Motor differences in autism have long been recognized but poorly measured. Autistic children and adults tend to score lower on classic dexterity assessments such as the Purdue Pegboard test, inserting fewer pegs into small holes within a time limit with either hand. Maximum finger tapping speed is reduced, and grip strength tends to be smaller. Studies of walking have documented larger step-to-step variability and abnormal gait patterns in autistic children, while autistic adults show less stable postural control. Yet these observations have been gathered with a patchwork of different tests, expensive motion capture systems, and separate assessments for arms and legs, making it nearly impossible to ask whether the same underlying factor drives motor differences throughout the body.

The new study, led by Atsushi Takagi and Chihiro Itoi of NTT Communication Science Laboratories together with colleagues, set out to close that gap with a single, common framework. The team built on a motor assessment previously validated on more than 1,600 people aged three to eighty-eight, which measures how consistently a limb repeats a circular movement. In the test, participants hold a smartphone and trace a ten-centimeter circle on a guide sheet, completing one revolution per metronome beat at 2.5 hertz for the hands. For the feet, the phone is strapped in an armband above the ankle, and the participant swings the straight leg from the hip to trace a fifteen-centimeter circle on the floor at a slower 1.5 hertz pace. Each limb is tested for just fifteen seconds.

The technical trick lies in how the raw accelerometer signal is turned into a measure of skill. The three-axis acceleration data are first centered and normalized, then rotated using principal component analysis so that the circular motion lies mainly in a two-dimensional plane. Each revolution is identified by successive crossings of the movement phase, and the similarity between neighboring revolutions is quantified with the Hausdorff distance, a metric that is fast to compute and robust to differences in sampling. Averaging the Hausdorff distances across all adjacent revolution pairs yields a single variability score for each limb. When every circle looks alike, variability is low and control is judged robust; when each revolution wanders along a slightly different path, variability is high and control is less consistent.

Crucially, the researchers first confirmed that both groups kept tempo equally well. Seventeen clinically diagnosed autistic adults, with a mean age of twenty-eight, and twenty age- and IQ-matched comparison participants traced circles at statistically indistinguishable speeds for every limb. This mattered because movement variability is known to rise with speed, a relationship described since Fitts’s classic 1954 work, so any group difference in variability could only be interpreted fairly if speed was matched. Prior studies have also found that autistic individuals can keep tempo with a single-frequency external rhythm without difficulty, and the present results are consistent with that picture.

When variability itself was compared, a clear asymmetry emerged. For the hands, statistical analysis revealed significant effects of dominance, group, and their interaction. Follow-up tests showed that the autistic group’s non-dominant hand was significantly more variable than that of the comparison group, while the dominant hand showed no reliable difference. For the feet, the pattern was broader: the autistic group showed greater variability across both the dominant and non-dominant lower limbs, with no specific dominance interaction. In other words, the upper limb difference was confined to the non-dominant side, whereas the lower limb difference appeared globally. The autistic group also displayed significantly greater hand laterality, meaning the gap between their dominant and non-dominant hands was larger than in comparison participants.

Perhaps the most intriguing result came from linking the limbs together. Using a linear mixed-effects model, the researchers asked whether a person’s foot variability predicted their hand variability after accounting for group, dominance, and their interaction. It did: the association was positive, with a slope of 0.28, meaning that individuals with wobblier feet also tended to have wobblier hands. Notably, this relationship held equally in both groups, since adding a group-by-foot-variability interaction did not improve the model. The authors are careful to stress that this does not prove a causal link or a single shared neural mechanism, because the group effects differed between the upper and lower limbs. Still, it suggests that common factors, such as motor timing variability, sensory variability, or habitual limb use, may influence movement consistency throughout the body, coexisting with limb-specific effects.

The increased hand laterality in the autistic group came as a surprise. Earlier questionnaire studies and meta-analyses have suggested that autistic individuals are more likely to be mixed-handed, which led the team to predict smaller, not larger, differences between the two sides. Yet on the Edinburgh Handedness Inventory both groups reported equally strong right-handedness, and a slight difference in footedness on the Coren quotient vanished after correction for multiple comparisons. The authors propose that what people say about their hand preferences may diverge from how skillfully they actually use each hand, and that questionnaire measures suffer from ceiling effects that blunt their sensitivity. Motor-based assessments, by contrast, may capture real differences in lateralized skill that self-report misses.

