Biology subjects

Morehead, J. R.

Publications and source records attributed to Morehead, J. R..

9 recordsLinked to original sources

bioRxiv2025

An 'Aha!' moment precedes the strategic response to a visuomotor rotation

Strategic behaviour in sensorimotor adaptation tasks is typically described as either a gradual error minimisation process or as a process of learning through trial-and-error. The former predicts a gradual monotonic reduction in error, until some asymptote, while the latter predicts behavioural exploration to discover an efficacious solution. Another explanation is that a sufficiently rich understanding of the task culminates in an "Aha!" moment, and that this richer understanding naturally entails a new strategy. This predicts some period of perseveration in baseline behaviour, followed by an abrupt shift to a new strategic solution. To avoid obfuscation caused by the motor system, we first investigate these hypotheses in a strategy-only aiming game, where participants aim and fire a cannon at targets. Most participants exhibited a period of baseline perseveration followed by a single-trial shift to a distinct strategy. This strategy typically appears to produce the correct compensatory magnitude for a noisily estimated perturbation, but also substantial variability in compensatory sign. We then applied the same analyses to reaching data from a visuomotor rotation task that inhibited implicit adaptation through delaying feedback presentation. Similarly, we found that participants typically perseverated in reaching towards the target before suddenly switching, in a single trial, to good performance in compensatory magnitude. We fit our generative "Aha!" model to 1337 participants across several datasets and show that it faithfully captures these phenomena. Our findings highlight the need to update existing models to account for the abrupt, insight-driven shifts that characterise individual human responses to sensorimotor perturbations.

neuroscience↗

bioRxiv2024

De Novo Sensorimotor Skill Learning Through Reuse of Movement Components

From tying ones shoelaces to driving a car, complex skills involving the coordination of multiple muscles are common in everyday life; yet relatively little is known about how these skills are learned. Recent studies have shown that new sensorimotor skills involving re-mapping familiar body movements to unfamiliar outputs cannot be learned by adjusting pre-existing controllers, and that new task-specific controllers must instead be learned "de novo". To date, however, few studies have investigated de novo learning in scenarios requiring continuous and coordinated control of relatively unpractised body movements. In this study, we used a myoelectric interface to investigate how a novel controller is learned when the task involves an unpractised combination of relatively untrained continuous muscle contractions. Over five sessions on five consecutive days, participants learned to trace a series of trajectories using a computer cursor controlled by the activation of two muscles. The timing of the generated cursor trajectory and its shape relative to the target improved for conditions trained with post-trial visual feedback. Improvements in timing transferred to all untrained conditions, but improvements in shape transferred only to untrained conditions requiring the trained order of muscle activation. All muscle outputs in the final session could already be generated during the first session, suggesting that participants learned the new task by improving the selection of existing motor commands. These results show that generating novel motor behaviours need not involve learning to generate new motor commands. Significance StatementReal-world skills often involve continuous coordination of multiple muscles. Such skills cannot be learned by adjusting an existing control policy, instead requiring a new controller to be learned "de novo". It remains unclear how new controllers are learned for tasks involving unfamiliar combinations of body movements. In this study, we used a novel human-computer interface task to test this. Over five sessions, participants learned to trace a series of cursor trajectories by coordinating the activation of two muscles. We found that participants tended to reuse the same muscle contractions for trained and untrained variants of the task, and that performance improvements were attributable to improvements in the choice of muscle contractions from a pre-existing repertoire. Our results demonstrate that learning of new complex movements does not necessarily require learning to generate new patterns of muscle activity.

neuroscience↗

bioRxiv2023

Visuomotor memory is not bound to visual motion

Sensory feedback plays a critical role in motor control and motor learning, with both processes adjusting outgoing motor commands relative to the error between actual sensory feedback and the predictions of a forward model. However, models of motor control rarely specify the exact nature of these predictions. We hypothesized that large differences in low-level perceptual feedback would delineate contextual boundaries for motor memory, as sufficiently different feedback would require a distinct mapping between motor commands and sensory states. We tested this hypothesis by measuring transfer of visuomotor adaptation across contexts where hand movements caused visual motion in opposite directions. Instead of observing that visual feedback is bound to distinct internal models, we found nearly complete transfer of learning across the contexts, with evidence that the motor memory was bound to the planned displacement of the hand, rather than visual features of the task space.

