Error-based Learning Research Articles

Visuomotor adaptation learning not affected by repeated sport-related concussion

Sports-related concussions (SRC) have been associated with emotional, cognitive, and affective symptoms including a negative impact on motor-based learning. However, no study has assessed the impact of SRC on cerebellar-based motor learning. Cerebellar-based motor learning was assessed in three different groups of athletes with different SRC history: athletes with no history of SRC: athletes in the acute stage of SRC (within two weeks of injury), and athletes in the chronic stage of SRC (over one year after injury). We used a visuomotor adaptation task (VAT) to measure both explicit strategy-based learning and implicit error-based learning. We found that there was no difference in cerebellar dependent motor learning in SRC and non-SRC athletes. These findings suggest that the cerebellum may be more resilient to damage from SRCs than the motor cortex.

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection. MethodsData from the Infant Brain Imaging Study (n= 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning–based predictive tests examined cerebellar–frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar–default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections. ResultsCerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections. ConclusionsWe failed to identify cerebellar functional connectivity–based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

Which training methods are effective for learning new smartphone memory apps after acquired brain injury? A pilot randomized controlled trial comparing trial and error, systematic instruction and error-based learning

ABSTRACT This study aimed to compare the efficacy of three skills training methods (Trial and error TEL; systematic instruction SI; and error-based learning EBL) for training the use of a smartphone reminder app in individuals with an acquired brain injury. Participants (N = 38, Mage = 61.21 years, 71.1% stroke) were randomly allocated to one of three training conditions and trained over one two-hour session. Proficiency of performance with the trained app (primary outcome) was assessed immediately post-training, one- and six-weeks post-intervention. Secondary outcomes included generalization of skills, error commission, smartphone use frequency and confidence, and subjective memory complaints. Proficiency with the trained app after TEL was higher than SI immediately after the training (d = 0.87) and EBL at the one-week follow-up (d = 0.98). No differences were found six-weeks post-training. Smartphone use confidence increased at the six-week follow-up after TEL (d = 1.12) and EBL training (d = 0.91) but not after SI (d = 0.26). Self-reported memory complaints decreased across time for all groups (η p 2 = 0.30). There was no clearly superior training method for optimizing proficiency with the reminder app. The expected benefits of SI and EBL may not have emerged due to the single-session format of the training. However, smartphone training via TEL or EBL has the potential to address confidence-related barriers to smartphone use.

Task-relevant and task-irrelevant variability causally shape error-based motor learning

Recent studies of motor learning show dissociable roles of reward- and sensory-prediction errors in updating motor commands by using typical adaptation paradigms where force or visual perturbations are imposed on hand movements. Such classic adaptation paradigms ignore a problem of redundancy inherently embedded in the motor pathways where the central nervous system has to find a unique solution in the high-dimensional motor command space. Computationally, a possible way of solving such a redundancy problem is exploring and updating motor commands based on the learned knowledge of the structures of both the motor pathways and the tasks. However, the effects of task-irrelevant motor command exploration in structure learning and its effects on reward-based and error-based learning have yet to be examined. Here, we used a redundant motor task where participants manipulated a cursor on a monitor screen with their hand gesture movements and then analyzed single-trial motor learning by fitting models consisting of reward-based and error-based learning contributions. We found that the error-based learning rate positively correlated with both task-relevant and task-irrelevant variability, likely reflecting the effect of motor exploration in structure learning. Further modeling results show that the effects of both task-relevant and task-irrelevant variability are simultaneous, and not mediated by one another. In contrast, the reward-based learning rate correlated with neither task-relevant nor task-irrelevant variability. Thus, although not having a direct influence on the task outcome, exploration in the task-irrelevant space late in training has a significant effect on the learning of a task structure used for error-based learning. This suggests that motor exploration, in both task-relevant and task-irrelevant spaces, has an essential role in error-based motor learning in a redundant motor mechanism.

Motor adaptation and internal model formation in a robot-mediated forcefield.