Why would the non-dominant hand and both feet be selectively affected? The researchers weigh several possibilities. One candidate is heightened sensory variability: autistic individuals show larger trial-to-trial variability in sensory percepts, and tasks like peg insertion that demand tactile processing are disproportionately affected. But it is unclear why such a difference would localize to the non-dominant side alone. Another candidate is motor timing. Autistic children show reduced cerebellar activity and connectivity during finger tapping, and the cerebellum is critical for precise timing; timing variability is itself known to be larger in the non-dominant hand. A third possibility involves practice and generalization. Autistic children have been reported to use their non-dominant hand less often, and previous work suggests that motor learning in autism may generalize poorly across limbs, with internal models formed in a highly limb-specific way and an increased reliance on proprioceptive feedback. Skills refined through years of dominant-hand use may simply fail to transfer, leaving the non-dominant hand and the comparatively unfamiliar feet less robustly controlled. The authors caution that habitual use was not measured, so this remains a hypothesis.

The practical implications may prove the most consequential part of the story. Because the test requires only a smartphone, fifteen seconds of movement, and no laboratory equipment, it could be deployed in clinics, schools, or homes as a rapid, quantitative probe of coordination. Related work by the same group has linked circular leg movement variability to postural stability, suggesting the measure carries functional meaning beyond the task itself. The study does have limits: nearly all participants were right-handed, so laterality findings need testing across a range of handedness, and only adults were studied, leaving open when in development these differences emerge. Even so, the image of autistic motor control that emerges is more nuanced than a simple deficit. Dominant-hand skill is intact, tempo-keeping is intact, and yet the less-practiced corners of the motor repertoire betray a measurable, quantifiable difference, one that a device in nearly every pocket can now detect.

Subject of Research: Motor variability during circular movements as a measure of limb control differences in autistic adults

Article Title: Motor Variability of Circular Movements Reveals Differences in Non-Dominant Hand and Foot Control in Autistic Adults

Article References: Takagi, A., Itoi, C., Honda, K., & Kashino, M. (2026). Motor Variability of Circular Movements Reveals Differences in Non-Dominant Hand and Foot Control in Autistic Adults. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07546-3

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07546-3

Keywords: autism spectrum disorder, motor variability, sensorimotor control, smartphone accelerometer, non-dominant hand, lower limb control, laterality, Hausdorff distance, motor timing, cerebellum, clinical motor assessment, adults

The research tackles a long-standing question in sensorimotor science. When we reach for an object, our grip force rises in anticipation of its weight, a phenomenon known as anticipatory grip force control. If the prediction is right, the lift feels smooth and effortless. If it is wrong, the object either slips from our fingers or jerks upward with comical force. Decades of work have shown that these predictions are built from two ingredients: visual cues, most obviously the apparent size or volume of the object, and the memory of recent lifting experience. A large-looking bottle is expected to be heavy; a bottle that felt heavy last time is expected to feel heavy again.

That second ingredient turns out to be surprisingly dominant. In the Durham experiments, when participants lifted objects with no informative visual cues at all, their grip force behaviour was strongly biased by whatever they had lifted on the previous trial. This serial dependence, the tendency of the most recent experience to colour the next prediction, has been documented in perception before, but the new results show it exerting a powerful pull on motor planning itself. Left to its own devices, the lifting system essentially assumes the world has not changed since the last interaction.

The team measured this bias using peak grip force rate, or PGFR, the speed at which grip force climbs during the initial phase of a lift. PGFR is a well-established window into the brain’s internal heaviness predictions because it reflects what the motor system expected before tactile information arrived. Participants performed a precision-grip lifting task in which objects came in three heaviness levels, and the researchers tracked how PGFR shifted across four experimental conditions: a no-cue baseline, a familiar cue, and two kinds of novel cues.

The familiar cue was visual volume, the everyday signal that bigger means heavier. The novel cues were deliberately arbitrary: patterns of line orientations printed on the surfaces of the objects, with no natural relationship to weight. Crucially, the researchers also split the novel cues by presentation format. In the intrinsic condition, the line patterns appeared physically on the objects themselves, much as a label might. In the extrinsic condition, participants saw diagrammatic depictions of the same objects and patterns on a screen, meaning the weight information was available only through an interpretive step rather than being directly attached to the thing being lifted.

Before running the study, the authors had reasoned that the familiar volume cue might carry more predictive weight than an arbitrary pattern, simply because a lifetime of experience has calibrated the size-weight relationship. They also suspected that cues printed on the object might outperform cues shown on a screen, since intrinsic presentation requires no mapping between a display and the physical world. Neither prediction survived contact with the data. The familiar volume cue did not exert a stronger influence than the novel cues, and the intrinsic presentation offered no measurable advantage over the extrinsic one.