neuroscience↗

bioRxiv2023

Kinematic markers of skill in first-person shooter video games

Video games present a unique opportunity to study motor skill. First-person shooter (FPS) games have particular utility because they require visually-guided hand movements that are similar to widely studied planar reaching tasks. However, there is a need to ensure the tasks are equivalent if FPS games are to yield their potential as a powerful scientific tool for investigating sensorimotor control. Specifically, research is needed to ensure that differences in visual feedback of a movement do not affect motor learning between the two contexts. In traditional tasks, a movement will translate a cursor across a static background, whereas FPS games use movements to pan and tilt the view of the environment. To this end, we designed an online experiment where participants used their mouse or trackpad to shoot targets in both contexts. Kinematic analysis showed player movements were nearly identical between conditions, with highly correlated spatial and temporal metrics. This similarity suggests a shared internal model based on comparing predicted and observed displacement vectors, rather than primary sensory feedback. A second experiment, modelled on FPS-style aim-trainer games, found movements exhibited classic invariant features described within the sensorimotor literature. We found that two measures of mouse control, the mean and variability in distance of the primary sub-movement, were key predictors of overall task success. More broadly, these results show that FPS games offer a novel, engaging, and compelling environment to study sensorimotor skill, providing the same precise kinematic metrics as traditional planar reaching tasks. Significance statementSensorimotor control underpins human behaviour and is a predictor of education, health, and socioemotional wellbeing. First-person shooter (FPS) games hold promise for studying sensorimotor control at scale, but the visual feedback provided differs from traditional laboratory tasks. There is a need to ensure they provide measures that relate to traditional tasks. We designed an experiment where the visual contingency of movements could be varied whilst participants shot targets. Participants movements were similar between contexts, suggesting the use of a common internal model despite the sensory differences. A second experiment observed canonical learning patterns with practice and found two measures of mouse control strongly predicted overall performance. Our results highlight the opportunity offered by FPS games to study situated skilled behaviour.

neuroscience↗

bioRxiv2022

A Double Dissociation between Savings and Long-Term Memory in Motor Learning

Both declarative and procedural memories are easier to reacquire than learn from scratch. This advantage, known as savings, has been widely assumed to result from the reemergence of stable long-term memories. In fact, the presence of savings has often been used as a marker for whether a memory had been consolidated. However, recent findings have demonstrated that motor learning rates can be systematically controlled, providing a mechanistic alternative to the reemergence of a stable long-term memory, and recent work has reported conflicting results about whether implicit contributions to savings in motor learning are present, absent, or inverted, suggesting a limited understanding of the underlying mechanisms. In order to elucidate the mechanism responsible for savings in motor learning, we investigate the relationship between savings and long-term memory by determining how they depend on different components of motor learning. To accomplish this, we experimentally dissect motor adaptation based on short-term (1-minute) temporal persistence. Surprisingly, we find that a temporally-volatile component of implicit learning leads to savings whereas temporally-persistent learning does not, but that temporally-persistent learning leads to long-term memory at 24 hours whereas temporally-volatile learning does not. Moreover, we find that temporally-persistent implicit learning not only fails to contribute to savings, but that it produces an anti-savings which acts to reduce the net savings, and we show that the balance between temporally-volatile and temporally-persistent components can explain seemingly inconsistent reports about implicit savings. The clear double dissociation between the mechanisms for savings and long-term memory formation challenges widespread assumptions about the connection between savings and memory consolidation, and provides new insight into the mechanisms for motor learning.

neuroscience↗

bioRxiv2022

Measuring motion-to-photon latency for sensorimotor experiments with virtual reality systems

