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Automaticity is still ill-understood, and its relation to habit formation and skill acquisition is highly debated. Recently, the principle of caching has been advanced as a potentially promising avenue for studying automaticity. It is roughly understood as a means of storing direct input-output associations in a manner that supports instant lookup. We raise various concerns that should be addressed before the theoretical progress afforded by this principle can be evaluated. Is caching merely a metaphor for computer caching or is it a computational model that can be used to derive testable predictions? How do the short-term and long-term effects of automaticity relate to the distinction between working memory and long-term memory? Does caching apply to stimulus-response associations – as already suggested by Logan's instance theory – or to algorithms, too? How much practice is required for caching and how does caching depend on the task's type? What is the relation between control processes and caching as these pertain to the possible suppression of automatic processes? Dealing with these questions will arguably also advance our understanding of automaticity. [ABSTRACT FROM AUTHOR]

Byline: Lior Shmuelof (Department of Neurobiology Hebrew Univeristy, Jerusalem, Israel); Ehud Zohary (Department of Neurobiology Hebrew Univeristy, Jerusalem, Israel, Interdisciplinary Center for Neural Computation Hebrew University, Jerusalem, Israel, liorsh@pob.huji.ac.il) Keywords: [...]

A variety of eye tracking studies have demonstrated that young children with ASD gaze at images and movies of social interactions differently than typically developing children. These findings have supported the hypothesis that gaze behavior differences are generated by a weaker preference for social stimuli in ASD children. The hypothesis assumes that gaze differences are not caused by abnormalities in oculomotor function including saccade frequency and kinematics. Previous studies of oculomotor function have mostly been performed with school‐age children, adolescents, and adults using visual search, anti‐saccade, and gap saccade tasks that are less suitable for young pre‐school children. Here, we examined oculomotor function in 144 children (90 with ASD and 54 controls), 1–10‐years‐old, as they watched two animated movies interleaved with the presentation of multiple salient stimuli that elicited saccades‐to‐targets. The results revealed that the number of fixations, fixation duration, number of saccades, saccade duration, saccade accuracy, and saccade latency did not differ significantly across groups. Minor initial differences in saccade peak velocity were not supported by analysis with a linear mixed model. These findings suggest that most children with ASD exhibit similar oculomotor function to that of controls, when performing saccades‐to‐targets or freely viewing child‐friendly movies. This suggests that previously reported gaze abnormalities in children with ASD are not due to underlying oculomotor deficiencies. Lay Summary This study demonstrates that children with ASD perform similar eye movements to those of controls when freely observing movies or making eye movements to targets. Similar results were apparent across groups in the number of eye movements, their accuracy, duration, and other measures that assess eye movement control. These findings are important for interpreting previously reported differences in gaze behavior of children with ASD, which are likely due to atypical social preferences rather than impaired control of eye movements. [ABSTRACT FROM AUTHOR]

Motor skill learning involves improvement in feedforward control, the ability to execute a motor plan more reliably, and feedback control, the ability to adjust the motor plan on the fly. The dependence between these control components and the association between training conditions and their improvement have not been directly examined. This study characterizes the contribution of feedforward and feedback control components to motor skill learning using the arc-pointing task (APT), a drawing task that requires high motor acuity. In experiment 1, the performance of three groups of subjects was tested before and after training with online visual feedback (OF group), with knowledge of performance feedback that was presented after movement completion (KP group), and with both online and KP feedback (KP + OF group). Although the improvement of the OF group was not different from the improvement of the KP + OF group, comparison of the KP and KP + OF groups revealed an advantage to the KP group in the fast test speed, suggesting that training without online feedback leads to a greater improvement in feedforward control. In experiment 2, subject’s improvement was examined using test probes for estimating feedback and feedforward control. Both KP + OF and KP groups showed improvement in feedforward and feedback conditions with a trend toward a greater improvement of the KP group. Our results suggest that online visual feedback suppresses improvement in feedforward control during motor skill learning. [ABSTRACT FROM AUTHOR]