What did matter was simply having a cue at all. All three visual cue conditions significantly reduced the serial dependence bias compared with the no-cue baseline. In other words, any reliable visual signal about weight, whether natural or arbitrary, on-object or on-screen, was enough to loosen the grip of recent experience and let the motor system plan each lift on its own terms. The computational modelling accompanying the behavioural data, with individual cue-weighting parameters fitted for each participant, supported the same conclusion: novel cues were integrated into the heaviness prediction with weights comparable to those of the familiar cue.

The speed of this learning is the most striking aspect of the findings. Participants did not need weeks of training to make an arbitrary line orientation meaningful. The sensorimotor system, often portrayed as conservative and heavily reliant on long-term calibration, absorbed the new cue rapidly and deployed it in service of grip force planning without any apparent performance penalty. This suggests that the machinery of motor prediction is less wedded to specific cue types than theories emphasising learned cue familiarity might imply, and more open to whatever statistical regularities the environment currently offers.

The equivalence of intrinsic and extrinsic presentation carries equally interesting implications. Much of the emerging field of sensory augmentation assumes that information delivered through artificial channels, such as a display or a wearable device, will be harder to incorporate than information arriving through the body’s native senses. These results suggest that, at least for weight prediction in a controlled lifting task, the brain treats a diagrammatic on-screen cue as a perfectly serviceable predictor. The interpretive distance between a screen and an object did not dilute the cue’s influence on motor planning.

The practical implications extend beyond the laboratory. In industrial settings, medical environments, logistics and teleoperation, workers routinely handle objects whose weight cannot be judged by appearance, from sealed containers to virtual or robotic proxies. The findings suggest that equipping such environments with arbitrary visual markers, even simple line patterns shown on a monitor, could rapidly improve the safety and efficiency of manual handling by steadying grip force against the misleading pull of recent experience. For designers of augmented reality interfaces, the message is encouraging: the human motor system appears ready to trust whatever weight information you show it, almost immediately, and without demanding that the information be painted onto the object itself. As the authors note, the work highlights the flexibility of sensorimotor control and may inform manual interactions in environments with limited or unconventional weight predictors, a description that fits an increasing share of the technological world we now lift, carry and manipulate every day.

Subject of Research: How novel visual cues are learned to update internal predictions of object weight during lifting

Article Title: Learning a novel visual cue to update internal predictions of object weight

Article References: Learning a novel visual cue to update internal predictions of object weight. (n.d.). https://doi.org/10.1186/s12915-026-02741-1

Image Credits: AI Generated

DOI: 10.1186/s12915-026-02741-1

Keywords: grip force, motor planning, visual cues, object weight, serial dependence, sensorimotor control, anticipatory control, precision grip, sensory augmentation, psychophysics, BMC Biology, Durham University

Gait initiation is a deceptively complex act. The nervous system must shift the body’s center of mass forward and toward the swing limb, unweight the trailing leg, and generate carefully sequenced bursts of muscle activity, all within a few hundred milliseconds. During this window, sensory receptors embedded in muscles, tendons, joints, and skin fire continuously, informing the central nervous system about joint position, movement velocity, and load. Researchers have long measured how quickly muscles respond to perturbations in people with chronic ankle instability, but far less attention has been paid to the afferent side of the circuit — the incoming stream of proprioceptive signals that arrives before any muscle can react. Because direct recordings of these nerve signals in humans are ethically and technically impractical, the research team turned to a computational approach.

The study combined detailed biomechanical analysis of gait initiation with a neurophysiological model of proprioceptor behavior. Participants, including individuals with chronic ankle instability and healthy controls, walked in a motion capture laboratory equipped with force plates that recorded the ground reaction forces beneath each foot. From the movement trajectories and forces, the researchers reconstructed the mechanical events inside and around the ankle joint: changes in joint angle, angular velocity, muscle fascicle length, and the deformation of skin and joint capsules. These mechanical variables were then fed into mathematical models describing how different classes of proprioceptive afferents translate mechanical deformation into trains of neural impulses.

The models captured the behavior of several key sensory populations. Muscle spindles, the primary detectors of muscle length and stretch velocity, were modeled with their characteristic dynamic and static response components. Golgi tendon organs, which signal tensile load within tendons, were modeled as force-sensitive elements. Cutaneous and joint mechanoreceptors, which fire in response to skin stretch and joint capsule deformation, were included as well. Each model produced an estimate of instantaneous firing rate — effectively, a simulated neurogram of what the ankle’s sensory apparatus was telling the spinal cord and brain at every millisecond of the gait initiation sequence.

