Biology subjects

Mutha, P. K.

Publications and source records attributed to Mutha, P. K..

7 recordsLinked to original sources

bioRxiv2026

Interference Between Motor Memories Arises From Implicit Recalibration

Interference between consecutively acquired motor memories is a defining feature of sensorimotor adaptation, yet its mechanistic origin remains unresolved. Because adaptation is supported by separable explicit strategies and implicit recalibration, it offers a means to identify the learning process that gives rise to interference. Across four visuomotor adaptation experiments, we examined the conditions under which the acquisition of a new, competing motor memory influences the expression of a previously acquired memory. We selectively biased new learning towards either explicit or implicit processes, and quantified its impact on the recall of the original memory 24-hours later. Under standard adaptation conditions, participants exhibited classic interference, such that re-learning was indistinguishable from naive performance. However, when new learning was driven primarily by explicit strategies induced through delayed endpoint feedback, interference was markedly attenuated and the original memory was preserved. In contrast, when the competing memory was implicitly forged under error-clamp conditions, robust interference emerged. Furthermore, disrupting posterior parietal cortex (PPC) with cathodal hd-tDCS prior to implicit learning attenuated interference, indicating that intact PPC processing is required for incorporating new learning into an existing sensorimotor representation. Taken together, these findings suggest that interference reflects the integration of new learning into a shared representational substrate via implicit recalibration, a process that limits the coexistence of competing motor memories.

neuroscience↗

bioRxiv2026

Explicit reward stabilizes motor output by attenuating sensory prediction error driven learning

Motor adaptation driven by sensory prediction errors (SPEs) is often regarded as an automatic, implicit process that operates independent of reward. However, in most past work, reward and performance outcomes (success / failure) have been intrinsically confounded, making it unclear whether explicitly delivered reward per se influences SPE-driven learning. Here, we used an error-clamp paradigm to dissociate reward from both error and task outcome, enabling us to directly test whether explicit reward modulates implicit adaptation. Participants performed reaching movements under clamped visual feedback that produced a constant SPE, while being instructed to ignore the cursor. In two experiments, reward was delivered when the unseen hand successfully intersected the reach target. In an eight-target task, reward did not alter the overall magnitude or time course of adaptation. However, trial-level analyses revealed that rewarded movements were followed by smaller trial-to-trial updates, reduced variability, and a cumulative suppression of adaptive adjustments. Consistent with this result, individuals who experienced reward more frequently exhibited less asymptotic learning. In our second experiment using a simplified, two-target task, these trial-level effects accumulated to produce robust reductions in both asymptotic adaptation and aftereffects in the rewarded groups. Across both experiments, longer streaks of rewarded trials predicted progressively weaker SPE-driven updating. Collectively, these findings demonstrate that implicit adaptation is not insulated from reward signals. Instead, explicit reward appears to attenuate sensitivity to SPEs, stabilizing motor output particularly when the task structure allows consistent action-reward associations. We conclude that motivational signals can gate the expression of error-driven motor adaptation. SIGNIFICANCE STATEMENTImplicit motor adaptation is often thought to be insulated from motivational signals such as reward. By dissociating explicit reward from task outcome as well as visual error using an error-clamp paradigm, we show that reward reliably attenuates trial-to-trial adaptive corrections and reduces their variability, despite identical visual errors on every trial. These effects accumulate across rewarded movements and under stable task conditions, lead to reduced overall adaptation. Our findings thus demonstrate that reward can influence the expression of sensory error-based learning, and that task structure plays a critical role in revealing interactions between reward and implicit adaptation.

neuroscience↗

bioRxiv2025

Flexible, task-dependent bimanual coordination along movement direction and extent

Recent work suggests that coordination of bilateral arm movements is mediated through flexible, task-dependent control policies rather than coupled motor commands that activate homologous muscle groups in the two arms. Here we examined whether such flexibility in bimanual control extends independently across movement direction and extent. We employed a bimanual reach task in which we altered the contribution of each arm to the perpendicular (direction axis) and/or parallel (extent axis) position of a single feedback cursor controlled by both arms together. We first replicated findings of Kitchen et al. (2023) showing that when one arm contributed more to perpendicular cursor motion, its lateral variability was reduced, while variability of the other arm increased. We then extended this result to perturbations affecting movement extent, observing similar asymmetric adjustments in variability in the parallel direction. Subsequent manipulations where contributions along both axes were manipulated simultaneously, revealed that irrespective of the gain combinations, the arm with the higher perpendicular contribution showed reduced lateral variability and the arm with the higher parallel contribution demonstrated restricted parallel variability, while allowing the corresponding lower contribution arms to compensate for perturbation-induced errors. These results suggest that: 1) the sensorimotor system always prioritizes corrections for deviations directly impacting task goals while tolerating higher variability in less relevant dimensions, and 2) that it is capable of adjusting coordination along movement direction and extent largely independently based on peripheral task demands. These findings add to the growing body of evidence supporting task-dependent modulation of bimanual motor coordination. SIGNIFICANCE STATEMENTBimanual actions in humans are thought to be controlled via a control policy that can be flexibly tuned to task demands. This work aimed to demonstrate that such flexibility in control extends independently to movement direction and extent. We used a shared-cursor bimanual reaching task to selectively manipulate the contribution of each arm to cursor motion along the direction and extent axes first in isolation, then simultaneously. Our results show that the sensorimotor system restricts variability in the more task-relevant arm along each axis, enabling axis-specific coordination. These findings thus provide evidence that bimanual coordination along movement direction and extent can be governed flexibly and largely independently in response to changing task demands.

