Reward actively engages both implicit and explicit components in dual force field adaptation.

Motor learning occurs through multiple mechanisms, including unsupervised, supervised (error based), and reinforcement (reward based) learning. Although studies have shown that reward leads to an overall better motor adaptation, the specific processes by which reward influences adaptation are still unclear. Here, we examine how the presence of reward affects dual adaptation to novel dynamics and distinguish its influence on implicit and explicit learning. Participants adapted to two opposing force fields in an adaptation/deadaptation/error-clamp paradigm, where five levels of reward (a score and a digital face) were provided as participants reduced their lateral error. Both reward and control (no reward provided) groups simultaneously adapted to both opposing force fields, exhibiting a similar final level of adaptation, which was primarily implicit. Triple-rate models fit to the adaptation process found higher learning rates in the fast and slow processes and a slightly increased fast retention rate for the reward group. Whereas differences in the slow learning rate were only driven by implicit learning, the large difference in the fast learning rate was mainly explicit. Overall, we confirm previous work showing that reward increases learning rates, extending this to dual-adaptation experiments and demonstrating that reward influences both implicit and explicit adaptation. Specifically, we show that reward acts primarily explicitly on the fast learning rate and implicitly on the slow learning rates.NEW & NOTEWORTHY Here we show that rewarding participants' performance during dual force field adaptation primarily affects the initial rate of learning and the early timescales of adaptation, with little effect on the final adaptation level. However, reward affects both explicit and implicit components of adaptation. Whereas the learning rate of the slow process is increased implicitly, the fast learning and retention rates are increased through both implicit components and the use of explicit strategies.

ENIGMA: A Web Application for Running Online Artificial Grammar Learning Experiments.

Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA ( https://enigma-lang.org ) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners' performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331-361, 2020) to illustrate possible experimental designs and examine the quality of the collected data.

Interaction of dynamic error signals in saccade adaptation.

Motor adaptation maintains movement accuracy. To evaluate movement accuracy, motor adaptation relies on an error signal, generated by the movement target, while suppressing error signals from irrelevant objects in the vicinity. Previous work used static testing environments, where all information required to evaluate movement accuracy was available simultaneously. Using saccadic eye movements as a model for motor adaptation, we tested how movement accuracy is maintained in dynamic environments, where the availability of conflicting error signals varied over time. Participants made a vertical saccade toward a target (either a small square or a large ring). Upon saccade detection, two candidate stimuli were shown left and right of the target, and participants were instructed to discriminate a feature on one of the candidates. Critically, candidate stimuli were presented sequentially, and saccade adaptation, thus, had to resolve a conflict between a task-relevant and a task-irrelevant error signal that were separated in space and time. We found that the saccade target influenced several aspects of oculomotor learning. In presence of a small target, saccade adaptation evaluated movement accuracy based on the first available error signal after the saccade, irrespective of its task relevance. However, a large target not only allowed for greater flexibility when evaluating movement accuracy, but it also promoted a stronger contribution of strategic behavior when compensating inaccurate saccades. Our results demonstrate how motor adaptation maintains movement accuracy in dynamic environments, and how properties of the visual environment modulate the relative contribution of different learning processes.NEW & NOTEWORTHY Motor adaptation is typically studied in static environments, where all information that is required to evaluate movement accuracy is available simultaneously. Here, using saccadic eye movements as a model, we studied motor adaptation in a dynamic environment, where the availability of conflicting information about movement accuracy varied over time. We demonstrate that properties of the visual environment determine how dynamic movement errors are corrected.

Persistent changes in motor adaptation strategies after perturbations that require exploration of novel muscle activation patterns.

