation task designed to isolate 2 components of strategic adaptation: the discovery of a new strategy and the recall of a previously learned one.31-33 Contrary to our original hypothesis, performance in the 2 groups was highly similar across both learning components, indicating that explicit adaptation remains largely preserved despite visual impairment. Together with previous work, these findings identify explicit adaptation as a viable mechanism to guide rehabilitative strategies for individuals with a combination of motor dysfunction and visual impairment.
Methods
Ethics Statement
All participants gave written informed consent in accordance with policies approved by the UC Berkeley Institutional Review Board. Participation in the study was in exchange for monetary compensation.
Participants
The sample size was determined by availability rather than statistical power considerations. Our sole inclusion criterion was visual acuity worse than 20/50 (0.4 logMAR) in the better-seeing eye, uncorrectable with refractive aids (ie, glasses or contact lenses), as confirmed by clinical records. Participants were recruited through Meredith W. Morgan University Eye Center and a registry maintained by the National Research & Training Center on Blindness and LV (Table 1). Those who consented completed a phone-based screening, including a medical history review and the Montreal Cognitive Assessment-Blind (MoCA-Blind) to assess general cognitive function. In the LV group, visual acuity in the better-seeing eye had a mean of 20/195.0 (standard deviation [SD] = 145.8). Of these participants, 13 had congenital visual impairments and 10 had acquired impairments.
Demographic Information for Participants in the Low Vision and Control Groups.
We also recruited 23 age-matched controls from the UC Berkeley community (Table 1). All control participants had normal or corrected-to-normal vision. As intended, the LV and control groups did not differ significantly in age (t(44) = 0.06, P = .95), handedness (χ²(1) = 0, P = 1), or sex (χ²(1) = 0, P = 1). Although the LV group had, on average, 1 year less education (t(44) = 2.7, P = .01), cognitive ability—as measured by the MoCA-Blind—did not differ between groups (t(37) = 1.0, P = .33).
Apparatus
Participants used their own laptop computer to access a custom webpage hosting the experiment34,35 and performed the motor learning task using a trackpad (sampling rate ~60 Hz; Figure 1(A)). The size and position of stimuli were scaled based on each participant’s screen size/resolution, which was automatically detected. As such, any differences in screen size and screen magnification were accounted for between individuals. For reference, the parameters reported below correspond to a 13″ diagonal monitor. Participants were instructed to sit at a comfortable viewing distance from the screen (approximately arm’s length) and confirmed compliance prior to the task.
No association between low vision (LV) and impaired explicit motor adaptation. (A) Experiment setup. Participants used a trackpad to make rapid, goal-directed movements toward a visual target presented on a screen. (B) Delayed feedback task. After baseline trials with veridical feedback (cycles 1-10), participants completed a visuomotor rotation task with feedback rotated 60° clockwise or counterclockwise (counterbalanced across participants). Endpoint feedback was delayed by 800 ms—a manipulation known to suppress implicit adaptation and isolate explicit strategy use. Schematics illustrate hand and cursor positions during early adaptation (Discovery: cycles 7-16; Recall: cycles 47-56), late adaptation (Discovery: cycles 31-40; Recall: cycles 66-75), and the aftereffect phase (Discovery: cycles 41-45; Recall: cycles 76-80). (C-F) All participants. (C) Median hand angle (relative to the target at 0°) across cycles for Control (green) and LV (purple) groups; shaded areas denote ±1 standard error of the mean. (D) Boxplots of hand angle during the Discovery block across Early, Late, and Aftereffect phases. (E) Same measures shown for the Recall block. (F) Recall ratio, calculated as Early Recall/Late Discovery hand angle, quantifying strategic recall. (G-J) Learners only (participants whose late adaptation significantly exceeded 0°). Same analyses as above. Boxplots show median (solid line), mean (dashed line), interquartile range, and full range (excluding outliers). Individual dots reflect participant-level data.