Motor adaptation, the adjustment of a motor output in face of changes in the environment, may operate at different rates. When human participants encounter repeated or consistent perturbations, their corrections for the experienced errors are larger compared with when the perturbations are new or inconsistent. Such modulations of error sensitivity were traditionally considered to be an implicit process that does not require attentional resources. In recent years, the implicit view of motor adaptation has been challenged by evidence showing a contribution of explicit strategies to learning. These findings raise a fundamental question regarding the nature of the error sensitivity modulation processes. We tested the effect of explicit control on error sensitivity in a series of experiments, in which participants controlled a screen cursor to virtual targets. We manipulated environmental consistency by presenting rotations in random (low consistency) or random walk (high consistency) sequences and illustrated that perturbation consistency affects the rate of adaptation, corroborating previous studies. When participants were instructed to ignore the cursor and move directly to the target, thus eliminating the contribution of explicit strategies, consistency-driven error sensitivity modulation was not detected. In addition, delaying the visual feedback, a manipulation that affects implicit learning, did not influence error sensitivity under consistent perturbations. These results suggest that increases of learning rate in consistent environments are attributable to an explicit rather than implicit process in sensorimotor adaptation. [ABSTRACT FROM AUTHOR]

Motor acuity is considered to be the outcome of prolonged practice and to involve morphological changes in the motor cortex. We have previously designed a curved pointing task, the arc pointing task (APT), to study motor acuity acquisition, defined as a change in the speed-accuracy tradeoff function (SAF) of the task. Here, we studied the generalization of motor acuity between hands and between tasks (drawing the arc in the opposite direction and with the untrained hand) and the effect of training duration on motor acuity. We report that training-induced motor acuity improvement did not generalize across hands and across tasks performed with the same hand, suggesting a task-specific representation of motor acuity. To our surprise, the largest gains in motor acuity, measured both by changes in SAF and by improvement in multiple kinematic variables, were seen following a short exposure to the task. Our results suggest that motor acuity training-induced improvement is task specific and that motor acuity starts to improve following a very short practice. NEW & NOTEWORTHY We report that training induced motor acuity improvement does not generalize from one hand to another or between movements that are performed with the same effector. Furthermore, significant improvements in acuity were found following a very short exposure to the task (∼20 trials). Therefore, our results suggest that the nervous system has the capacity to rapidly improve motor acuity. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) based brain computer interfaces (BCI) detect changes in brain activity associated with imaginary limb movements, and translate them into device commands. MI based BCIs require training, during which the user gradually learns how to control his or her brain activity with the help of feedback. Additionally, machine learning techniques are frequently used to boost BCI performance and to adapt the decoding algorithm to the user's brain. Thus, both the brain and the machine need to adapt in order to improve performance. To study the utility of co-adaptive training in the BCI paradigm and the time scales involved, we investigated the performance of two groups of subjects, in a 4-day MI experiment using EEG recordings. One group (control, n = 9 subjects) performed the BCI task using a fixed classifier based on MI data from day 1. In the second group (experimental, n = 9 subjects), the classifier was regularly adapted based on brain activity patterns during the experiment days. We found that the experimental group showed a significantly larger change in performance following training compared to the control group. Specifically, although the experimental group exhibited a decrease in performance between days, it showed an increase in performance within each day, which compensated for the decrease. The control group showed decreases both within and between days. A correlation analysis in subjects who had a notable improvement in performance following training showed that performance was mainly associated with modulation of power in the α frequency band. To conclude, continuous updating of the classification algorithm improves the performance of subjects in longitudinal BCI training. [ABSTRACT FROM AUTHOR]

To adapt to deterministic force perturbations that depend on the current state of the hand, internal representations are formed to capture the relationships between forces experienced and motion. However, information from multiple modalities travels at different rates, resulting in intermodal delays that require compensation for these internal representations to develop. To understand how these delays are represented by the brain, we presented participants with delayed velocity-dependent force fields, i.e., forces that depend on hand velocity either 70 or 100 ms beforehand. We probed the internal representation of these delayed forces by examining the forces the participants applied to cope with the perturbations. The findings showed that for both delayed forces, the best model of internal representation consisted of a delayed velocity and current position and velocity. We show that participants relied initially on the current state, but with adaptation, the contribution of the delayed representation to adaptation increased. After adaptation, when the participants were asked to make movements with a higher velocity for which they had not previously experienced with the delayed force field, they applied forces that were consistent with current position and velocity as well as delayed velocity representations. This suggests that the sensorimotor system represents delayed force feedback using current and delayed state information and that it uses this representation when generalizing to faster movements. [ABSTRACT FROM AUTHOR]