neuroscience↗

bioRxiv2025

REACTIVATION PROTECTS HUMAN MOTOR MEMORIES AGAINST INTERFERENCE FROM COMPETING INPUTS

Memories are often thought to become increasingly stable and resistant to change over time. The reconsolidation framework proposes that reactivating a stable memory transiently increases its susceptibility to modification by subsequent experience, but evidence for such reactivation-induced destabilization remains mixed. Here we tested whether reactivation alters the susceptibility of a visuomotor memory to interference from competing learning. Across a series of motor adaptation experiments, we briefly reactivated a previously acquired memory before introducing an opposing perturbation. Contrary to reconsolidation predictions, reactivation did not increase interference during later retrieval. Instead, reactivation reduced interference when the competing memory was not allowed to stabilize after acquisition. Our findings challenge the view that reactivation universally destabilizes motor memories and instead suggest that it shapes which competing motor memory is expressed at retrieval.

neuroscience↗

bioRxiv2025

Motor Learning Driven by Sensory Prediction Errors is Insensitive to Task Performance Feedback

Accurate motor behavior relies on our ability to refine movements based on errors. While sensory prediction errors (SPEs), mismatches between expected and actual sensory feedback, predominantly drive such adaptation, task performance errors (TPEs), or failures in achieving movement goals, also appear to contribute. However, whether and how TPEs interact with SPEs to shape net learning, remains controversial. This controversy stems from difficulties in experimentally decorrelating these errors, ambiguity related to possible interpretations of task instructions, and inconsistencies between theory and computational models. To try and resolve this issue, we employed variants of an "error-clamp" adaptation paradigm across four reaching experiments (N = 144). Addressing the ambiguity of whether or not the TPE is indeed ignored in standard error-clamp designs as assumed in theoretical (but not computational) models, Experiment 1 explicitly manipulated TPE magnitude by shifting the endpoint feedback location while holding SPE constant. We found that learning was uninfluenced by TPE size. Experiment 2 assumed that the TPE is in fact disregarded under clamp instructions. To then study SPE-TPE interactions, we induced TPEs of varying magnitudes by shifting the target location ("target jump") while always clamping feedback to the original target location. Here, instructions to reach the new target also induced an SPE. Crucially, learning driven by this SPE was again unaffected by TPE magnitude, a result validated by two additional experiments. Our findings consistently demonstrate that SPE-mediated learning remains impervious to variations in task performance feedback, and point to a distinction in learning mechanisms triggered by these two error signals.

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bioRxiv2024

Task relevance selectively modulates sensorimotor adaptation in the presence of multiple prediction errors.

Adaptation to consistently occurring sensorimotor errors is considered obligatory in nature. We probed the robustness of this finding by asking if humans can selectively attenuate adaptation based on the task-relevance of error signals. Subjects made planar reaches to three different targets: an arc (Experiment 1), a bar (Experiment 2), and a point (Experiment 3). During the reach, perturbations in extent (visuomotor gain), direction (visuomotor rotation) or both simultaneously were employed. In Experiment 1, subjects showed robust adaptation to the rotation when reaching to the arc even though the presence of this perturbation was irrelevant for achievement of the task goal. Interestingly however, rotation adaptation was strongly attenuated when it was presented simultaneously with a task-relevant gain perturbation. In Experiment 2, which involved reaches to the bar, again, subjects successfully adapted to the task-irrelevant gain perturbation when it occurred in isolation. However, adaptation was attenuated when the gain co-occurred with a task-relevant rotation. Experiment 3 revealed that the attenuation observed in the first two experiments was not due to an inability to adapt to co-occurring rotation and gain perturbations. Collectively, our results suggest that the sensorimotor system selectively tunes learning in the presence of multiple error signals, a finding that can potentially be explained by a biased competition mechanism. That is, given limited processing capacity, a salient attribute - the relevance of the error to the task goal in this case - is prioritized for processing and drives subsequent adaptive changes in motor output. NEW AND NOTEWORTHYThe motor system continuously uses error feedback to recalibrate movements in response to changes in body and environmental conditions. Such error-based adaptation is thought to be obligatory, occurring whenever error signals are present, and even if the learning interferes with achievement of the task goal. Contrary to this classical view, we demonstrate selective modulation of motor adaptation in the presence of multiple error signals based on their task-relevance.

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bioRxiv2020

Task Errors Do Not Induce Implicit Sensorimotor Learning

The unpredictable nature of our world can introduce a variety of errors in our actions, including sensory prediction errors (SPEs) and task performance errors (TPEs). SPEs arise when our existing internal models of limb-environment properties and interactions become miscalibrated due to changes in the environment, while TPEs occur when environmental perturbations hinder achievement of task goals. The precise mechanisms employed by the sensorimotor system to learn from such limb- and task-related errors and improve future performance are not comprehensively understood. To gain insight into these mechanisms, we performed a series of learning experiments wherein the location and size of a reach target were varied, the visual feedback of the motion was clamped along fixed directions, and instructions were carefully manipulated. Our findings indicate that the mechanisms employed to compensate SPEs and TPEs are dissociable. Specifically, our results fail to support theories that suggest that TPEs trigger implicit refinement of reach plans, or that their occurrence automatically modulates SPE-mediated learning. Rather, TPEs drive improved action selection, that is, the selection of verbally-sensitive, volitional strategies that reduce future errors. Moreover, we find that exposure to SPEs is necessary and sufficient to trigger implicit recalibration. When SPE-mediated implicit learning and TPE-driven improved action selection combine, performance gains are larger. However, when actions are always successful and strategies are not employed, refinement in behaviour is smaller. Flexibly weighting strategic action selection and implicit recalibration could thus be a way of controlling how much, and how quickly, we learn from errors.

neuroscience↗