The central finding is striking: in individuals with chronic ankle instability, the estimated timing of proprioceptive afferent activity was systematically altered during the anticipatory postural phase of gait initiation. Whereas healthy participants showed well-organized, temporally structured afferent volleys that preceded and accompanied the postural adjustments needed to step forward, the unstable ankles displayed shifts and distortions in these signal patterns. In practical terms, the sensory information reaching the nervous system about the ankle’s position and loading arrived in a different temporal pattern than in uninjured joints. Because the nervous system depends on precisely timed sensory feedback to calibrate motor commands, even modest shifts in afferent timing could degrade the coordination of the postural adjustments that keep the body balanced during the transition to stepping.

The researchers emphasize that the disruption is not simply a matter of weaker signals. The models suggested changes in the relative timing between different receptor populations, meaning that the brain may receive internally inconsistent information about what the ankle is doing. Muscle spindle signals indicating one joint position may arrive in a different relationship to cutaneous signals than the nervous system has learned to expect. This form of sensory reweighting or desynchronization is thought to be a hallmark of the maladaptations that follow ligament injury, when damaged mechanoreceptors in the torn ligament are lost and remaining receptors must compensate — often imperfectly — for the missing input.

What makes this approach particularly powerful is that it opens a window onto a process that has been essentially invisible in human experiments. Traditional assessments of proprioception in chronic ankle instability, such as joint position matching tasks or thresholds for detecting passive movement, measure perception at one static moment in time. They cannot capture the rapid, continuous stream of sensory traffic that flows during movement. By deriving afferent firing patterns from movement data through validated receptor models, the study provides a dynamic, millisecond-by-millisecond estimate of sensory signaling during a functional task. This is a fundamentally different lens from reactive measures such as reaction times or postural sway scores, which reflect the end result of sensory-motor integration rather than its incoming raw material.

The clinical implications are potentially significant. Rehabilitation programs for chronic ankle instability currently emphasize balance training, strengthening, and perturbation-based exercises, which are effective for many but not all patients. If the root of the problem lies in altered afferent timing during the anticipatory phase of movement, then therapies could be designed specifically to recalibrate sensory signaling. Tasks that demand precise ankle positioning under time pressure, or training environments that manipulate the relationship between movement and sensory feedback, might encourage the nervous system to relearn the temporal patterns it lost after injury. The modeling framework also suggests new outcome measures: instead of relying solely on questionnaires and postural tests, clinicians could one day track afferent timing metrics derived from inexpensive motion capture to quantify sensory recovery objectively.

The study also fits into a broader scientific movement that treats sensory timing as a central currency of motor control. Research across domains, from upper limb reaching to locomotion in older adults, has shown that the nervous system is exquisitely sensitive to delays and variability in sensory feedback. Even artificial delays of tens of milliseconds can disrupt motor learning and adaptation. In this light, chronic ankle instability can be understood not merely as a mechanical joint problem or a strength deficit, but as a disorder of sensorimotor timing — a failure of the dialogue between the ankle and the spinal cord to keep the conversation synchronized during the most demanding phases of movement.

There are, of course, important caveats. The afferent patterns in this study are model-derived estimates, not direct neural recordings, and their accuracy depends on the fidelity of the underlying neurophysiological models and the biomechanical reconstructions. Individual variability in anatomy, injury history, and compensation strategies means that the group-level patterns reported here may not apply uniformly to every patient. The researchers also note that gait initiation, while clinically relevant, is only one of many tasks in which altered afferent timing could play a role; landing, cutting, and uneven-terrain walking may each impose distinct sensory demands. Future work combining these modeling techniques with other modalities, such as electroencephalography or high-density electromyography, could trace how altered afferent timing propagates through the central nervous system to produce the characteristic motor deficits of the condition.

Nevertheless, the study marks an important step toward a mechanistic, quantitative understanding of one of sports medicine’s most stubborn problems. By converting observable movement into estimated neural signaling, it transforms chronic ankle instability from a fuzzy clinical label into a measurable pattern of sensory disruption with specific temporal signatures. If subsequent studies confirm and extend these findings, the humble ankle sprain — so often dismissed as a trivial injury — may finally be understood at the level of the nerve impulses it deranges, and treated accordingly.

Subject of Research: Model-derived proprioceptive afferent timing during gait initiation in chronic ankle instability

Article Title: Model-derived proprioceptive afferent timing during gait initiation in chronic ankle instability

Article References: Bijari, K., Sadeghi, H., Yousefi, M., & Yiou, E. (2026). Model-derived proprioceptive afferent timing during gait initiation in chronic ankle instability. Scientific Reports. https://doi.org/10.1038/s41598-026-71074-4

Image Credits: AI Generated

DOI: 10.1038/s41598-026-71074-4

Keywords: chronic ankle instability, proprioception, gait initiation, afferent timing, muscle spindles, Golgi tendon organs, sensorimotor control, computational modeling, biomechanics, ankle sprain, postural control, neurogram