Motor skill learning requires the acquisition of novel muscle patterns and a new control policy-a process that requires time. In contrast, motor adaptation often requires only the adjustment of existing muscle patterns-a fast process. By altering the mapping of muscle activations onto cursor movements in a myoelectrically controlled virtual environment, we are able to create perturbations that require either the recombination of existing muscle synergies (compatible virtual surgery) or the learning of novel muscle patterns (incompatible virtual surgery). We investigated whether adaptation to a compatible surgery is affected by prior exposure to an incompatible surgery, i.e., a motor skill learning task. We found that adaptation to a compatible surgery was characterized by a decrease in the quality of muscle pattern reconstructions using the original synergies and an increase in reaction times only after exposure to an incompatible surgery. In contrast, prior exposure to a compatible surgery did not affect the learning process required to overcome an incompatible surgery. The fact that exposure to an incompatible surgery had a profound effect on the muscle patterns during the adaptation to a subsequent compatible surgery and not vice versa suggests that null space exploration, possibly combined with an explicit exploration strategy, is engaged during exposure to an incompatible surgery and remains enhanced during a new adaptation episode. We conclude that motor skill learning, requiring novel muscle activation patterns, leads to changes in the exploration strategy employed during a subsequent perturbation.NEW & NOTEWORTHY Motor skill learning requires the acquisition of novel muscle patterns, whereas motor adaptation requires adjusting existing ones. We wondered whether training a new motor skill affects motor adaptation strategies. We show that learning an incompatible perturbation, a complex skill requiring new muscle synergies, affects the muscle patterns observed during adaption to a compatible perturbation, which requires adjusting the existing synergies. Our results suggest that motor skill learning results in persistent changes in the exploration strategy.

Involvement of the prefrontal cortex in motor sequence learning: A functional near-infrared spectroscopy (fNIRS) study.

Our previous functional near-infrared spectroscopy (fNIRS) study on motor sequence learning (Polskaia et al., 2020) did not detect the same decrease in activity in the left dorsolateral prefrontal cortex (DLPFC) associated with movement automaticity, as reported by Wu et al. (2004). This was partly attributed to insufficient practice time to reach neural efficiency. Therefore, we sought to expand on our previous work to better understand the contribution of the prefrontal cortex (PFC) to motor sequence learning by examining learning across a longer period of time. Participants were randomly assigned to one of two groups: control or trained. fNIRS was acquired at three time points: pre-test, post-test, and retention. Participants performed four sequences (S1, S2, S3, and S4) of right-hand finger tapping. The trained group also underwent four days of practice of S1 and S2. No group differences in the left DLPFC and ventrolateral (VLPFC) were found between sessions for S1 and S2. Our findings revealed increased contribution from the right VLPFC in post-test for the trained group, which may reflect the active retrieval of explicit information from long-term memory. Our results suggest that despite additional practice time, explicit motor sequence learning requires the continued involvement of the PFC.

1-Back reinforcement symbolic-matching by humans: How do they learn it?

For humans, a distinction has been made between implicit and explicit learning. Implicit learning is thought to involve automatic processes of the kind involved in much Pavlovian conditioning, while explicit learning is thought to involve conscious hypothesis testing and rule formation, in which the subject's statement of the rule has been taken as evidence of explicit learning. Various methods have been used to determine if nonverbal animals are able to learn a task explicitly - among these is the 1-back reinforcement task in which feedback from performance on the current conditional discrimination trial is provided only after completion of the following trial. We propose that it is not whether an organism can learn the task, but whether they learn it rapidly, all-or-none, that provides a better distinction between the two kinds of learning. We had humans learn a symbolic matching, 1-back reinforcement task. Almost half of the subjects failed to learn the task, and of those who did, none described the 1-back rule. Thus, it is possible to learn this task without learning the 1-back rule. Furthermore, the backward learning functions for humans differ from those of pigeons. Human subjects who learned the task did so all-or-none, suggesting explicit learning. In earlier research with pigeons, they too showed significant learning of this task; however, backward learning functions suggested that they did so gradually over the course of several sessions of training and to a lower level of asymptotic accuracy than the humans, a result suggesting implicit learning was involved.

Implicit Motor Learning Strategies Benefit Dual-Task Performance in Patients with Stroke.

Background and Objectives: In stroke rehabilitation, the use of either implicit or explicit learning as a motor learning approach during dual tasks is common, but it is unclear which strategy is more beneficial. This study aims to determine the benefits of implicit versus explicit motor learning approaches in patients with stroke. Materials and Methods: Seventeen patients with stroke and 21 control participants were included. Motor learning was evaluated using the Serial Reaction Time Task (SRTT) in the context of dual-task conditions. The SRTT was conducted on two separate days: one day for implicit learning conditions and the other day for explicit learning conditions. Under the explicit learning conditions, a task rule was given to the participants before they started the task, but not under the implicit learning conditions. Learning scores were calculated for both implicit and explicit learning, and these scores were then compared within groups for patients with stroke and controls. We calculated the difference in learning scores between implicit and explicit learning and conducted a correlation analysis with the Trail Making Test (TMT) Parts A and B. Results: Learning scores on the SRTT were not different between implicit and explicit learning in controls but were significantly greater in patients with stroke for implicit learning than for explicit learning. The difference in learning scores between implicit and explicit learning in patients with stroke was correlated with TMT-A and showed a correlation trend with TMT-B. Conclusions: Implicit learning approaches may be effective in the acquisition of motor skills with dual-task demands in post-stroke patients with deficits in attention and working memory.