We note that, unlike our laboratory-based setup—where vision of the reaching hand is occluded—this was not feasible in the present online testing protocol. That said, we have previously shown that measures of both implicit and explicit adaptation are highly comparable between in-person and online settings.26,35,36 Moreover, based on informal observations, participants appeared to remain focused on the screen throughout the experiment to monitor target location and task performance, such that vision of the hand was largely confined to the visual periphery. Throughout the 1-hour session, the experimenter remained available by phone to provide instructions and real-time support.
General Procedures
Participants performed reaching movements across a virtual workspace on their screen. Each trial involved a movement from the center of the workspace (white circle, 0.5 cm diameter) to a visual target (blue circle, 0.5 cm diameter). The radial distance from the center to the target was 6 cm. Targets appeared at 1 of 2 locations: 60° (upper right) or 210° (lower left).
Each trial began when the participant moved their cursor into the starting circle. After the cursor remained in the start circle for 500 ms, the blue target appeared. Participants were instructed to make a rapid, slicing movement through the target following an auditory go-cue (a single beep). A delay of 1200 ms (±100 ms jitter) was imposed between target appearance and the auditory go-cue to standardize movement preparation time across participants and minimize speed–accuracy trade-offs. If participants moved before the go-cue, they heard the message “Wait for the tone!” If they failed to initiate movement within 800 ms after the cue, they heard “Move earlier!”
The cursor disappeared as soon as the hand left the start circle. Visual feedback during center-out movements took 1 of 3 forms: veridical, rotated, or no feedback. In veridical feedback trials, the cursor appeared at the movement endpoint and accurately reflected the hand’s direction (Figure 1(B)). In rotated feedback trials, the cursor was shown at the endpoint but offset by 60° from the actual movement angle once the movement distance exceeded 6 cm, the target distance. The direction of rotation (clockwise or counterclockwise) was counterbalanced across participants. In no-feedback trials, the cursor remained off throughout the movement. To prevent feedback from influencing the return movement to start the next trial, the cursor was only visible within 2 cm of the start location. A single trial consisted of 1 complete outward movement from the start circle to a target. A movement cycle comprised 2 center-out trials: 1 to each of the 2 target locations (60° and 210°), with target order randomized within each cycle.
Experimental Design
First, participants watched an instructional video introducing key task features, including the go-cue and delayed cursor feedback. Second, they completed 15 practice trials to familiarize themselves with the web-based reaching environment and both veridical and no-feedback conditions. Third, participants completed the main task consisting of 6 blocks separated into 2 phases: initial exposure to a perturbation (assessing Discovery) and re-exposure (assessing Recall). Impaired discovery would manifest as reduced performance in the first phase, while impaired recall would emerge in the second. This design allows us to test how visual uncertainty influences the strategic processes that support sensorimotor learning. In the Discovery phase, they were first introduced to the adaptation paradigm in 3 blocks: baseline with veridical feedback (5 cycles), rotated feedback for strategy discovery (30 cycles), no-feedback aftereffect block (5 cycles). This phase was immediately followed by an identical Recall phase comprising the same 3 blocks. Across both phases, this totaled to 80 movement cycles across 2 targets = 160 trials total.
Before each veridical feedback block, participants were instructed: “Please move your white cursor directly to the blue target immediately after the tone.” Before each rotated feedback block, they were told: “Your white cursor will appear at an offset from your movement. To hit the blue target with your white cursor, please reach somewhere different than the blue target immediately after the tone.” Prior to the no-feedback blocks, the instruction was: “Your white cursor will be hidden and no longer offset from where you moved. Please move directly and immediately to the blue target after the tone.” The experimenter remained on the phone and assessed comprehension by prompting participants to restate the instructions in their own words. No participants were excluded based on this understanding check.
