Biology subjects

Kim, H. E.

Publications and source records attributed to Kim, H. E..

15 recordsLinked to original sources

bioRxiv2026

Individual limb measures of split-belt adaptation are correlated with shifts in lower limb position sense

Stable gait in humans relies on both precise estimates of lower limb position and the ability to robustly adapt walking movements across different environments. However, the specific relationship between sensorimotor adaptation and the perception of lower limb position during gait remains unclear. In this study, we tested a specific theoretical model of how sensorimotor adaptation during gait could cause a shift in the perception of lower limb position. To test this theory, we probed lower limb position sense estimates using a valid and reliable two-alternative forced choice task before and after a long bout of split-belt treadmill walking. The theoretical model assumes that a shift in the perception of limb position is caused by the optimal integration between the predicted and actual sensory feedback of step lengths. Using this model, we generated hypotheses relating to the direction of the shift in limb position sense estimates as well as the relationship between the magnitude of these sensory shifts and the plateau of adaptation when measured using the canonical step length asymmetry index. Like prior studies, participants robustly adapted their step length asymmetry during split-belt treadmill walking. However, we did not observe reliable changes in limb position sense after this adaptation relative to baseline. Furthermore, the changes in limb position sense were not related to the plateau of sensorimotor adaptation. However, post-hoc analysis of other gait parameters, most notably double limb support time, revealed a strong relationship between its adaptation plateau and sensory recalibration. These results suggest that while the specific theorical model we proposed was not supported, sensory recalibration could play an important role in split-belt adaptation and raise questions regarding the error signal driving split-belt adaptation.

neuroscience↗

bioRxiv2026

Dissociating the behavioral and computational features of implicit motor learning and explicit perturbation detection

The human sensorimotor system is remarkably effective at automatically parsing total movement error into its constituent parts, the error component due to a perturbation, or externally-generated error (EGE), versus the error component due to motor noise, or internally-generated error (IGE). Participants robustly, and implicitly, adapt to minuscule (2{degrees}) EGEs in the form of randomized visuomotor rotations while ignoring identically-sized errors caused by IGE. This error parsing, and its associated perceptual processes, directly contrasts previous work showing that humans must observe rotations that are > 1.5x the standard deviations of their motor variability, or [≥] 4{degrees}, before explicitly reporting their presence. While the combined results suggest a dissociation between perception for action--which allows for precise and automatic error parsing--and perception for conscious detection, this must be inferred across studies using different methodologies. Here, we combined a within-subjects study design and computational modeling to shed light on the principles underlying implicit adaptation to a perturbation and explicit perturbation detection. Neuro-typical adults participated in two experiments consisting of pseudo-randomized rotations during reaches to a single target, with one session requiring explicit reports after each reach of whether a perturbation was detected. Participants demonstrated a clear dissociation between implicit responses to a perturbation and explicit detection, with robust adaptation to 1{degrees} EGEs, but an inability to reliably report the presence of an EGE until it reached [~] 4{degrees}. For the adaptation task, a model that assumes the participant compares proprioceptive and visual cues to detect a perturbation and corrects for a proportion of this error best fit the data. For signal-detection, a Bayesian causal-inference model in which sensory cues are optimally integrated with a prior on their cause best fit those data. These results indicate that implicit adaptation is dissociated from explicit perturbation detection and the sensorimotor system applies distinct computational strategies to these behaviors.

neuroscience↗

bioRxiv2025

Deciphering Tumor Microenvironment Dynamics in Tumorigenesis and Lymph Node Metastasis of Esophageal Squamous Cell Carcinoma using Single-Cell RNA Sequencing

