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Interlimb differences in reach control could impact the learning of a motor sequence that requires whole-arm movements. The purpose of this study was to investigate the learning of an implicit, 3-dimensional whole-arm sequence task with the non-dominant left arm compared to the dominant right arm. Thirty-one right-hand dominant adults completed two consecutive days of practice of a motor sequence task presented in a virtual environment with either their dominant right or non-dominant left arm. Targets were presented one-at-a-time alternating between Random and Repeated sequences. Task performance was indicated by the time to complete the sequence (response time), and kinematic measures (hand path distance, peak velocity) were used to examine how movements changed over time. While the Left Arm group was slower than the Right Arm group at baseline, both groups significantly improved response time with practice with the Left Arm group demonstrating greater gains. The Left Arm group improved performance by decreasing hand path distance (straighter path to targets) while the Right Arm group improved performance through a smaller decrease in hand path distance combined with increasing peak velocity. Gains made during practice on Day 1 were retained on Day 2 for both groups. Overall, individuals reaching with the non-dominant left arm learned the whole-arm motor sequence task but did so through a different strategy than individuals reaching with the dominant right arm. The strategy adopted for the learning of movement sequences that require whole-arm movements may be impacted by differences in reach control between the nondominant and dominant arms.

Existing methods for measuring implicit sequence-learning consciousness are conducted offline. Based on the traditional measurement of cued-generation task, this study implemented an online measurement method by converting a generation task into a forced-choice task to observe the dynamic changes of consciousness in the implicit sequence-learning process. In this study, we compared the performance of online measurement task and traditional sequence-learning tasks in 31 university students. The results revealed that the online indicators were significantly correlated with classic consciousness indicators and typical ERP components of consciousness. Without affecting the development of consciousness, the online measurement indicators were found to promptly and effectively reflect the gradually changing progression of consciousness in implicit sequence learning.

Motor imagery (MI) and physical practice (PP) have been seen as parallel processes that can drive acquisition of motor skills. Emerging evidence, however, suggests these two processes may be fundamentally different, whereby MI-based motor skill acquisition relies more on effector-independent encoding of movement relative to PP. This alternate view is supported by evidence where real and virtual lesions to brain areas involved in visuospatial processing impair MI-based skill acquisition, and via behavioural studies showing perceptual, but not motor, transfer impairs skill acquisition via MI whereas this effect is reversed in PP. This study further investigated the degree to which MI utilizes effector-independent encoding of movement by investigating the role of the supplementary motor area (SMA), an area involved in perceptual to motor transformations, in MI-based motor skill acquisition. Sixty-four participants completed a serial reaction time paradigm following assignment to one of four groups based on training modality (MI or PP) and stimulation type (sham stimulation or continuous theta burst stimulation to inhibit the SMA). Faster reaction times (RTs) to elements of a repeated sequence in comparison to randomly generated elements indicated that sequence-specific learning occurred. Learning occurred in both PP and MI, with the magnitude of learning significantly smaller in MI. Inhibitory stimulation impaired learning in both modalities. In the context of a framework that distinguishes effector-independent and -dependent components of learning, these findings indicate the SMA plays a role in developing motor chunks in both PP and MI facilitating effector-independent learning in both modalities.

The influence of sleep on motor skill consolidation has been a research topic of increasing interest. In this study, we distinguished general skill learning from sequence-specific learning in a probabilistic implicit sequence learning task (alternating serial reaction time) in young and old adults before and after a 12-h offline interval which did or did not contain sleep (p.m.-a.m. and a.m.-p.m. groups, respectively). The results showed that general skill learning, as assessed via overall reaction time, improved offline in both the young and older groups, with the young group improving more than the old. However, the improvement was not sleep-dependent, in that there was no difference between the a.m.-p.m. and p.m.-a.m. groups. We did not find sequence-specific offline improvement in either age group for the a.m.-either p.m. or p.m.-a.m. groups, suggesting that consolidation of this kind of implicit motor sequence learning may not be influenced by sleep.

Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.

Motor imagery (MI), the mental rehearsal of motor tasks, has promise as a therapy in post-stroke rehabilitation. The potential effectiveness of MI is attributed to the facilitation of plasticity in numerous brain regions akin to those recruited for physical practice. It is suggested, however, that MI relies more heavily on regions commonly affected post-stroke, including left hemisphere parietal regions involved in visuospatial processes. However, the impact of parietal damage on MI-based skill acquisition that underlies rehabilitation remains unclear. Here, we examine the contribution of the left inferior parietal lobule (IPL) to MI using inhibitory transcranial magnetic stimulation (TMS) and an MI-based implicit sequence learning (ISL) paradigm. Participants ( N  = 27) completed the MI-based ISL paradigm after receiving continuous theta burst stimulation to the left IPL (TMS), or with the coil angled away from the scalp (sham). Reaction time differences (dRT) and effect sizes between implicit and random sequences assessed success of MI-based learning. Mean dRT for the sham group was 36.1 ± 28.2 ms ( d  = 0.71). Mean dRT in the TMS group was 7.7 ± 38.5 ms ( d  = 0.11). These results indicate that inhibition of the left IPL impaired MI-based learning. We conclude that the IPL and likely the visuospatial processes it mediates are critical for MI performance and thus MI-based skill acquisition or learning. Ultimately, these findings have implications for the use of MI in post-stroke rehabilitation.