Data Analysis
All data and statistical analyses will be performed in R. The primary dependent variable was the endpoint movement angle on each trial (ie, the angle of the cursor when the movement amplitude reached a 6 cm radial distance from the start position relative to the location of the target). Because no target-specific differences were expected, movement angles were averaged across the 2 target locations to simplify visualization.
We compared movement angle between groups in 3 a priori defined epochs 31 : Early adaptation, late adaptation, and aftereffect. These epochs were examined separately in the Discovery and Recall phases. Early adaptation was defined as the initial 10 movement cycles after the rotation was introduced (Discovery: cycles 7-16; Recall: cycles 47-56). Late adaptation was defined as the final 10 movement cycles of the rotation blocks (Discovery: cycles 31-40; Recall: cycles 66-75). Aftereffect was defined as all movement cycles without visual feedback after the rotation was removed (following Discovery: cycles 41-45; following Recall: cycles 76-80).
We used F-tests with the Satterthwaite approximation to assess the significance of coefficients (beta values) from linear mixed-effects models (R functions: lmer, lmerTest, ANOVA, and emmeans). Pairwise post hoc comparisons were conducted using 2-tailed t-tests or Wilcoxon signed-rank tests when parametric assumptions were violated. P-values were adjusted for multiple comparisons using the Tukey method. When variances between groups were unequal, degrees of freedom were adjusted accordingly.
Data Availability Statement
All data and analysis scripts generated in this study will be made publicly available upon publication.
Results
No Association Between LV and Impaired Explicit Motor Adaptation
Following an initial veridical feedback baseline block to familiarize participants with the task environment and apparatus, the feedback cursor was rotated by 60°. To compensate for this rotation, both groups exhibited significant movement angle changes in the opposite direction of the rotation, drawing the cursor closer to the target (Figure 1(C)). When participants were asked to forgo their strategies and re-aim back to the target during the initial no-feedback aftereffect block, both groups were able to “switch-off” their strategies and successfully re-aim back to the target. Both groups exhibited minimal aftereffects, confirming that our delayed feedback manipulation was successful in eliminating implicit adaptation.32,33 Upon re-exposure to the rotation in the Recall block, participants were able to rapidly retrieve their re-aiming strategy and subsequently switch back to aiming directly to the target in the second no-feedback aftereffect block.
The data for both groups plotted in Figure 1(C) appears qualitatively similar, suggesting that LV was not associated with impacts on the discovery or recall of an explicit strategy. To statistically evaluate performance, we focused on 3 a priori-defined phases: early adaptation, late adaptation, and aftereffect (Figure 1(C)-(E)). Consistent with this interpretation, we found no significant main effect of group (F(1, 222) = 0.2, P = 0.69), nor any significant interactions involving group (all Ps > .62), indicating that performance did not differ between LV and Control participants across any phase or block. A complementary Bayesian analysis yielded a Bayes Factor of 5.6 in favor of the null, providing substantial evidence against a group difference. As shown in Figure 1(C), the LV group’s learning curves closely mirrored that of the Control group.
Across both groups, we observed the expected hallmarks of explicit adaptation. There was a significant main effect of phase (F(3, 308) = 24.6, P < .0001) and a significant interaction between phase and block (F(3, 308) = 8.4, P < .0001), indicating that movement angles varied across phases in a block-dependent manner: In the Discovery phase (Figure 1(D)), participants exhibited modest early adaptation (8.9° [2.8°, 14.9°]; t(306) = 2.1, P = .14), followed by a substantial increase in late adaptation (34.9° [28.9°, 41.0°]; t(306) = 9.3, P < .001). Aftereffects were minimal (−0.7° [−6.8°, 5.3°]; t(308) = 0.48, P = .96), suggesting that learning was dominated by explicit strategies with little to no implicit adaptation. In the Recall phase (Figure 1(E)), participants adapted rapidly from the outset. Early adaptation was robust (38.3° [32.2°, 44.3°]; t(306) = 10.4, P < .001) and remained high during late adaptation (39.5° [33.5°, 45.6°]; t(306) = 10.8, P < .001). As in the Discovery phase, aftereffects remained minimal (1.5° [−4.5°, 7.5°]; t(306) = 0.34, P = .99).