Esophageal squamous cell carcinoma (ESCC) exhibits profound inter-tissue heterogeneity, yet how immune and stromal ecosystems diverge between primary tumors and metastatic lymph nodes remains poorly understood. Here, we generated a single-cell atlas of 344,790 cells and paired T cell receptor profiles from primary tumor mucosa, adjacent mucosa, non-metastatic lymph nodes, and metastatic lymph nodes from 18 patients with treatment-naive ESCC. We identify compartment-specific immune and stromal architectures that shape distinct antitumor responses and suppressive programs. Primary tumors were characterized by marked enrichment of regulatory T cells (TREG) and activated dendritic cells coordinated through CTLA4-associated circuits, accompanied by accelerated CD8+ T cell differentiation toward intermediate and terminal exhaustion with loss of cytotoxicity. In contrast, metastatic lymph nodes preserved substantial pools of pre-exhausted CD8+ T cells with retained effector potential and strong clonal connectivity to the primary tumor, suggesting sustained antigen-driven trafficking. However, these reinvigoration-competent populations were embedded within a niche dominated by TREM2high macrophages that delivered SPP1-dependent suppressive signals to exhausted and regulatory T cells. Stromal lineages also displayed niche-specific specialization: tumor mucosa contained chemokine-rich inflammatory fibroblasts, whereas metastatic lymph nodes upregulated extracellular matrix remodeling programs. Together, our findings demonstrate that ESCC progression is governed by anatomically distinct exhaustion trajectories, clonal behaviors, stromal states, and suppressive circuits. This framework provides mechanistic insight into why lymph node response is a key determinant of clinical outcome and highlights the need for site-tailored immunomodulation strategies that target TREG-mediated suppression in primary tumors and TREM2high macrophage programs in metastatic nodes.

cancer biology↗

bioRxiv2025

A Bayesian decision-making model of implicit motor learning from internal and external errors

A key challenge for the sensorimotor system is deciding which errors to learn from and which to ignore. Recent work has shown that humans are remarkably precise in parsing movement errors into internally- and externally-generated components for this purpose: Participants automatically ignore internally-generated reaching errors caused by motor noise, yet implicitly adapt to size-matched externally-generated errors caused by visuomotor rotations (Ranjan & Smith 2018, 2022). Following replication of these results with 16 neurotypical adults, we formalized our understanding of this behavior with a novel Bayesian decision-making model. The Parsing of Internal and External Causes of Error (PIECE) model frames adaptation as a process of causal inference regarding the source of error, with the magnitude of motor corrections reflecting a combination of state estimation and the observers degree-of-belief that their movement was externally perturbed. Thus, PIECE presents a challenge to a class of computational models that frames adaptation as a process of re-aligning the perceived hand position with the movement goal. We show that only PIECE can capture the precise parsing of internal versus external errors observed. Combined, this work provides a normative explanation of how the nervous system discounts intrinsic motor noise and adapts to perturbations, keeping movements finely-calibrated.

neuroscience↗

bioRxiv2024

Evidence for an efferent-based prediction contributing to implicit motor adaptation

Models of sensorimotor adaptation have proposed that implicit adaptation is driven by error signals created by discrepancies between various sensory information sources. While proprioception has been suggested as a critical source for the error signals driving adaptation, the role of an efferent-based motor prediction has largely been neglected. In this study, we examined the effect of dissociating the afferent and efferent information available during implicit adaptation. Participants moved a visual cursor towards targets by applying horizontal forces to a stationary handle at a central home location. During perturbation trials, the cursor followed an invariant path rotated relative to the target. Participants were instructed to ignore this task-irrelevant cursor feedback and to isometrically "reach" towards the target. Participants implicitly adapted in the isometric task, even when the hand never actually moved to the target. Moreover, the level of adaptation surpassed that of a typical clamped reaching paradigm by nearly twofold. This was confirmed in a secondary experiment where participants performed actual reaching movements and demonstrated significantly less adaptation. Our findings suggest that while afferent proprioceptive feedback of hand position around the target most likely plays a role in adaptation, it is not necessary to induce adaptation.

neuroscience↗

bioRxiv2024

Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke

Motor learning involves both explicit and implicit processes that are fundamental for acquiring and adapting complex motor skills. However, stroke may damage the neural substrates underlying explicit and/or implicit learning, leading to deficits in overall motor performance. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke when tested during a task that elicits dissociable contributions from both. Here, we compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning (i.e., strategy-use) using visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. The removal of visual feedback after the first 40 strides of split-belt walking, combined with task instructions, minimized contributions from explicit learning for the remainder of the task. We utilized a multi-rate state-space model to characterize individual explicit and implicit process contributions to overall behavioral change. The computational and behavioral analyses revealed that, compared to controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning, a result that runs counter to prior work testing each process individually during gait. Since post-stroke locomotor rehabilitation involves interventions that rely on both explicit and implicit motor learning, future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning. New and noteworthyMotor learning involves both implicit and explicit processes, the underlying neural substrates of which could be damaged by after stroke. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke. Using a locomotor task that elicits dissociable contributions from both processes and computational modeling, we found evidence that chronic stroke causes deficits in both explicit and implicit locomotor learning.