When subjects are intentionally preparing a curved trajectory, they are engaged in a time-consuming trajectory planning process that is separate from target selection. To investigate the construction of such a plan, we examined the effect of artificially shortening preparation time on the performance of intentionally curved trajectories using the Timed Response task that enforces initiation of movements prematurely. Fifteen subjects performed obstacle avoidance movements toward one of four targets that were presented 25 or 350 ms before the "go" signal, imposing short and long preparation time conditions with mean values of 170 ms and 493 ms, respectively. While trajectories with short preparation times showed target specificity at their onset, they were significantly more variable and showed larger angular deviations from the lines connecting their initial position and the target, compared to the trajectories with long preparation times. Importantly, the trajectories of the short preparation time movements still reached their end-point targets accurately, with comparable movement durations. We hypothesize that success in the short preparation time condition is a result of an online control mechanism that allows further refinement of the plan during its execution and study this control mechanism with a novel trajectory analysis approach using minimum jerk optimization and geometrical modeling approaches. Results show a later agreement of the short preparation time trajectories with the optimal minimum jerk trajectory, accompanied by a later initiation of a parabolic segment. Both observations are consistent with the existence of an online trajectory planning process. Our results suggest that when preparation time is not sufficiently long, subjects execute a more variable and less optimally prepared initial trajectory and exploit online control mechanisms to refine their actions on the fly. [ABSTRACT FROM AUTHOR]

Although motor adaptation is typically rapid, accumulating evidence shows that it is also associated with long-lasting behavioral and neuronal changes. Two processes were suggested to explain the formation of long-term motor memories: recall, reflecting a retrieval of previous motor actions, and faster relearning, reflecting an increased sensitivity to errors. Although these manifestations of motor memories were initially demonstrated in the context of adaptation experiments in reaching, indications of long-term motor memories were also demonstrated recently in other kinds of adaptation such as in locomotor adaptation. Little is known about the neural processes that underlie these distinct aspects of memory. We hypothesize that recall and faster relearning reflect different learning processes that operate at the same time and depend on different neuronal networks. Seventeen subjects performed a multisession locomotor adaptation experiment in the laboratory, together with resting-state and localizer fMRI scans, after the baseline and the locomotor adaptation sessions.Wereport a modulation of the cerebellar-thalamic- cortical and cerebellar- basal ganglia networks after locomotor adaptation. Interestingly, whereas thalamic- cortical baseline connectivity was correlated with recall, cerebellar-thalamic baseline connectivity was correlated with faster relearning. Our results suggest that separate neuronal networks underlie error sensitivity and retrieval components. Individual differences in baseline resting-state connectivity can predict idiosyncratic combination of these components. [ABSTRACT FROM AUTHOR]

The process of sensorimotor adaptation is considered to be driven by errors. While sensory prediction errors, defined as the difference between the planned and the actual movement of the cursor, drive implicit learning processes, target errors (e.g., the distance of the cursor from the target) are thought to drive explicit learning mechanisms. This distinction was mainly studied in the context of arm reaching tasks where the position and the size of the target were constant. We hypothesize that in a dynamic reaching environment, where subjects have to hit moving targets and the targets' dynamic characteristics affect task success, implicit processes will benefit from target errors as well. We examine the effect of target errors on learning of an unnoticed perturbation during unconstrained reaching movements. Subjects played a Pong game, in which they had to hit a moving ball by moving a paddle controlled by their hand. During the game, the movement of the paddle was gradually rotated with respect to the hand, reaching a final rotation of 25°. Subjects were assigned to one of two groups: The high-target error group played the Pong with a small ball, and the low-target error group played with a big ball. Before and after the Pong game, subjects performed open-loop reaching movements toward static targets with no visual feedback. While both groups adapted to the rotation, the postrotation reaching movements were directionally biased only in the small-ball group. This result provides evidence that implicit adaptation is sensitive to target errors. [ABSTRACT FROM AUTHOR]

Cerebral Palsy (CP) results from an insult to the developing brain and is associated with deficits in locomotor and manual skills and in sensorimotor adaptation. We hypothesized that the poor sensorimotor adaptation in persons with CP is related to their high execution variability and does not reflect a general impairment in adaptation learning. We studied the interaction between performance variability and adaptation deficits using a multi-session locomotor adaptation design in persons with CP. Six adolescents with diplegic CP were exposed, during a period of 15 weeks, to a repeated split-belt treadmill perturbation spread over 30 sessions and were tested again 6 months after the end of training. Compared to age-matched healthy controls, subjects with CP showed poor adaptation and high execution variability in the first exposure to the perturbation. Following training they showed marked reduction in execution variability and an increase in learning rates. The reduction in variability and the improvement in adaptation were highly correlated in the CP group and were retained 6 months after training. Interestingly, despite reducing their variability in the washout phase, subjects with CP did not improve learning rates during washout phases that were introduced only four times during the experiment. Our results suggest that locomotor adaptation in subjects with CP is related to their execution variability. Nevertheless, while variability reduction is generalized to other locomotor contexts, the development of savings requires both reduction in execution variability and multiple exposures to the perturbation. [ABSTRACT FROM AUTHOR]

