Biology subjects

Dowdall, L.

Publications and source records attributed to Dowdall, L..

4 recordsLinked to original sources

bioRxiv2025

Motor Learning Outside the Body: Broad Skill Generalisation with an Extra Robotic Limb

Our ability to transfer motor skills across tools and contexts is what makes modern technology usable. The success of motor augmentation devices, such as supernumerary robotic limbs, hinges on users capacity for generalised motor performance. We trained participants over seven days to use an extra robotic thumb (Third Thumb, Dani Clode Design), worn on the right hand and controlled via the toes. We tested whether motor learning was confined to the specific tasks and body parts involved in controlling and interacting with the Third Thumb, or whether it could generalise beyond them. Participants showed broad skill generalisation across tasks, body postures, and even when either the Third Thumb or the controller was reassigned to a different body part, suggesting the development of abstract, body-independent motor representations. Training also reduced cognitive demands and increased the sense of agency over the device. However, participants still preferred using their biological hand over the Third Thumb when given the option, suggesting that factors beyond motor skill generalisation, cognitive effort, and embodiment must be addressed to support the real-world adoption of such technologies.

neuroscience↗

bioRxiv2025

Muscle control of an extra robotic digit

Controlling an extra robotic finger requires the brain to adapt existing motor signals. While most current strategies exploit physical movement, there is growing interest in harnessing muscle activity directly via surface electromyography (EMG) as a more seamless interface. We systematically compared muscle- (EMG) and movement-based (force sensor) control of a Third Thumb. Using identical instructions and a counterbalanced within-participants design, we assessed initial skill, learning, and cognitive load across a variety of tasks, enabling a blinded comparison across control modalities. Both control modalities afforded successful Third Thumb control and learning, although force control consistently delivered better performance. Despite execution differences, learning rates and cognitive loads were comparable, with a similar evoked sense of agency. Signal analyses showed performance was predicted by real-time force sensor parameters but not by EMG, reflecting distinct control dynamics. Nonetheless, EMG training led to greater skill transfer to force control, suggesting it may better support generalisable learning. These findings challenge the assumption that proximity to neural signals ensures better control. Although EMG underperformed in execution, it showed unique advantages, including enhanced generalisation and access to richer signals, highlighting the need for improved real-time decoding to fully exploit its potential.

neuroscience↗

bioRxiv2025

Developing a Sensory Representation of an Artificial Body Part

Somatosensory feedback is essential for motor control, yet artificial limbs are thought to lack such feedback. We investigated how the body and brain gather informative sensory signals from a wearable augmentation interface (a robotic digit for motor augmentation), and whether naturally-mediated feedback can support technological embodiment. Participants intuitively interpreted natural feedback across perceptual tasks, performing comparably to state-of-the-art artificial feedback systems. fMRI revealed an immediate and distinct, topographically-organised representation of the robotic digit. After longitudinal training, this representation was further refined, becoming more similar to the biological digits, which correlated with increased subjective somatosensory embodiment. Our findings demonstrate that wearable devices naturally provide a powerful source of feedback which is immediately integrated with our body. Long-term use can then promote device-embodiment across the sensorimotor hierarchy.

neuroscience↗

bioRxiv2021

Somatosensory signals from the controllers of an extra robotic finger support motor learning

Considerable resources are being invested to provide bidirectional control of substitutive and augmentative motor interfaces through artificial somatosensory feedback. Here, we investigated whether intrinsic somatosensory information, from body part(s) proportionally controlling an augmentation device, can be utilised to infer the devices state and position, to better support motor control and learning. In a placebo-controlled design, we used local anaesthetic to attenuate somatosensory inputs to the big toes while participants learned to operate a toe-controlled robotic extra finger (Third Thumb) using pressure sensors. Motor learning outcomes were compared against a control group who received sham anaesthetic. The availability of somatosensory cues about the amount of exerted pressure generally facilitated acquisition, retention and transfer of motor skills, and performance under cognitive load. Motor performance was not impaired by anaesthesia when tasks involved close collaboration with the biological fingers, indicating that the brain could close the gap of the missing pressure signals by alternative means, including feedback from other body parts involved in the motor task. Together, our findings demonstrate that there are intrinsic natural avenues to provide surrogate position information to support motor control of an artificial body part, beyond artificial extrinsic signalling.

neuroscience↗