neuroscience↗

bioRxiv2023

Reinforcement learning during locomotion

When learning a new motor skill, people often must use trial and error to discover which movement is best. In the reinforcement learning framework, this concept is known as exploration and has been linked to increased movement variability in motor tasks. For locomotor tasks, however, increased variability decreases upright stability. As such, exploration during gait may jeopardize balance and safety, making reinforcement learning less effective. Therefore, we set out to determine if humans could acquire and retain a novel locomotor pattern using reinforcement learning alone. Young healthy male and female participants walked on a treadmill and were provided with binary reward feedback (indicated by a green checkmark on the screen) that was tied to a fixed monetary bonus, to learn a novel stepping pattern. We also recruited a comparison group who walked with the same novel stepping pattern but did so by correcting for target error, induced by providing real time veridical visual feedback of steps and a target. In two experiments, we compared learning, motor variability, and two forms of motor memories between the groups. We found that individuals in the binary reward group did, in fact, acquire the new walking pattern by exploring (increasing motor variability). Additionally, while reinforcement learning did not increase implicit motor memories, it resulted in more accurate explicit motor memories compared to the target error group. Overall, these results demonstrate that humans can acquire new walking patterns with reinforcement learning and retain much of the learning over 24 hours. Significance StatementHumans can learn some novel movements by independently discovering the actions that lead to success. This discovery process, exploration, requires increased motor variability to determine the best movement. However, in bipedal locomotion especially, increasing motor variability decreases stability, heightening the risk of negative outcomes such as a trip, injury, or fall. Despite this stability constraint, the current study shows that individuals do use exploration to find the most rewarding walking patterns. This form of learning led to improved explicit retention but not implicit aftereffects. Thus, the reinforcement learning framework can explain findings across a wide range of motor and cognitive tasks, including locomotion.

neuroscience↗

bioRxiv2023

A reliable and efficient adaptive Bayesian method to assess static lower limb proprioception.

BackgroundLower limb proprioception is critical for maintaining stability during gait and may impact how individuals modify their movements in response to changes in the environment and body state, a process termed "sensorimotor adaptation". However, the connection between lower limb proprioception and sensorimotor adaptation during human gait has not been established. We suspect this gap is due in part to the lack of reliable, efficient methods to assess global lower limb proprioception in an ecologically valid context. New MethodWe assessed static lower limb proprioception using an alternative forced choice task, administered twice to determine test-retest reliability. Participants stood on a dual-belt treadmill which passively moved one limb to stimulus locations selected by a Bayesian adaptive algorithm. At the stimulus locations, participants judged relative foot positions and the algorithm estimated the point of subjective equality (PSE) and the uncertainty of lower limb proprioception. ResultsUsing the Bland-Altman method, combined with Bayesian statistics, we found that both the PSE and uncertainty estimates had good reliability. Comparison with Existing Method(s)Current methods assessing static lower limb proprioception do so within a single joint, in non-weight bearing positions, and rely heavily on memory. One exception assessed static lower limb proprioception in standing but did not measure reliability and contained confounds impacting participants judgments, which we experimentally controlled here. ConclusionsThis efficient and reliable method assessing lower limb proprioception will aid future mechanistic understanding of locomotor adaptation and serve as a useful tool for basic and clinical researchers studying balance and falls.

neuroscience↗

bioRxiv2021

Proprioceptive Re-alignment drives Implicit Sensorimotor Adaptation

Multiple learning processes contribute to successful goal-directed actions in the face of changing physiological states, biomechanical constraints, and environmental contexts. Amongst these processes, implicit sensorimotor adaptation is of primary importance, ensuring that movements remain well-calibrated and accurate. A large body of work on reaching movements has emphasized how adaptation centers on an iterative process designed to minimize visual errors. The role of proprioception has been largely neglected, thought to play a passive role in which proprioception is affected by the visual error but does not directly contribute to adaptation. Here we present an alternative to this visuo-centric framework, arguing that that implicit adaptation can be understood as minimizing a proprioceptive error, the distance between the perceived hand position and its intended goal. We use this proprioceptive re-alignment model (PReMo) to re-examine many phenomena that have previously been interpreted in terms of learning from visual errors, as well as offer novel accounts for unexplained phenomena. We discuss potential challenges for this new perspective on implicit adaptation and outline a set of predictions for future experimentation.