Task space exploration improves adaptation after incompatible virtual surgeries.

Humans have a remarkable capacity to learn new motor skills, a process that requires novel muscle activity patterns. Muscle synergies may simplify the generation of muscle patterns through the selection of a small number of synergy combinations. Learning of new motor skills may then be achieved by acquiring novel muscle synergies. In a previous study, we used myoelectric control to construct virtual surgeries that altered the mapping from muscle activity to cursor movements. After compatible virtual surgeries, which could be compensated by recombining subject-specific muscle synergies, participants adapted quickly. In contrast, after incompatible virtual surgeries, which could not be compensated by recombining existing synergies, participants explored new muscle patterns but failed to adapt. Here, we tested whether task space exploration can promote learning of novel muscle synergies required to overcome an incompatible surgery. Participants performed the same reaching task as in our previous study but with more time to complete each trial, thus allowing for exploration. We found an improvement in trial success after incompatible virtual surgeries. Remarkably, improvements in movement direction accuracy after incompatible surgeries occurred faster for corrective movements than for the initial movement, suggesting that learning of new synergies is more effective when used for feedback control. Moreover, reaction time was significantly higher after incompatible than compatible virtual surgeries, suggesting an increased use of an explicit adaptive strategy to overcome incompatible surgeries. Taken together, these results indicate that exploration is important for skill learning and suggest that human participants, with sufficient time, can learn new muscle synergies.NEW & NOTEWORTHY Motor skill learning requires the acquisition of novel muscle patterns, a slow adaptive process. Here we show that learning to control a cursor after an incompatible virtual surgery, a complex skill requiring new muscle synergies, is possible when enough time for task space exploration is provided. Our results suggest that learning new synergies is related to the exceptional human capacity to acquire a wide variety of novel motor skills with practice.

Separation of multiple motor memories through implicit and explicit processes.

Acquisition of multiple motor skills without interference is a remarkable ability in daily life. During adaptation to opposing perturbations, a common paradigm to study this ability, each perturbation can be successfully learned when a contextual follow-through movement is associated with the direction of the perturbation. It is still unclear, however, to what extent this learning engages the cognitive explicit process and the implicit process. Here, we untangled the individual contributions of the explicit and implicit components while participants learned opposing visuomotor perturbations, with a second unperturbed follow-through movement. In experiment 1, we replicated previous adaptation results and showed that follow-through movements also allow learning for opposing visuomotor rotations. For one group of participants in experiment 2, we isolated strategic explicit learning, while for another group we isolated the implicit component. Our data showed that opposing perturbations could be fully learned by explicit strategies, but when strategy was restricted, distinct implicit processes contributed to learning. In experiment 3, we examined whether learning is influenced by the disparity between the follow-through contexts. We found that the location of follow-through targets had little effect on total learning, yet it led to more instances in which participants failed to learn the task. In experiment 4, we explored the generalization capability to untrained targets. Participants showed near-flat generalization of the implicit and explicit processes. Overall, our results indicate that follow-through contextual cues might activate, in part, top-down cognitive factors that influence not only the dynamics of the explicit learning but also the implicit process.NEW & NOTEWORTHY Acquisition of multiple motor skills is a remarkable ability in everyday activities. Yet, the contribution of implicit and explicit learning processes during learning of multiple motor skills is still unclear. We sought to dissociate this ability during learning opposing visuomotor rotations, each associated with a contextual follow-through movement. We show that follow-through contextual cues influence activity of both implicit and explicit processes, suggesting that context-based top-down cognitive factors influence not only the explicit learning but also the implicit process.

Updating implicit contextual priors with explicit learning for the prediction of social and physical events.