Post hoc comparisons confirmed significantly greater early adaptation in the Recall phase compared to Discovery (t(308) = 29.4, P < .001), reflecting the classic signature of “savings”—an accelerated re-expression of a previously learned strategy.37-40 However, late adaptation did not differ between blocks (t(308) = 1.3, P = .92), nor did aftereffects (t(308) = 0.6, P = .99). These findings suggest that although participants rapidly retrieved their explicit strategies during the recall block, overall learning remained unchanged, and implicit adaptation was not engaged in either case.
Recognizing that movement angle alone may not fully capture recall (given variability in strategy use during the initial exposure), we conducted a complementary analysis. Specifically, we computed a “recall ratio,” defined as the movement angle during early adaptation in the Recall phase normalized by the angle during late adaptation in the Discovery phase. A ratio of 1 indicates complete recall of the previously learned strategy, values below 1 reflect partial recall, and values above 1 suggest the use of a larger aiming strategy upon re-exposure to the perturbation.
Using this measure, both groups demonstrated strong recall (Figure 1(F)). The Control group had a median recall ratio of 1.0 (Interquartile Range = 0.9-1.1; Wilcoxon test, Recall Ratio vs 0: V = 251, P < .001), and the LV group also had a median recall ratio of 1.0 (IQR = 0.9-1.8; V = 235, P = .002). Importantly, the recall ratio did not differ significantly between groups (W = 307, P = .36), providing further evidence that strategic recall remains intact in individuals with LV.
As shown in Figure 1(D) to (F), participants showed substantial variability in both the discovery and recall of an explicit aiming strategy. Consistent with prior findings, a subset of individuals failed to adapt to the perturbation by the end of the Discovery block—that is, their late adaptation angles did not differ significantly from baseline. This included 8 participants in the Control group (35%) and 10 in the LV group (43%), with comparable proportions across groups (Fisher’s exact test, P = .76, odds ratio = 0.70). One interpretation is that these “non-learners” reflect participants who did not attend to the feedback or misunderstood task instructions. However, a more likely explanation is that these individuals attempted to adapt but were unable to discover an effective explicit strategy. This interpretation is supported by the close correspondence between the non-learner rates observed here and those reported in our prior studies of strategic adaptation in older adult cohorts comparable in age to the present sample (see review Cisneros et al 27 ). The mechanisms underlying these age-related limitations in strategy discovery remain an active focus of ongoing investigation.
Returning to our main question, it is possible that inclusion of non-learner data may obscure potential group differences in strategy use, as recall measures are difficult to interpret when no explicit strategy was successfully established during learning. We thus performed a secondary analysis limited to “learners,” defined as participants whose movement angle during the late adaptation epoch of the Discovery block was significantly greater than zero. This subset included 15 participants from the Control group and 13 from the LV group: Our key findings were replicated (Figure 1(G)-(J)). There was no main effect of group (F(1, 202) = 0.01, P = .92) and no significant interactions involving group (all Ps > .36), across all phases. As before, both groups showed strong recall (IQR, Control: 1.0-1.0; LV: 0.9-1.1), with no significant difference between them (W = 101, P = .89), reaffirming that LV had minimal impact on the ability to discover and recall an explicit aiming strategy.
Impaired Visually Guided Motor Control in Individuals With LV
Given that our primary finding is a null effect, it is important to consider whether the participants with LV truly differed in the quality of their visually guided motor actions in this paradigm. To evaluate visuomotor performance, we analyzed kinematic behavior during the baseline phase of the task. As shown in Figure 2, there were no significant group differences in movement time during the ballistic phase of the reach (Control: 401.5 ± 37.9 ms; LV: 373.6 ± 49.9 ms; W = 235, P = .53). In contrast, baseline movement angle variability around the visual target was significantly greater in the LV group compared with controls (movement angle SD: Control: 7.6° ± 0.8°; LV: 8.2° ± 0.4°; W = 142, P = .04), indicating reduced movement consistency when visual input was degraded.