When movements are perturbed in adaptation tasks, humans and other animals show incomplete compensation, tolerating small but sustained residual errors that persist despite repeated trials. State-space models explain this residual asymptotic error as interplay between learning from error and reversion to baseline, a form of forgetting. Previous work using zero-error-clamp trials has shown that reversion to baseline is not obligatory and can be overcome by manipulating feedback. We posited that novel error-clamp trials, in which feedback is constrained but has nonzero error and variance, might serve as a contextual cue for recruitment of other learning mechanisms that would then close the residual error. When error clamps were nonzero and had zero variance, human subjects changed their learning policy, using exploration in response to the residual error, despite their willingness to sustain such an error during the training block. In contrast, when the distribution of feedback in clamp trials was naturalistic, with persistent mean error but also with variance, a statespace model accounted for behavior in clamps, even in the absence of task success. Therefore, when the distribution of errors matched those during training, state-space models captured behavior during both adaptation and error-clamp trials because error-based learning dominated; when the distribution of feedback was altered, other forms of learning were triggered that did not follow the state-space model dynamics exhibited during training. The residual error during adaptation appears attributable to an error-dependent learning process that has the property of reversion toward baseline and that can suppress other forms of learning. [ABSTRACT FROM AUTHOR]

A methodology for investigating learning is developed using activation distributions, as opposed to standard voxel-level interaction tests. The approach uses tests of dimensionality to consider the ensemble of paired changes in voxel activation. The developed method allows for the investigation of non-focal and non-localized changes due to learning. In exchange for increased power to detect learning-based changes, this procedure sacrifices the localization information gained via voxel-level interaction testing. The test is demonstrated on an arc-pointing motor task for the study of motor learning, which served as the motivation for this methodological development. The proposed framework considers activation distribution, while the specific proposed test investigates linear tests of dimensionality. This paper includes: the development of the framework, a large scale simulation study, and the subsequent application to a study of motor learning in healthy adults. While the performance of the method was excellent when model assumptions held, complications arose in instances of massive numbers of null voxels or varying angles of principal dimension across subjects. Further analysis found that careful masking addressed the former concern, while an angle correction successfully resolved the latter. The simulation results demonstrated that the study of linear dimensionality is able to capture learning effects. The motivating data set used to illustrate the method evaluates two similar arc-pointing tasks, each over two sessions, with training on only one of the tasks in between sessions. The results suggests different activation distribution dimensionality when considering the trained and untrained tasks separately. Specifically, the untrained task evidences greater activation distribution dimensionality than the trained task. However, the direct comparison between the two tasks did not yield a significant result. The nature of the indication for greater dimensionality in the untrained task is explored and found to be non-linear variation in the data. [ABSTRACT FROM AUTHOR]

We recently defined a component of motor skill learning as "motor acuity," quantified as a shift in the speed-accuracy trade-off function for a task. These shifts are primarily driven by reductions in movement variability. To determine the neural correlates of improvement in motor acuity, we devised a motor task compatible with magnetic resonance brain imaging that required subjects to make finely controlled wrist movements under visual guidance. Subjects were imaged on day 1 and day 5 while they performed this task and were trained outside the scanner on intervening days 2, 3, and 4. The potential confound of performance changes between days 1 and 5 was avoided by constraining movement time to a fixed duration. After training, subjects showed a marked increase in success rate and a reduction in trial-by-trial variability for the trained task but not for an untrained control task, without changes in mean trajectory. The decrease in variability for the trained task was associated with increased activation in contralateral primary motor and premotor cortical areas and in ipsilateral cerebellum. A global nonlocalizing multivariate analysis confirmed that learning was associated with increased overall brain activation. We suggest that motor acuity is acquired through increases in the number of neurons recruited in contralateral motor cortical areas and in ipsilateral cerebellum, which could reflect increased signal-to-noise ratio in motor output and improved state estimation for feedback corrections, respectively. [ABSTRACT FROM AUTHOR]