For proper action understanding we can infer action intention from the kinematic features of movement (the event in terms of sensorial evidence) and/or from the contextual scenario in which the action occurs. In line with predictive coding theories, the implicit learning of statistical regularities between events and contextual cues strongly biases action prediction. Here, we assessed the relative sensitivity of contextual priors to an explicit learning aimed at reinforcing either context-based or event-based prediction. First, in an implicit learning phase we exposed participants to videos showing specific associations between a contextual cue and a particular event (action or shape) in order to create high or low contextual priors. Then, in an explicit learning phase we provided a feedback reinforcing the response suggested by the contextual prior or by the sensory evidence. We found that the former improved the ability to predict the unfolding of social or physical events embedded in high-probability contexts and worsened the prediction of those embedded in low-probability contexts. Conversely, the latter had weaker effects, ultimately failing to override the reliance on contextual priors. Further, we acknowledged an association between the extent of individual autistic traits and the ability to leverage explicit learning mechanisms encouraging perceptual information.

Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge.

Humans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected only in the structured condition, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in areas around the left pre-central gyrus as well as right temporo-frontal areas significantly predicted behavioral performance. Sensor level results confirmed this relationship between neural entrainment and subsequent explicit knowledge. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two aspects are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on factors such as sufficient exposure time and attention.

Use of explicit processes during a visually guided locomotor learning task predicts 24-h retention after stroke.

Implicit and explicit processes can occur within a single locomotor learning task. The combination of these learning processes may impact how individuals acquire/retain the task. Because these learning processes rely on distinct neural pathways, neurological conditions may selectively impact the processes that occur, thus, impacting learning and retention. Thus, our purpose was to examine the contribution of implicit and explicit processes during a visually guided walking task and characterize the relationship between explicit processes and performance/retention in stroke survivors and age-matched healthy adults. Twenty chronic stroke survivors and twenty healthy adults participated in a 2-day treadmill study. Day 1 included baseline, acquisition1, catch, acquisition2, and immediate retention phases, and day 2 included 24-h retention. During acquisition phases, subjects learned to take a longer step with one leg through distorted visual feedback. During catch and retention phases, visual feedback was removed and subjects were instructed to walk normally (catch) or how they walked during the acquisition phases (retention). Change in step length from baseline to catch represented implicit processes. Change in step length from catch to the end of acquisition2 represented explicit processes. A mixed ANOVA found no difference in the type of learning between groups (P = 0.74). There was a significant relationship between explicit processes and 24-h retention in stroke survivors (r = 0.47, P = 0.04) but not in healthy adults (r = 0.34, P = 0.15). These results suggest that stroke may not affect the underlying learning mechanisms used during locomotor learning, but that these mechanisms impact how well stroke survivors retain the new walking pattern.NEW & NOTEWORTHY This study found that stroke survivors used implicit and explicit processes similar to age-matched healthy adults during a visually guided locomotion learning task. The amount of explicit processes was related to how well stroke survivors retained the new walking pattern but not to how well they performed during the task. This work illustrates the importance of understanding the underlying learning mechanisms to maximize retention of a newly learned motor behavior.

Taking aim at the perceptual side of motor learning: exploring how explicit and implicit learning encode perceptual error information through depth vision.

Motor learning in visuomotor adaptation tasks results from both explicit and implicit processes, each responding differently to an error signal. Although the motor output side of these processes has been extensively studied, the visual input side is relatively unknown. We investigated if and how depth perception affects the computation of error information by explicit and implicit motor learning. Two groups of participants made reaching movements to bring a virtual cursor to a target in the frontoparallel plane. The Delayed group was allowed to reaim and their feedback was delayed to emphasize explicit learning, whereas the camped group received task-irrelevant clamped cursor feedback and continued to aim straight at the target to emphasize implicit adaptation. Both groups played this game in a highly detailed virtual environment (depth condition), leveraging a cover task of playing darts in a virtual tavern, and in an empty environment (no-depth condition). The delayed group showed an increase in error sensitivity under depth relative to no-depth. In contrast, the clamped group adapted to the same degree under both conditions. The movement kinematics of the delayed participants also changed under the depth condition, consistent with the target appearing more distant, unlike the Clamped group. A comparison of the delayed behavioral data with a perceptual task from the same individuals showed that the greater reaiming in the depth condition was consistent with an increase in the scaling of the error distance and size. These findings suggest that explicit and implicit learning processes may rely on different sources of perceptual information.NEW & NOTEWORTHY We leveraged a classic sensorimotor adaptation task to perform a first systematic assessment of the role of perceptual cues in the estimation of an error signal in the 3-D space during motor learning. We crossed two conditions presenting different amounts of depth information, with two manipulations emphasizing explicit and implicit learning processes. Explicit learning responded to the visual conditions, consistent with perceptual reports, whereas implicit learning appeared to be independent of them.