We recently reported that visuospatial working memory capacity predicts the rate of explicit motor sequence learning (Bo and Seidler in J Neurophysiol 101:3116–3125, 2009 ). In the current study, we evaluated relationships between visuospatial and verbal working memory and implicit performance change in the serial reaction time (SRT) task. Participants performed two computerized working memory tasks adapted from change detection working memory assessments, an implicit SRT task, and several neuropsychological tests. We observed significant correlations between visuospatial working memory (VSWM) and verbal working memory (VWM) performance. VSWM, VWM, and card rotation task were each significantly correlated with the rate of reaction time improvement in the SRT task. Multiple linear regression analysis revealed that VSWM explained a significant portion of the variance in rate of SRT performance change (exponential fit to the performance curve) across individual participants, and the addition of VWM did not significantly improve the model. These findings suggest that VSWM plays a role in the implicit performance improvement of second-order conditional sequences.

Implicit motor sequence learning refers to an important human ability to acquire new motor skills through the repeated performance of a motor sequence. This learning process is characterized by slow, incremental gains of motor performance. The present fMRI study was developed to better delineate the areas supporting these temporal dynamics of learning. By using the serial color matching paradigm, our study focused on the motor level of sequence learning and tracked the time course of learning-related neural changes. Imaging results showed a significant contribution of the left anterior hippocampus in an early sequence acquisition stage (first scanning session) as well as during a later stage with stabilized learning effects (second scanning session). Hippocampal activation significantly correlated with the behavioral learning process and was affected by a change of the motor sequence. These results suggest a strong involvement of the hippocampus in implicit motor sequence learning. On the other hand, a very extensive and bilateral neural network of parietal, temporal and frontal cortical areas (including SMA, pre-SMA) together with parts of the cerebellum and striatum were found to play a role during random visuo-motor task performance.

Implicit sequence learning involves learning about dependencies in sequences of events without intent to learn or awareness of what has been learned. Sequence learning is related to striatal dopamine levels, striatal activation, and integrity of white matter connections. People with Parkinson's disease (PD) have degeneration of dopamine-producing neurons, leading to dopamine deficiency and therefore striatal deficits, and they have difficulties with sequencing, including complex language comprehension and postural stability. Most research on implicit sequence learning in PD has used motor-based tasks. However, because PD presents with motor deficits, it is difficult to assess whether learning itself is impaired in these tasks. The present study used an implicit sequence learning task with a reduced motor component, the Triplets Learning Task (TLT). People with PD and age- and education-matched healthy older adults completed three sessions (each consisting of 10 blocks of 50 trials) of the TLT. Results revealed that the PD group was able to learn the sequence, however, when learning was examined using a Half Blocks analysis (Nemeth et al., 2013), which compared learning in the 1st 25/50 trials of all blocks to that in the 2nd 25/50 trials, the PD group showed significantly less learning than Controls in the 2nd Half Blocks, but not in the 1st. Nemeth et al. (2013) hypothesized that the 1st Half Blocks involve recall and reactivation of the sequence learned, thus reflecting hippocampal-dependent learning, while the 2nd Half Blocks involve proceduralized behavior of learned sequences, reflecting striatal-based learning. The present results suggest that the PD group had intact hippocampal-dependent implicit sequence learning, but impaired striatal-dependent learning. Thus, sequencing deficits in PD are likely due to striatal impairments, but other brain systems, such as the hippocampus, may be able to partially compensate for striatal decline to improve performance.

The present fMRI study investigated the neural areas involved in implicit perceptual sequence learning. To obtain more insight in the functional contributions of the brain areas, we tracked both the behavioral and neural time course of the learning process, using a perceptual serial color matching task. Next, to investigate whether the neural time course was specific for perceptual information, imaging results were compared to the results of implicit motor sequence learning, previously investigated using an identical serial color matching task (Gheysen et al., 2010). Results indicated that implicit sequences can be acquired by at least two neural systems: the caudate nucleus and the hippocampus, having different operating principles. The caudate nucleus contributed to the implicit sequence learning process for perceptual as well as motor information in a similar and gradual way. The hippocampus, on the other hand, was engaged in a much faster learning process which was more pronounced for the motor compared to the perceptual task. Interestingly, the perceptual and motor learning process occurred on a comparable implicit level, suggesting that consciousness is not the main determinant factor dissociating the hippocampal from the caudate learning system. This study is not only the first to successfully and unambiguously compare brain activation between perceptual and motor levels of implicit sequence learning, it also provides new insights into the specific hippocampal and caudate learning function.