Motor adaptation relies on error-based learning for accurate movements in changing environments. However, the neurophysiological mechanisms driving individual differences in performance are unclear. Transcranial magnetic stimulation (TMS)-evoked potential can provide a direct measure of cortical excitability. To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield. A group of 15 right-handed healthy participants (mean age 23 years) performed a robot-mediated forcefield perturbation task. There were two conditions: unperturbed non-adaptation and perturbed adaptation. TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex (left M1). Electroencephalographic (EEG) activity was continuously recorded, and cortical excitability was measured by TMS-evoked potential (TEP). Motor learning was quantified by the motor learning index. Larger error-related negativity (ERN) in fronto-central regions was associated with improved motor performance as measured by a reduction in trajectory errors. Baseline TEP N100 peak amplitude predicted motor learning (P=0.005), which was significantly attenuated relative to baseline (P=0.0018) following motor adaptation. ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation. Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex. TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.

Analysis of error-based machine learning algorithms in network anomaly detection and categorization

Intrusion and anomaly detection are particularly important to protect computer networks and communication vulnerability. This research aims to experimentally identify the best error-based machine learning algorithm for anomaly detection and anomaly attack categorization with the highest accuracy and fastest build time. A two-stage anomaly and categorization framework has been set up for experimental evaluation. The first stage identifies if a network flow is normal or anomalous and the second stage identifies type of attack if the first stage result is anomalous. The goal is to eventually use the best algorithm in an online stream model of network intrusion detection. To this end, five research propositions are defined, four sets of experiments are set up, and four research questions are asked. The UNSW-NB15 dataset for network anomaly is used for training and testing in the experiments. Machine learning algorithms are classified into four different learning approaches: information-based, similarity-based, probability-based, and error-based. Our focus in this paper is on the error-based learning models, specifically, the following algorithms: Winnow, Logistic, Perceptron, Support Vector Machine (SVM), and Deep Learning. The results are also compared with the results of non-error-based machine learning algorithms. The results obtained show that, overall, the error-based machine learning algorithm, Winnow, is the best with 100% accuracy and time to build the model of 0.47 s for network anomaly detection. In terms of accuracy only, SVM comes top for network anomaly attack categorization but Simple Logistic is the best when accuracy and time to build are considered together.

The Substantia Nigra Pars Reticulata Modulates Error-Based Saccadic Learning in Monkeys.

The basal ganglia have long been considered crucial for associative learning, but whether they also are involved in another type of learning, error-based motor learning, is not clear. Error-based learning has been considered the province of the cerebellum. However, learning to use a robotic arm and saccade adaptation, which use error-based learning, are facilitated by motivation, which is a function of the basal ganglia. Additionally, patients with Parkinson’s disease, a basal ganglia deficit, show slower saccade adaptation than age matched controls. To further investigate whether the basal ganglia actually influence error-based learning, we reversibly inactivated the oculomotor portion of the substantia nigra pars reticulata (SNr) in two monkeys and tested saccade adaptation. Here, we show that nigral inactivation affected saccade adaptation. In particular, the inactivation facilitated the amplitude decrease adaptation of ipsiversive saccades. Consistent with previous studies, no effect was seen on the amplitude of the ipsiversive saccades when we did not induce adaptation. Therefore, the facilitated adaptation was not caused by inactivation directly modulating ipsiversive saccades. On the other hand, the kinematics of corrective saccades, which represent error processing, were changed after the inactivation. Thus, our data suggest that the oculomotor SNr assists saccade adaptation by strengthening the error signal. This effect indicates the basal ganglia influence error-based motor learning, a previously unrecognized function.