Neurocircuitry of Contingency Awareness in Pavlovian Fear Conditioning.

In Pavlovian fear conditioning, contingency awareness provides an indicator of explicit fear learning. A less studied aspect of fear-based psychopathologies and their treatment, awareness of learned fear is a common cause of distress in persons with such conditions and is a focus of their treatment. The present work is a substudy of a broader fear-conditioning fMRI study. Following fear conditioning, we identified a subset of individuals who did not exhibit explicit awareness of the CS-US contingency. This prompted an exploratory analysis of differences in "aware" versus "unaware" individuals after fear conditioning. Self-reported expectancies of the CS-US contingency obtained immediately following fear conditioning were used to differentiate the two groups. Results corrected for multiple comparisons indicated significantly greater BOLD signal in the bilateral dlPFC, right vmPFC, bilateral vlPFC, left insula, left hippocampus, and bilateral amygdala for the CS+>CS- contrast in the aware group compared with the unaware group (all p values ≤ 0.004). PPI analysis with a left hippocampal seed indicated stronger coupling with the dlPFC and vmPFC in the aware group compared with the unaware group (all p values ≤ 0.002). Our findings add to our current knowledge of the networks involved in explicit learning and awareness of conditioned fear, with important clinical implications.

Pigeons acquire the 1-back task: Implications for implicit versus explicit learning?

In humans, a distinction can be made between implicit or procedural learning (involving stimulus-response associations) and explicit or declarative learning (involving verbalizable rules) that is relatively easy to make in verbal humans. According to several investigators, it is also possible to make such a distinction in nonverbal animals. One way is by training them on a conditional discrimination task (e.g., matching-to-sample) in which reinforcement for correct choice on the current trial is delayed until after a choice is made on the next trial - a method known as the 1-back procedure. According to Smith, Jackson, and Church ( Journal of Comparative Psychology, 134(4), 423-434, 2020), the delay between the sample-correct-comparison response on one trial and reinforcement obtained on the next trial is too long for implicit (associative) learning. Thus, according to this theory, learning must be explicit. In the present experiments we trained pigeons using the 1-back procedure. In Experiment 1, pigeons were trained on red/green 1-back matching using a non-correction procedure. Some of the pigeons showed significant learning. When a correction procedure was introduced, all the pigeons showed evidence of learning. In Experiment 2, new pigeons learned red/green 1-back matching with the correction procedure. In Experiment 3, new pigeons learned symbolic 1-back matching with yellow and blue conditional stimuli and red/green choice stimuli. Thus, pigeons can learn using 1-back reinforcement. Although it would appear that the pigeons acquired this task explicitly, we believe that this procedure does not adequately distinguish between implicit and explicit learning.

Implicit motor learning in primary school children: A systematic review.

The aim of this study was to assess the current state of evidence and methodological quality of studies on implicit and explicit motor learning in both typically developing children and children with developmental disorders. A systematic literature review was conducted on the experimental literature published up to April 2020. A total of 25 studies were included. Studies were evaluated on methodological quality, paradigm used, and level of evidence. The results showed that implicit paradigms are as effective as explicit paradigms in both groups of children. Studies are predominantly experimental in nature involving mostly upper limb aiming tasks. The few studies that were performed outside the lab (n = 5) suggest superior efficacy of the implicit paradigm. Methodological quality varied between studies and was not always of sufficient standard to allow conclusions. In particular, manipulation checks were only performed in 13 studies (52% of all studies), limiting conclusions. Further progress can be made by focussing on improving methodological quality through retention testing by the inclusion of a control group, by the inclusion of a manipulation check, and via assessment of relevant co-variables, such as working memory, age, and motor competence.

A functional near-infrared spectroscopy (fNIRS) examination of how self-initiated sequential movements become automatic.

The neural mechanisms underlying movement automaticity have been investigated using PET and fMRI and more recently functional near-infrared spectroscopy (fNIRS). As fNIRS is an emerging technique, the objective of the present study was to replicate the functional magnetic resonance imaging-related motor sequence findings as reported by Wu et al. (J Neurophysiol 91:1690-1698, https://doi.org/10.1152/jn.01052.2003, 2004) using fNIRS. Seventeen right-handed participants practiced self-initiated sequential finger movements of two lengths (4 and 12) until a level of automaticity was achieved. Automaticity was evaluated by performing a visual-letter-counting task concurrently with the sequential finger movements. Our data were unable to replicate the pre-to-post-practice decrease in cortical activity in the left dorsolateral prefrontal cortex for both motor sequence tasks. The findings did reveal increased contribution from the right hemisphere following learning. The observed lateralization is suggestive of explicit learning and the involvement of working memory in motor sequence production.

