Biology subjects

Henriques, D. Y.

Publications and source records attributed to Henriques, D. Y..

3 recordsLinked to original sources

bioRxiv2025

Tool and hand adaptation and localization in immersive virtual reality

The human brain readily adapts movements to achieve motor goals. Most visuomotor adaptation studies use planar reaching with a cursor, where misaligned feedback leads to compensatory adjustments, reach aftereffects, and shifts in perceived hand location. Whether these effects extend to more natural settings and tool use remains unclear. In the Hand Experiment, we showed that in immersive virtual reality (VR), adaptation to 30{degrees} and 60{degrees} visuomotor rotations produced robust reach aftereffects and the expected shifts in hand localization. In the Pen Experiment, we extended this paradigm to a familiar hand-held tool, assessing localization of both the tool tip and the hand. Adaptation with the pen induced comparable or greater recalibration effects than with the hand-cursor, including shifts in both perceived tool and hand position. These findings demonstrate that visuomotor adaptation in immersive VR generalizes beyond cursor-based tasks, revealing how the sensorimotor system recalibrates internal representations of both the body and tools in realistic 3D environments. Author summary for PLOS ONEThis study shows that when people adapt their movements in virtual reality, the brain recalibrates not only the sensed position of the hand but also of familiar tools, highlighting how we update body and tool representations in everyday-like environments.

neuroscience↗

bioRxiv2025

Precision of reaches and proprioception in motor control and adaptation

How do precision of movement and proprioception influence motor control and adaptation? Several theories--such as the exploration-exploitation hypothesis--propose that variability plays a key role in motor performance and learning. However, empirical measures of motor and proprioceptive precision are often limited by small sample sizes, and proprioceptive estimates, especially those relying on efferent signals, are difficult to isolate and quantify. In this study, we leveraged a large dataset of 270 participants--including a subsample of older adults (ages 54- 84)--to assess the precision of hand movements and proprioceptive estimates, and to examine whether these factors predict individual differences in motor learning and adaptation. We found that baseline reach variance did not predict learning or changes in hand localization. Although active hand localization (which includes efferent contributions) was slightly more precise--showing an 8.6% reduction in variance--this suggests that unseen hand estimates rely primarily on proprioception. Neither motor nor sensory precision varied with age. However, reach aftereffects were modestly associated with proprioceptive precision before training and proprioceptive recalibration after training. No other measure of learning or variance was reliably associated. These findings suggest that reach aftereffects may partly reflect changes in hand proprioception, but overall, we identified no predictors of adaptation to a rotated visual cursor.

neuroscience↗

bioRxiv2018

Motor Learning Without Moving: Proprioceptive and Predictive Hand Localization After Passive Visuoproprioceptive Discrepancy Training

An accurate estimate of limb position is necessary for movement planning, before and after motor learning. Where we localize our unseen hand after a reach depends on felt hand position, or proprioception, but in studies and theories on motor adaptation this is quite often neglected in favour of predicted sensory consequences based on efference copies of motor commands. Both sources of information should contribute, so here we set out to further investigate how much of hand localization depends on proprioception and how much on predicted sensory consequences. We use a training paradigm combining robot controlled hand movements with rotated visual feedback that eliminates the possibility to update predicted sensory consequences ( exposure training), but still recalibrates proprioception, as well as a classic training paradigm with self-generated movements in another set of participants. After each kind of training we measure participants hand location estimates based on both efference-based predictions and afferent proprioceptive signals with self-generated hand movements ( active localization) as well as based on proprioception only with robot-generated movements ( passive localization). In the exposure training group, we find indistinguishable shifts in passive and active hand localization, but after classic training, active localization shifts more than passive, indicating a contribution from updated predicted sensory consequences. Both changes in open-loop reaches and hand localization are only slightly smaller after exposure training as compared to after classic training, confirming that proprioception plays a large role in estimating limb position and in planning movements, even after adaptation. (data: https://doi.org/10.17605/osf.io/zfdth, preprint: https://doi.org/10.1101/384941)

neuroscience↗