Faster relearning of an external perturbation, savings, offers a behavioral linkage between motor learning and memory. To explain savings effects in reaching adaptation experiments, recent models suggested the existence of multiple learning components, each shows different learning and forgetting properties that may change following initial learning. Nevertheless, the existence of these components in rhythmic movements with other effectors, such as during locomotor adaptation, has not yet been studied. Here, we study savings in locomotor adaptation in two experiments; in the first, subjects adapted to speed perturbations during walking on a split-belt treadmill, briefly adapted to a counter-perturbation and then readapted. In a second experiment, subjects readapted after a prolonged period of washout of initial adaptation. In both experiments we find clear evidence for increased learning rates (savings) during readaptation. We show that the basic error-based multiple timescales linear state space model is not sufficient to explain savings during locomotor adaptation. Instead, we show that locomotor adaptation leads to changes in learning parameters, so that learning rates are faster during readaptation. Interestingly, we find an intersubject correlation between the slow learning component in initial adaptation and the fast learning component in the readaptation phase, suggesting an underlying mechanism for savings. Together, these findings suggest that savings in locomotion and in reaching may share common computational and neuronal mechanisms; both are driven by the slow learning component and are likely to depend on cortical plasticity. [ABSTRACT FROM AUTHOR]

The human motor system rapidly adapts to systematic perturbations but the adapted behavior seems to be forgotten equally rapidly. The reason for this forgetting is unclear, as is how to overcome it to promote long-term learning. Here we show that adapted behavior can be stabilized by a period of binary feedback about success and failure in the absence of vector error feedback. We examined die time course of decay after adaptation to a visuomotor rotation through a visual error-clamp condition--trials in which subjects received false visual feedback showing perfect directional performance, regardless of the movements they actually made. Exposure to this error-clamp following initial visuomotor adaptation led to a rapid reversion to baseline behavior. In contrast, exposure to binary feedback after initial adaptation turned the adapted state into a new baseline, to which subjects reverted after transient exposure to another visuomotor rotation. When both binary feedback and vector error were present, some subjects exhibited rapid decay to the original baseline, while others persisted in the new baseline. We propose that learning can be decomposed into two components--a fast-learning, fast-forgetting adaptation process that is sensitive to vector errors and insensitive to task success, and a second process driven by success that learns more slowly but is less susceptible to forgetting. These two learning systems may be recruited to different degrees across individuals. Understanding this competitive balance and exploiting the long-term retention properties of learning through reinforcement is likely to be essential for successful neuro-rehabilitation. [ABSTRACT FROM AUTHOR]

The public pays large sums of money to watch skilled motor performance. Notably, however, in recent decades motor skill learning (performance improvement beyond baseline levels) has received less experimental attention than motor adaptation (return to baseline performance in the setting of an external perturbation). Motor skill can be assessed at the levels of task success and movement quality, but the link between these levels remains poorly understood. We devised a motor skill task that required visually guided curved movements of the wrist without a perturbation, and we defined skill learning at the task level as a change in the speed-accuracy trade-off function (SAF). Practice in restricted speed ranges led to a global shift of the SAF. We asked how the SAF shift maps onto changes in trajectory kinematics, to establish a link between task-level performance and fine motor control. Although there were small changes in mean trajectory, improved performance largely consisted of reduction in trial-to-trial variability and increase in movement smoothness. We found evidence for improved feedback control, which could explain the reduction in variability but does not preclude other explanations such as an increased signal-tonoise ratio in cortical representations. Interestingly, submovement structure remained learning invariant. The global generalization of the SAF across a wide range of difficulty suggests that skill for this task is represented in a temporally scalable network. We propose that motor skill acquisition can be characterized as a slow reduction in movement variability, which is distinct from faster model-based learning that reduces systematic error in adaptation paradigms. [ABSTRACT FROM AUTHOR]

Here we argue that general principles with regard to the contributions of the cerebellum, basal ganglia, and primary motor cortex to motor learning can begin to be inferred from explicit comparison across model systems and consideration of phylogeny. Both the cerebellum and the basal ganglia have highly conserved circuit architecture in vertebrates. The cerebellum has consistently been shown to be necessary for adaptation of eye and limb movements. The precise contribution of the basal ganglia to motor learning remains unclear but one consistent finding is that they are necessary for early acquisition of novel sequential actions. The primary motor cortex allows independent control of joints and construction of new movement synergies. We suggest that this capacity of the motor cortex implies that it is a necessary locus for motor skill learning, which we argue is the ability to execute selected actions with increasing speed and precision. [ABSTRACT FROM AUTHOR]