A locomotor learning paradigm using distorted visual feedback elicits strategic learning.

Distorted visual feedback (DVF) during locomotion has been suggested to result in the development of a new walking pattern in healthy individuals through implicit learning processes. Recent work in upper extremity visuomotor rotation paradigms suggest that these paradigms involve implicit and explicit learning. Additionally, in upper extremity visuomotor paradigms, the verbal cues provided appear to impact how a behavior is learned and when this learned behavior is used. Here, in two experiments in neurologically intact individuals, we tested how verbal instruction impacts learning a new locomotor pattern on a treadmill through DVF, the transfer of that pattern to overground walking, and what types of learning occur (i.e., implicit vs. explicit learning). In experiment 1, we found that the instructions provided impacted the amount learned through DVF, but not the size of the aftereffects or the amount of the pattern transferred to overground walking. Additionally, the aftereffects observed were significantly different from the baseline walking pattern, but smaller than the behavior changes observed during learning, which is uncharacteristic of implicit sensorimotor adaptation. Thus, experiment 2 aimed to determine the cause of these discrepancies. In this experiment, when VF was not provided, individuals continued using the learned walking pattern when instructed to do so and returned toward their baseline pattern when instructed to do so. Based on these results, we conclude that DVF during locomotion results in a large portion of explicit learning and a small portion of implicit learning. NEW & NOTEWORTHY The results of this study suggest that distorted visual feedback during locomotor learning involves the development of an explicit strategy with only a small component of implicit learning. This is important because previous studies using distorted visual feedback have suggested that locomotor learning relies primarily on implicit learning. This paradigm, therefore, provides a new way to examine a different form of learning in locomotion.

Translating visual information into action predictions: Statistical learning in action and nonaction contexts.

Humans are sensitive to the statistical regularities in action sequences carried out by others. In the present eyetracking study, we investigated whether this sensitivity can support the prediction of upcoming actions when observing unfamiliar action sequences. In two between-subjects conditions, we examined whether observers would be more sensitive to statistical regularities in sequences performed by a human agent versus self-propelled 'ghost' events. Secondly, we investigated whether regularities are learned better when they are associated with contingent effects. Both implicit and explicit measures of learning were compared between agent and ghost conditions. Implicit learning was measured via predictive eye movements to upcoming actions or events, and explicit learning was measured via both uninstructed reproduction of the action sequences and verbal reports of the regularities. The findings revealed that participants, regardless of condition, readily learned the regularities and made correct predictive eye movements to upcoming events during online observation. However, different patterns of explicit-learning outcomes emerged following observation: Participants were most likely to re-create the sequence regularities and to verbally report them when they had observed an actor create a contingent effect. These results suggest that the shift from implicit predictions to explicit knowledge of what has been learned is facilitated when observers perceive another agent's actions and when these actions cause effects. These findings are discussed with respect to the potential role of the motor system in modulating how statistical regularities are learned and used to modify behavior.

Chunking, Conscious Processing, and EEG During Sequence Acquisition and Performance Pressure: A Comprehensive Test of Reinvestment Theory.

This study was designed to test the theorized link between reinvestment, motor chunks, and conscious processing, to provide a thorough examination of reinvestment theory. The authors measured electroencephalographic power and connectivity alongside self-reported conscious processing and behavioral indices of chunking in a 2 (group) × 5 (block) mixed-model design. A total of 55 individuals acquired a motor sequence (blocks A1, A2, A3, and A4) by relatively explicit (errorful) or implicit (errorless) paradigms. Then they performed in a pressure condition (block T). Results confirmed that chunking characterizes both modes of acquisition. However, explicit acquisition resulted in quicker chunking, reduced conscious processing, and increased cortical efficiency (left-temporal high-alpha power). In support of reinvestment theory, self-reported conscious processing tended to increase under pressure among explicit trainees only. In contrast to reinvestment theory, this had no adverse effect on performance. The results endorse explicit acquisition as an effective mode of training and provide a new neurophysiological explanation of this phenomenon.