WITHDRAWN: CoppeliaSim: Adaptable modular robot and its different locomotions simulation framework

From Aeronautics to UBTECH LYNX and ASUS ZENBO the adoptability of robotics in every domain is vast. Integrity of the robots is all about the sensing and controlling. While designing any hardware module, it prefers to design in any simulator first, as if there are any changes and technical issues raises, we can re simulate it again and again till the desired result is achieved. Hardware of the hexa -robot is transformed its creation from an error-based learning technique to the skills of walking and climbing over huddles. This paper proposes a forced learning-based method to learn the light footed and dynamic motor skills of a hexapod to efficiently walk and recover from falling in complex terrains. Learning approaches typically faces trials and errors, and if the robot run across unknown situation, a new learning scenario is needed again, which is time consuming as well as costly. The above drawbacks of designing directly with hardware is to overcome by simulating and controlling hexa robot using coppeliaSim. we design this hexa robot in such a way to performs same functions and satisfy the purpose of its design by using coppeliasim simulator which creates a platform in which we can simulate a robot in 3d model. The propose work discuss about Enable a robot to walk and adapt its locomotion to helm unknown complex terrains through simulator.

Learning Safety Through Public Serious Games: A Study of "Prepare for Impact" on a Very Large, International Sample of Players.

Recent years have witnessed a growing interest in serious games (SGs), i.e., digital games for education and training. However, although the potential scalability of SGs to large player populations is often praised in the literature, available SG evaluations did not provide evidence of it because they did not study learning on large, varied, international samples in naturalistic conditions. This article considers a SG that educates players about aircraft cabin safety. It presents the first study of learning in a SG intervention conducted in naturalistic conditions with a very large, worldwide sample, which includes 45,000 players who accepted to answer a knowledge questionnaire before and after playing the game, and more than 400,000 players whose in-game behavior was analyzed. Results show that the SG led to improvement in players' knowledge, assessed with different metrics. Moreover, analysis of repeated play shows that participants improved their in-game safety behavior over time. We also focus on the role of making errors in the game, showing how they led to improvement in knowledge. Finally, we highlight the theoretical models, such as error-based learning and Protection Motivation Theory, that oriented the game design, and can be reused to create SGs for other domains.

Content-Aware Eye Tracking for Autostereoscopic 3D Display.

This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm and relevant systems for accurate and fast detection and tracking of eye pupil centers in 3D with a single visual camera and near-infrared (NIR) light emitting diodes (LEDs). Our proposed eye tracker consists of eye–nose detection, eye–nose shape keypoint alignment, a tracker checker, and tracking with NIR LED on/off control. Eye–nose detection generates facial subregion boxes, including the eyes and nose, which utilize an Error-Based Learning (EBL) method for the selection of the best learnt database (DB). After detection, the eye–nose shape alignment is processed by the Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). The aligner is content-aware in the sense that corresponding designated aligners are applied based on image content classification, such as the various light conditions and wearing eyeglasses. The conducted experiments on real image DBs yield promising eye detection and tracking outcomes, even in the presence of challenging conditions.

A comparison of systematic instruction, error-based learning and trial and error to train the use of smartphone memory apps after acquired brain injury: A three-armed phase II randomised controlled trial study protocol

AbstractBackground:The uptake of smartphones as external compensatory memory aids following an acquired brain injury (ABI) in rehabilitation settings is low. Potential reasons for this include professionals not having evidence-based guidelines regarding the best methods to train smartphone use and prospective users not being familiar with technology and/or having memory and learning difficulties. This paper describes the protocol of a study that aims to compare the efficacy of three training methods (Systematic Instruction, Error-based Learning and Trial-and-Error) for training the use of a smartphone reminder app, in people with ABI presenting with memory complaints.Methods/Design:This is a three-armed, assessor-blinded, Phase II randomised controlled trial. The estimated sample size is 51 participants aged >18 years, who are equally randomised to one of the three training groups. They are seen across four sessions: one to conduct baseline measures; one for training the use of an app and two for follow-up assessments (1- and 6-weeks post-training). The main outcome measure is proficiency of performance in tasks with the trained app. Secondary outcomes include generalisation of skills to other apps, number of errors committed while attempting the tasks, frequency of smartphone usage in general and as a memory aid and confidence in smartphone use and memory self-efficacy. Outcome measures are collected by an independent blinded assessor. Proficiency of performance, generalisation of skills and error commission are measured immediately post-training and at the two follow-up sessions. The other secondary measures are taken pre-intervention and at the two follow-up sessions.Discussion:This study will provide initial evidence regarding the efficacy of three different methods to train ABI survivors with memory difficulties in how to use smartphone apps as compensatory memory aids. The results could inform a larger Phase III trial and advance knowledge concerning the advantages or disadvantages of using error-reducing and trial-and-error techniques. Further, the findings could determine the potential of error-based learning as an emerging training method for people with memory impairment within rehabilitation.