neuroscience↗

bioRxiv2021

Use-dependent biases primarily originate from a contaminated motor plan

Repetition of a specific movement biases subsequent actions towards the recently practiced movement, a phenomenon referred to as use-dependent learning (UDL). UDL has been attributed to shifts in the tuning of neurons in the motor cortex. However, recent studies employing a forced reaction time task, including the eLife article by Marinovic et al (2017), indicate that these biases may also arise from a contaminated motor plan, one that is biased towards the practiced direction. We advanced this line of inquiry, seeking to establish the relative contribution of execution and planning processes to UDL in a center-out reaching task in which participants were able to initiate movements of their own volition. On most trials, the target appeared at a designated "frequent" location; on other trials, the target appeared at one of six "rare" locations. In Experiment 1, participants exhibited a robust movement bias towards the frequent target when movements were self-initiated quickly, but a small movement bias when movements were self-initiated slowly - the signature of a contaminated motor plan. Strikingly, the heading angles were bimodally distributed, with one peak at the frequent target location and the other at the rare target location - a finding reinforced by a re-analysis of two widely cited studies on UDL. Notably, the latter peak was shifted in the frequently practiced direction, a signature of a motor execution bias. To eliminate the contribution of planning-related UDL, we imposed a delay between target onset and movement initiation in Experiment 2. As predicted, the heading angles became unimodally distributed around the rare target. The peak of this distribution was again shifted towards the location of the frequent target, indicative of a persistent bias in motor execution. Taken together, these results highlight two distinct components of UDL even when movements are self-initiated: First, the temporal dynamics underlying movement planning, in which a default plan is progressively overridden by a new plan, produces a pronounced motor planning bias. Second, there is a small, temporally stable bias that may reflect shifts in motor unit tuning.

neuroscience↗

bioRxiv2021

Long-term Motor Learning in the Wild with High Volume Video Game Data

Motor learning occurs over long periods of practice during which motor acuity - the ability to execute actions more accurately, precisely, and within a shorter amount of time - improves. Laboratory-based motor learning studies are typically limited to a small number of participants and a time frame of minutes to several hours per participant. Thus, there is a need to assess the generalizability of theories and findings from lab-based motor learning studies on much larger samples across longer time scales. In addition, laboratory-based studies of motor learning use relatively simple motor tasks which participants are unlikely to be intrinsically motivated to learn, limiting the interpretation of their findings in more ecologically valid settings. We studied the acquisition and longitudinal refinement of a complex sensorimotor skill embodied in a first-person shooter video game scenario, with a large sample size (N = 7174 participants, 682,564 repeats of the 60 sec game) over a period of months. Participants voluntarily practiced the gaming scenario for as much as several hours per day up to 100 days. We found improvement in performance accuracy (quantified as hit rate) was modest over time but motor acuity (quantified as hits per second) improved considerably, with 40-60% retention from one day to the next. We observed steady improvements in motor acuity across multiple days of video game practice, unlike most motor learning tasks studied in the lab that hit a performance ceiling rather quickly. Learning rate was a nonlinear function of baseline performance level, amount of daily practice, and to a lesser extent, number of days between practice sessions. In addition, we found that the benefit of additional practice on any given day was non-monotonic; the greatest improvements in motor acuity were evident with about an hour of practice and 90% of the learning benefit was achieved by practicing 30 minutes per day. Taken together, these results provide a proof-of-concept in studying motor skill acquisition outside the confines of the traditional laboratory and provide new insights into how a complex motor skill is acquired in an ecologically valid setting and refined across much longer time scales than typically explored.

neuroscience↗

bioRxiv2021

Distinct Processing of Sensory Prediction Error and Task Error during Motor Learning