Low vision (LV) is associated with increased movement variability toward the visual target and slowing of visually guided return movements. (A) Movement time (B) movement angle variability (ie, standard deviation of movement angles), and (C) return-to-start position times during baseline no-feedback trials in individuals with LV (purple) compared with matched controls (green). Boxplots show median (solid line), interquartile range, and full range (excluding outliers). Individual dots reflect participant-level data. Dots represent individual participants. Asterisks indicate P < .05.
Following the ballistic reach, participants were required to visually guide the cursor back to the central start position. Return-to-start times were significantly longer in individuals with LV than in controls (Control: 7.6° ± 0.8°; LV: 8.2° ± c0.4°; W = 159, P = .03), revealing an impairment in visually guided motor control.
Given these baseline differences, we repeated our between-group analyses of explicit adaptation while systematically including movement angle variability and return-to-start time as covariates. Critically, there was no significant main effect of Group in either model (movement angle SD as a covariate: F = 0.2, P = .64; return-to-start time as a covariate: F = 0.22, P = .64). Thus, despite exhibiting greater movement variability and slower visually guided movements, individuals with LV showed no measurable deficit in explicit motor adaptation.
Discussion
LV is known to impair the ability to discriminate the spatial position of visual objects,41,42 leading to less accurate goal-directed movements.41,43-48 However, the impact of LV on motor adaptation, particularly explicit strategy use, remains poorly understood. To address this, we used a visuomotor adaptation task specifically designed to isolate explicit re-aiming. Strikingly, LV had no measurable effect on strategy use, during either initial discovery or later recall. Despite visual impairments, individuals with LV were just as effective as controls in forming and retrieving a deliberate, compensatory strategy.
When considering null effects, it is always important to vet whether the study design had the desired impact. Importantly, in a closely matched cohort of individuals with LV, we previously observed a marked impairment in implicit adaptation. 24 The presence of a robust deficit in implicit adaptation in prior work, alongside preserved explicit adaptation in the present study, argues against a general insensitivity of our methods. Instead, these findings challenge our original hypothesis that degraded visual input uniformly impairs all forms of visuomotor learning, demonstrating that explicit strategy use may be more resilient to visual degradation. This dissociation between the effects of visual uncertainty on implicit and explicit processes adds to a growing body of evidence suggesting that these learning systems rely on distinct computational and neural substrates.4,27,49
Several factors may explain why explicit strategy use remained intact in individuals with LV. One possibility is that chronic visual uncertainty engages compensatory mechanisms that preserve strategic control (particularly in the face of impaired implicit processes underlying motor adaptation) similar to what we previously observed in individuals with proprioceptive deafferentation. 50 Specifically, individuals with LV may learn to rely more heavily on intact regions of their visual field51,52 or develop heightened neural sensitivity to visual errors over time. 53
Alternatively, intact strategy use may reflect task conditions in which visual errors were large, unambiguous, and well above perceptual thresholds. We deliberately employed a large 60° rotation, as such errors most reliably elicit explicit strategy use both in laboratory settings and in everyday behavior. 1 This choice also avoided commonly used rotations (eg, 45° or 90°), for which participants may have developed pre-existing heuristics that could accelerate strategy discovery. However, it is possible that in more subtle conditions, where errors are smaller or lower in contrast, 14 strategy discovery in individuals with LV may be compromised. Future studies manipulating perturbation magnitude and feedback salience will be important for determining the conditions under which visual impairment may impact strategy use.