Three-dimensional assessment of robot-assisted pedicle screw placement accuracy and instrumentation reliability based on a preplanned trajectory.

Robotic spine surgery systems are increasingly used in the US market. As this technology gains traction, however, it is necessary to identify mechanisms that assess its effectiveness and allow for its continued improvement. One such mechanism is the development of a new 3D grading system that can serve as the foundation for error-based learning in robot systems. Herein the authors attempted 1) to define a system of providing accuracy data along all three pedicle screw placement axes, that is, cephalocaudal, mediolateral, and screw long axes; and 2) to use the grading system to evaluate the mean accuracy of thoracolumbar pedicle screws placed using a single commercially available robotic system. The authors retrospectively reviewed a prospectively maintained, IRB-approved database of patients at a single tertiary care center who had undergone instrumented fusion of the thoracic or lumbosacral spine using robotic assistance. Patients with preoperatively planned screw trajectories and postoperative CT studies were included in the final analysis. Screw accuracy was measured as the net deviation of the planned trajectory from the actual screw trajectory in the mediolateral, cephalocaudal, and screw long axes. The authors identified 47 patients, 51% male, whose pedicles had been instrumented with a total of 254 screws (63 thoracic, 191 lumbosacral). The patients had a mean age of 61.1 years and a mean BMI of 30.0 kg/m2. The mean screw tip accuracies were 1.3 ± 1.3 mm, 1.2 ± 1.1 mm, and 2.6 ± 2.2 mm in the mediolateral, cephalocaudal, and screw long axes, respectively, for a net linear deviation of 3.6 ± 2.3 mm and net angular deviation of 3.6° ± 2.8°. According to the Gertzbein-Robbins grading system, 184 screws (72%) were classified as grade A and 70 screws (28%) as grade B. Placement of 100% of the screws was clinically acceptable. The accuracy of the discussed robotic spine system is similar to that described for other surgical systems. Additionally, the authors outline a new method of grading screw placement accuracy that measures deviation in all three relevant axes. This grading system could provide the error signal necessary for unsupervised machine learning by robotic systems, which would in turn support continued improvement in instrumentation placement accuracy.

Risk prediction error signaling: A two-component response?

Organisms use rewards to navigate and adapt to (uncertain) environments. Error-based learning about rewards is supported by the dopaminergic system, which is thought to signal reward prediction errors to make adjustments to past predictions. More recently, the phasic dopamine response was suggested to have two components: the first rapid component is thought to signal the detection of a potentially rewarding stimulus; the second, slightly later component characterizes the stimulus by its reward prediction error. Error-based learning signals have also been found for risk. However, whether the neural generators of these signals employ a two-component coding scheme like the dopaminergic system is unknown. Here, using human high density EEG, we ask whether risk learning, or more generally speaking surprise-based learning under uncertainty, is similarly comprised of two temporally dissociable components. Using a simple card game, we show that the risk prediction error is reflected in the amplitude of the P3b component. This P3b modulation is preceded by an earlier component, that is modulated by the stimulus salience. Source analyses are compatible with the idea that both the early salience signal and the later risk prediction error signal are generated in insular, frontal, and temporal cortex. The identified sources are parts of the risk processing network that receives input from noradrenergic cells in the locus coeruleus. Finally, the P3b amplitude modulation is mirrored by an analogous modulation of pupil size, which is consistent with the idea that both the P3b and pupil size indirectly reflect locus coeruleus activity.

Contrasting mechanistic accounts of the lexical boost.