Implicit motor recalibration allows us to flexibly move in novel and changing environments. Conventionally, implicit recalibration is thought to be driven by errors in predicting the sensory outcome of movement (i.e., sensory prediction errors). However, recent studies have shown that implicit recalibration is also influenced by errors in achieving the movement goal (i.e., task errors). Exactly how sensory prediction errors and task errors interact to drive implicit recalibration and, in particular, whether task errors alone might be sufficient to drive implicit recalibration remain unknown. To test this, we induced task errors in the absence of sensory prediction errors by displacing the target mid-movement. We found that task errors alone failed to induce implicit recalibration. In additional experiments, we simultaneously varied the size of sensory prediction errors and task errors. We found that implicit recalibration driven by sensory prediction errors could be continuously modulated by task errors, revealing an unappreciated dependency between these two sources of error. Moreover, implicit recalibration was attenuated when the target was simply flickered in its original location, even though this manipulation did not affect task error - an effect likely attributed to attention being directed away from the feedback cursor. Taken as a whole, the results were accounted for by a computational model in which sensory prediction errors and task errors, modulated by attention, interact to determine the extent of implicit recalibration. Authors summaryWhat information does the brain use to maintain precise calibration of the sensorimotor system? Using a reaching task paired with computational modeling, we find that movements are implicitly recalibrated by errors in predicting both the sensory outcome of movement (i.e., sensory prediction errors) as well as errors in achieving the movement goal (i.e., task errors). Even though task errors alone do not elicit implicit recalibration, they nonetheless modulate implicit recalibration when sensory prediction error is present. The results elucidate an unappreciated interaction between these two sources of error in driving implicit recalibration.

neuroscience↗

bioRxiv2020

Individual differences in proprioception predict the extent of implicit sensorimotor adaptation

Recent studies have revealed an upper bound in motor adaptation, beyond which other learning systems may be recruited. The factors determining this upper bound are poorly understood. The multisensory integration hypothesis states that this limit arises from opposing responses to visual and proprioceptive feedback. As individuals adapt to a visual perturbation, they experience an increasing proprioceptive error in the opposite direction, and the upper bound is the point where these two error signals reach an equilibrium. Assuming that visual and proprioceptive feedback are weighted according to their variability, there should be a correlation between proprioceptive variability and the limits of adaptation. Alternatively, the proprioceptive realignment hypothesis states that the upper bound arises when the (biased) sensed hand position realigns with the target. When a visuo-proprioceptive discrepancy is introduced, the sensed hand position is biased towards the visual cursor and the adaptive system nullifies this discrepancy by driving the hand away from the target. This hypothesis predicts a correlation between the size of the proprioceptive shift and the upper bound of adaptation. We tested these two hypotheses by considering natural variation in proprioception and motor adaptation across individuals. We observed a modest, yet reliable correlation between the upper bound of adaptation with both proprioceptive measures (variability and shift). While these results do not favor one hypothesis over the other, they underscore the critical role of proprioception in sensorimotor adaptation, and moreover, motivate a novel perspective on how these proprioceptive constraints drive implicit changes in motor behavior. SIGNIFICANCE STATEMENTWhile the sensorimotor system uses sensory feedback to remain properly calibrated, this learning process is constrained, limited in the maximum degree of plasticity. The factors determining this limit remain elusive. Guided by two hypotheses concerning how visual and proprioceptive information are integrated, we show that individual differences in the upper bound of adaptation in response to a visual perturbation can be predicted by the bias and variability in proprioception. These results underscore the critical, but often neglected role of proprioception in human motor learning.

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↗

bioRxiv2020

The Effect of Visual Uncertainty on Implicit Motor Adaptation

Sensorimotor adaptation is driven by sensory prediction errors, the difference between the predicted and actual feedback. When the position of the feedback is made uncertain, adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to a weakening of the error signal driving adaptation. Here we consider an alternative hypothesis, namely that uncertainty alters the perceived location of the feedback. We present two visuomotor adaptation experiments to compare these hypotheses, varying the size and uncertainty of a visual error signal. Uncertainty attenuated learning when the error size was small but had no effect when the error size was large. This pattern of results favors the hypothesis that uncertainty does not impact the strength of the error signal, but rather, leads to mis-localization of the error. We formalize these ideas to offer a novel perspective on the effect of visual uncertainty on implicit sensorimotor adaptation. SIGNIFICANCE STATEMENTCurrent models of sensorimotor adaptation assume that the rate of learning will be related to properties of the error signal (e.g., size, consistency, relevance). Recent evidence has challenged this view, pointing to a rigid, modular system, one that automatically recalibrates the sensorimotor map in response to movement errors, with minimal constraint. In light of these developments, this study revisits the influence of feedback uncertainty on sensorimotor adaptation. Adaptation was attenuated in response to a noisy feedback signal, but the effect was only manifest for small errors and not for large errors. This interaction suggests that uncertainty does not weaken the error signal. Rather, it may influence the perceived location of the feedback and thus the change in the sensorimotor map induced by that error. These ideas are formalized to show how the motor system remains exquisitely calibrated, even if adaptation is largely insensitive to the statistics of error signals.

neuroscience↗