The human primary motor cortex (M1) is robustly activated during visually guided hand movements. M1 multivoxel patterns of functional MRI activation are more correlated during repeated hand movements to the same targets than to greatly differing ones, and therefore potentially contain information about movement direction. It is unclear, however, whether direction specificity is due to the motor command, as implicitly assumed, or to the visual aspects of the task, such as the target location and the direction of the cursor's trajectory. To disambiguate the visual and motor components, different visual-to-motor transformations were applied during an fMRI scan, in which participants made visually guided hand movements in various directions. The first run was the "baseline" (i.e., visual and motor mappings were matched); in the second run ("rotation"), the cursor movement was rotated by 45° with respect to the joystick movement. As expected, positive correlations were seen between the M1 multivoxel patterns evoked by the baseline run and by the rotation run, when the two movements were matched in their movement direction but the visual aspects differed. Importantly, similar correlations were observed when the visual elements were matched but the direction of hand movement differed. This indicates that M1 is sensitive to both motor and visual components of the task. However, repeated observation of the cursor movement without concurrent joystick control did not elicit significant activation in M1 or any correlated patterns of activation. Thus, visual aspects of movement are encoded in M1 only when they are coupled with motor consequences. [ABSTRACT FROM AUTHOR]

In monkeys, neurons in the hand representation of the primary motor cortex (M1) are often tuned to the direction of hand movement, and there is evidence that these neurons are clustered according to their "preferred" direction of movement. However, this organizational principle has yet to be demonstrated in M1 of humans. We conducted a functional magnetic resonance imaging (fMRI) study in which participants used a joystick to move a cursor from a central origin to one of five equidistant targets. The fMRI signal of individual voxels was sensitive to the directional aspects of the reaching task and manifested direction-specific adaptation. Furthermore, the correlation between multivoxel patterns of responses for different movement directions depended on the angular distance between them. We conclude that M1 neurons are likely to be organized in clusters according to their preferred direction, since only such a coarse-grained representation can lead to directional selectivity of voxels, encompassing millions of neurons. A simple model that estimates cluster size suggests that the diameter of these clusters is on the order of a few hundred micrometers. [ABSTRACT FROM AUTHOR]

Summary: Neuropsychological case studies suggest the existence of two functionally separate visual streams: the ventral pathway, central for object recognition; and the dorsal pathway, engaged in visually guided actions. However, a clear dissociation between the functions of the two streams has not been decisively shown in intact humans. In this study, we demonstrate dissociation between dorsal and ventral fMRI activation patterns during observation of object manipulation video clips. Parietal areas, such as anterior intraparietal sulcus (aIPS) display grasp viewing-dependent adaptation (i.e., fMR adaptation during repeated viewing of the same object-grasping movement) as well as a contralateral preference for the viewed manipulating hand. Ventral regions, such as the fusiform gyrus, show similar characteristics (i.e., adaptation, contralateral preference), but these depend on object identity. Our results support the hypothesized functional specialization in the visual system and suggest that parietal areas (such as aIPS) are engaged in action recognition, as well as in action planning. [Copyright &y& Elsevier]

Mimicking hand actions made by someone facing us (that is, allocentric viewpoint) is typically performed with the opposite hand. Using functional magnetic resonance imaging (fMRI), we found a similar mirror-image representation of others' actions in the human anterior parietal cortex. Viewing egocentric-based actions elicited greater fMRI activation in the contralteral hemisphere (as in, self action), whereas observation of action seen from an allocentric viewpoint generated greater activation in the ipsilateral hemisphere. This mirror-like mapping occurs without active imitation, providing further evidence for an automatic action-simulation system in the parietal cortex. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including aspect of skill learning, explicit memory mechanisms and treatment of Huntington's disease.

The anterior part of the human intraparietal sulcus is known to be involved in visually guided grasping. This region is also active during the observation of object manipulation by others. Here, we explore the nature of action representation using functional magnetic resonance imaging (fMRI). Fourteen subjects observed video clips showing object manipulation by the right or left hand . The clips were presented in either the right or left peripheral visual field. The fMRI activation in the occipital cortex and in the caudal sections of the parietal cortex was specific to the visual-field location of the clips. In contrast, the principal factor determining the response in anterior intraparietal cortex was the identity of the observed hand. Furthermore, these "hand-specific" parietal areas also showed contralateral hand specificity during self action (i.e., object manipulation) without visual feedback. A similar selectivity for the identity of the observed hand was seen when using a region of interest analysis, focusing individually defined visuomotor voxels within the parietal cortex. This dual visuomotor grasping representation lends further evidence for the existence of a mirror system in humans and suggests that the anterior intraparietal cortex is involved in the specific motor simulation of hand actions. [ABSTRACT FROM AUTHOR]