While many recent studies focused on abstract syntactic priming effects have implicated an error-based learning mechanism, there is little consensus on the most likely mechanism underlying the lexical boost. The current study aimed at refining understanding of the mechanism that leads to this priming effect. In two eye-tracking during reading experiments, the nature of the lexical boost was investigated by comparing predictions from competing accounts in terms of decay and the requirement of structural overlap between primes and targets. Experiment 1 revealed facilitation of target structure processing for shorter relative to longer primes, when there were fewer intervening words between prime and target verbs. In Experiment 2, significant lexically boosted priming effects were observed, but only when the target structure also appeared in the prime, and not when the prime had a different structure but a high degree of lexical overlap with the target. Overall, these results are most consistent with a short-lived mechanistic account rather than an error-based learning account of the lexical boost. Furthermore, these results align with dual-mechanism accounts of syntactic priming whereby different mechanisms are claimed to produce abstract syntactic priming effects and the lexical boost.

An investigation into the English language experiences of Indian international students studying in New Zealand universities

ABSTRACTLanguage proficiency is highlighted in the literature on the adjustment and security of international students. India is the second top source country for mobile students globally, but few studies have focused explicitly on students from India. The purpose of this study was to examine the English language experiences of Indian students at New Zealand universities. This mixed-method study utilised a questionnaire (n = 109) and interviews (n = 15). Collectively the participants had high levels of confidence and proficiency in English language and adjusted more rapidly than many international students to the new academic environment. Major differences experienced by the participants between India and New Zealand related to unfamiliar forms of academic writing and tasks, and on the role of learning support services. Formal high value assessment was for some the first indication of a problem with writing. While most students adjusted through error-based learning others struggled with argumentation and avoiding plagiarism. These findings have relevance for local and international students. We recommend the use of, and research on, early low-stakes assessment of writing and on effective support services for students transitioning into new academic contexts.

Error-Based Learning Mechanism for Fast Online Adaptation in Robot Motor Control.

Existing state-of-the-art frequency adaptation mechanisms of central pattern generators (CPGs) for robot locomotion control typically rely on correlation-based learning. They do not account for the tracking error that may occur between the actual system motion and CPG output, leading to the loss of precision, unwanted movement, inefficient energy locomotion, and in the worst cases, motor collapse. To overcome this problem, we developed online error-based learning for frequency adaptation of CPGs. The learning mechanism used for error reduction is a novel modification of the dual learner (DL) called dual integral learner (DIL). Being able to reduce tracking and steady-state errors, it can also perform fast and stable learning, adapting the CPG frequency to match the performance of robotic systems. Control parameters of the DIL are more straightforward for complex systems (like walking robots), compared to traditional correlation-based learning, since they correspond to error reduction. Due to its embedded memory, the DIL can relearn quickly and recover spontaneously from the previously learned parameters. All these features are not covered by the existing frequency adaptation mechanisms. We integrated the DIL into a neural CPG-based motor control system for use on different legged robots with various morphologies for evaluation. The results show that: 1) the DIL does not require precise adjustment of its parameters to fit specific robots; and 2) the DIL can automatically and quickly adapt the CPG frequency to the robots such that the entire trajectory of the CPG can be precisely followed with very low tracking and steady-state errors. Consequently, the robots can perform the desired movements with more energy-efficient locomotion compared to the state-of-the-art correlation-based learning mechanism called frequency adaptation through fast dynamical coupling (AFDC). In the future, the proposed error-based learning mechanism for fast online adaptation in robot motor control can be used as a basis for trajectory optimization, universal controllers, and other studies concerning the change of intrinsic or extrinsic parameters.

The interplay between cerebellum and basal ganglia in motor adaptation: A modeling study.