Clinically, the finding that strategy use remains largely intact in LV, and perhaps more broadly, resilient to visual uncertainty, has important implications. Explicit strategies are central to many rehabilitation protocols, where patients with movement disorders are taught to consciously compensate for changes in body state (eg, muscle weakness) or environment (eg, prosthetic use) following neurological injury.29,30,54,55 Our results suggest that even individuals with impaired vision (such as those affected by stroke to visual cortex, traumatic brain injury, or central neurodegenerative disease) may benefit from strategy-based interventions. Rehabilitation specialists may leverage this preserved capacity by providing clear, goal-oriented instructions and emphasizing task-relevant errors (for example, by making them more salient (eg, larger, brighter, or color-coded). Such approaches might promote effective strategic compensation when sensory feedback is limited.
Nonetheless, several limitations warrant consideration. First, the absence of group differences in explicit adaptation may reflect limits in severity; more profound visual impairments may be required to disrupt strategic learning. That said, our sample spanned a broad range of clinically diagnosed LV conditions and functional impairments, capturing substantial heterogeneity in visual loss. All participants had documented clinical diagnoses indicating meaningful visual impairment, defined by uncorrectable visual acuity worse than 20/50, with many at or below 20/200—a threshold commonly used to define legal blindness.
Second, visual function was characterized using relatively coarse clinical criteria rather than fine-grained psychophysical measures. Without additional assessments—such as contrast sensitivity, visual field mapping, or task-specific psychophysical thresholds (eg, discriminating whether the cursor lay to the left or right of the target)—we cannot fully quantify individual variability in visual uncertainty within the LV group. Incorporating such measures would strengthen future work. At the same time, psychophysical tests primarily assess visual perception in isolation, whereas the behavioral measures reported here (movement variability and return-to-start time) directly probe the functional use of vision during visually guided reaching. In this sense, these measures provide a more ecologically relevant demonstration of visual impairment within this cohort.
Third, the experiments were conducted remotely, limiting our ability to fully control and standardize the testing environment. This approach was essential for reaching individuals who could not easily travel to the laboratory.56,57 However, over the past several years, both our group and others have repeatedly demonstrated that core features of motor adaptation can be reliably replicated in online settings, providing confidence in the validity of this approach for studying sensorimotor learning.35,58-61 That said, converging evidence from future in-person studies with greater experimental control would further strengthen the present findings.
Additionally, we implemented multiple safeguards to maintain high data quality, consistent with best practices in online crowdsourcing research. 58 Participation was restricted to trackpad users to standardize input devices. Stimulus size and position were scaled to each participant’s screen dimensions, thereby accounting for individual differences in screen size and resolution. We also provided clear instructions regarding viewing distance (approximately arm’s length) to promote consistency across participants while preserving a naturalistic testing environment, in contrast to highly constrained laboratory setups (eg, bite bars). Analyses focused on group-level comparisons, for which individual sources of variability (eg, screen size and refresh rate) are expected to average out. Real-time phone support from the experimenter further helped ensure procedural consistency across participants. Finally, our results replicated hallmark features of explicit strategy use, such as minimal aftereffects 32 and rapid recall,37,38 that are routinely observed in laboratory-based studies, further supporting the validity of our online approach.
Together, these findings indicate that the explicit mechanisms supporting sensorimotor adaptation are resilient to common levels of visual impairment in individuals with LV. This resilience underscores the potential to leverage explicit, strategy-based learning in motor rehabilitation, even in clinical populations where visual input is degraded due to sensory or neural injury, such as stroke, traumatic brain injury, or other neurodegenerative diseases.
Footnotes
Author Contributions
Mihai Cipleu: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Validation; Visualization; Writing—original draft; and Writing—review & editing. Sritej Padmanabhan: Investigation; Project administration; Validation; and Writing—review & editing. Emily A. Cooper: Conceptualization; Methodology; Visualization; and Writing—review & editing. Jonathan S. Tsay: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; and Writing—original draft.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.