Consumer virtual reality (VR) systems are increasingly being deployed in research to study sensorimotor behaviours, but properties of such systems require verification before being used as scientific tools. The motion-to-photon latency (the lag between a user making a movement and the movement being displayed within the display) is a particularly important metric as temporal delays can degrade sensorimotor performance. Extant approaches to quantifying this measure have involved the use of bespoke software and hardware and produce a single measure of latency and ignore the effect of the motion prediction algorithms used in modern VR systems. This reduces confidence in the generalisability of the results. We developed a novel, system-independent, high-speed camera-based latency measurement technique to co-register real and virtual controller movements, allowing assessment of how latencies change through a movement. We applied this technique to measure the motion-to-photon latency of controller movements in the HTC Vive, Oculus Rift, Oculus Rift S, and Valve Index, using the Unity game engine and SteamVR. For the start of a sudden movement, all measured headsets had mean latencies between 21-42ms. Once motion prediction could account for the inherent delays, the latency was functionally reduced to 2-13ms, and our technique revealed this reduction occurs within [~]25-58ms of movement onset. Our findings indicate that sudden accelerations (e.g. movement onset, impacts and direction changes) will increase latencies and lower spatial accuracy. Our technique allows researchers to measure these factors and determine the impact on their experimental design before collecting sensorimotor data from VR systems.

neuroscience↗

bioRxiv2021

A Synthesis of the Many Errors and Learning Processes of Visuomotor Adaptation

Visuomotor adaptation has one of the oldest experimental histories in psychology and neuroscience, yet its precise nature has always been a topic of debate. Here we offer a survey and synthesis of recent work on visuomotor adaptation that we hope will prove illuminating for this ongoing dialogue. We discuss three types of error signals that drive learning in adaptation tasks: task performance error, sensory prediction-error, and a binary target hitting error. Each of these errors has been shown to drive distinct learning processes. Namely, both target hitting errors and putative sensory prediction-errors drive an implicit change in visuomotor maps, while task performance error drives learning of explicit strategy use and non-motor decision-making. Each of these learning processes contributes to the overall learning that takes place in visuomotor adaptation tasks, and although the learning processes and error signals are independent, they interact in a complex manner. We outline many task contexts where the operation of these processes is counter-intuitive and offer general guidelines for their control, measurement and interpretation. We believe this new framework unifies several disparate threads of research in sensorimotor adaptation that often seem in conflict. We conclude by explaining how this more nuanced understanding of errors and learning processes could lend itself to the analysis of other types of sensorimotor adaptation, of motor skill learning, of the neural processing underlying sensorimotor adaptation in humans, of animal models and of brain computer interfaces.

neuroscience↗

bioRxiv2020

Re-exposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes

The motor system demonstrates an exquisite ability to adapt to changes in the environment, and to quickly reset when these changes prove transient. If similar environmental changes are encountered in the future, learning may be faster, a phenomenon known as savings. In studies of sensorimotor learning, a central component of savings is attributed to the explicit recall of the task structure and appropriate compensatory strategies. Whether implicit adaptation also contributes to savings remains subject to debate (Leow et al., 2020; Yin and Wei, 2020). We tackled this question by measuring, in parallel, explicit and implicit adaptive responses in a visuomotor rotation task, employing a protocol that typically elicits savings. While the initial rate of learning was faster in the second exposure to the perturbation, an analysis decomposing the two processes showed the benefit to be solely associated with explicit re-aiming. Surprisingly, we found a significant decrease after relearning in aftereffect magnitudes during no-feedback trials, a direct measure of implicit adaptation. In a second experiment, we isolated implicit adaptation using clamped visual feedback, a method known to eliminate the contribution of explicit learning processes. Consistent with the results of the first experiment, participants exhibited a marked reduction in the adaptation function, as well as an attenuated aftereffect when re-learning from the clamped feedback. Motivated by these results, we re-analyzed data from prior studies and observed a consistent, yet unappreciated pattern of attenuation of implicit adaptation during relearning. These results indicate that explicit and implicit sensorimotor processes exhibit opposite effects upon relearning: Explicit learning shows savings, while implicit adaptation becomes attenuated.

neuroscience↗

bioRxiv2017

Invariant errors reveal limitations in motor correction rather than constraints on error sensitivity

Implicit sensorimotor adaptation is traditionally described as a process of error reduction, whereby a fraction of the error is corrected for with each movement. Here, in our study of healthy human participants, we characterize two constraints on this learning process: the size of adaptive corrections is only related to error size when errors are smaller than 6{degrees}, and learning functions converge to a similar level of asymptotic learning over a wide range of error sizes. These findings are problematic for current models of sensorimotor adaptation, and point to a new theoretical perspective in which learning is constrained by the size of the error correction, rather than sensitivity to error.

neuroscience↗