Motor adaptation to perturbations is provided by learning mechanisms operating in the cerebellum and basal ganglia. The cerebellum normally performs motor adaptation through supervised learning using information about movement error provided by visual feedback. However, if visual feedback is critically distorted, the system may disengage cerebellar error-based learning and switch to reinforcement learning mechanisms mediated by basal ganglia. Yet, the exact conditions and mechanisms of cerebellum and basal ganglia involvement in motor adaptation remain unknown. We use mathematical modeling to simulate control of planar reaching movements that relies on both error-based and non-error-based learning mechanisms. We show that for learning to be efficient only one of these mechanisms should be active at a time. We suggest that switching between the mechanisms is provided by a special circuit that effectively suppresses the learning process in one structure and enables it in the other. To do so, this circuit modulates learning rate in the cerebellum and dopamine release in basal ganglia depending on error-based learning efficiency. We use the model to explain and interpret experimental data on error- and non-error-based motor adaptation under different conditions.

Language ERPs reflect learning through prediction error propagation

Event-related potentials (ERPs) provide a window into how the brain is processing language. Here, we propose a theory that argues that ERPs such as the N400 and P600 arise as side effects of an error-based learning mechanism that explains linguistic adaptation and language learning. We instantiated this theory in a connectionist model that can simulate data from three studies on the N400 (amplitude modulation by expectancy, contextual constraint, and sentence position), five studies on the P600 (agreement, tense, word category, subcategorization and garden-path sentences), and a study on the semantic P600 in role reversal anomalies. Since ERPs are learning signals, this account explains adaptation of ERP amplitude to within-experiment frequency manipulations and the way ERP effects are shaped by word predictability in earlier sentences. Moreover, it predicts that ERPs can change over language development. The model provides an account of the sensitivity of ERPs to expectation mismatch, the relative timing of the N400 and P600, the semantic nature of the N400, the syntactic nature of the P600, and the fact that ERPs can change with experience. This approach suggests that comprehension ERPs are related to sentence production and language acquisition mechanisms.

Error-based learning and lexical competition in word production: Evidence from multilingual naming.

We tested whether learning associated to lexical selection is error-based, and whether lexical selection is competitive by assessing the after-effects of producing words on subsequent production of semantic competitors differing in degree of error (translation equivalents). Speakers named pictures or words in one language (part A), and then named the same set of pictures (old set) and a new set in another language (part B). RTs for the old set (i.e., translation equivalents) were larger than for the new set (i.e., items which not have been named previously in another language). Supporting that learning is error-based, this cost was mostly larger after naming in a language with a higher degree of error (L2 vs. L1). Supporting that lexical selection is competitive, after naming in a language with a high degree of error (L3), the cost was larger for naming in another language with a high degree of error (L2 vs. L1).

Neural signatures of reward and sensory error feedback processing in motor learning.

At least two distinct processes have been identified by which motor commands are adapted according to movement-related feedback: reward-based learning and sensory error-based learning. In sensory error-based learning, mappings between sensory targets and motor commands are recalibrated according to sensory error feedback. In reward-based learning, motor commands are associated with subjective value, such that successful actions are reinforced. We designed two tasks to isolate reward- and sensory error-based motor adaptation, and we used electroencephalography in humans to identify and dissociate the neural correlates of reward and sensory error feedback processing. We designed a visuomotor rotation task to isolate sensory error-based learning that was induced by altered visual feedback of hand position. In a reward learning task, we isolated reward-based learning induced by binary reward feedback that was decoupled from the visual target. A fronto-central event-related potential called the feedback-related negativity (FRN) was elicited specifically by binary reward feedback but not sensory error feedback. A more posterior component called the P300 was evoked by feedback in both tasks. In the visuomotor rotation task, P300 amplitude was increased by sensory error induced by perturbed visual feedback and was correlated with learning rate. In the reward learning task, P300 amplitude was increased by reward relative to nonreward and by surprise regardless of feedback valence. We propose that during motor adaptation the FRN specifically reflects a reward-based learning signal whereas the P300 reflects feedback processing that is related to adaptation more generally. NEW & NOTEWORTHY We studied the event-related potentials evoked by feedback stimuli during motor adaptation tasks that isolate reward- and sensory error-based learning mechanisms. We found that the feedback-related negativity was specifically elicited by binary reward feedback, whereas the P300 was observed in both tasks. These results reveal neural processes associated with different learning mechanisms and elucidate which classes of errors, from a computational standpoint, elicit the feedback-related negativity and P300.