Effect of visuomotor-map uncertainty on visuomotor adaptation

Vision and proprioception contribute to generating hand movement. If a conflict between the visual and proprioceptive feedback of hand position is given, reaching movement is disturbed initially but recovers after training. Although previous studies have predominantly investigated the adaptive change in the motor output, it is unclear whether the contributions of visual and proprioceptive feedback controls to the reaching movement are modified by visuomotor adaptation. To investigate this, we focused on the change in proprioceptive feedback control associated with visuomotor adaptation. After the adaptation to gradually introduce visuomotor rotation, the hand reached the shifted position of the visual target to move the cursor to the visual target correctly. When the cursor feedback was occasionally eliminated (probe trial), the end point of the hand movement was biased in the visual-target direction, while the movement was initiated in the adapted direction, suggesting the incomplete adaptation of proprioceptive feedback control. Moreover, after the learning of uncertain visuomotor rotation, in which the rotation angle was randomly fluctuated on a trial-by-trial basis, the end-point bias in the probe trial increased, but the initial movement direction was not affected, suggesting a reduction in the adaptation level of proprioceptive feedback control. These results suggest that the change in the relative contribution of visual and proprioceptive feedback controls to the reaching movement in response to the visuomotor-map uncertainty is involved in visuomotor adaptation, whereas feedforward control might adapt in a manner different from that of the feedback control.     

On-line corrections for visuomotor errors

This study was designed to determine how visual feedback mediates error corrections during reaching. We used visuomotor rotations to dissociate a cursor, representing finger position, from the actual finger location. We then extinguished cursor feedback at different distances from the start location to determine whether corrections were based on error extrapolation from prior cursor information. Results indicated that correction amplitude varied with the extent of cursor feedback. A second experiment tested specific aspects of error information that might mediate corrections to visuomotor rotations: rotation angle, distance between the finger and cursor positions and the duration of cursor exposure. Results showed that corrections did not depend on the amplitude of the rotation angle or the amount of time the cursor was shown. Instead, participants corrected for the cursor–finger distance, at the point where cursor feedback was last-seen. These findings suggest that within-trial corrections and inter-trial adaptation might employ different mechanisms.     

Changes in motor cortical activity during visuomotor adaptation

We examined neuronal activity in three motor cortical areas while a rhesus monkey adapted to novel visuomotor transforms. The monkey moved a joystick that controlled a cursor on a video screen. Each trial began with the joystick centered. Next, the cursor appeared in one of eight positions, arranged in a circle around a target stimulus at the center of the screen. To receive reinforcement, the monkey moved the joystick so that the cursor contacted the target continuously for 1s. The video monitor provided continuous visual feedback of both cursor and target position. With those elements of the task constant, we modified the transform between joystick movement and that of the cursor at the beginning of a block of trials. Neuronal activity was studied as the monkey adapted to these novel joystick-cursor transforms. Some novel tasks included spatial transforms such as single-axis inversions, asymmetric double-axis inversions and angular deviations (also known as rotations). Other tasks involved changes in the spatiotemporal pattern and magnitude of joystick movement. As the monkey adapted to various visuomotor tasks, 209 task-related neurons (selected for stable background activity) showed significant changes in their task-related activity: 88 neurons in the primary motor cortex (M1), 32 in the supplementary motor cortex (M2), and 89 in the caudal part of the dorsal premotor cortex (PMdc). Slightly more than half of the sample in each area showed significant changes in the magnitude of activity modulation during adaptation, with the number of increases approximately equaling the number of decreases. These data support the prediction that changes in task-related neuronal activity can be observed in M1 during motor adaptation, but fail to support the hypothesis that M1 and PMdc differ in this regard. When viewed in population averages, motor cortex continued to change its activity for at least dozens of trials after performance reached a plateau. This slow, apparently continuing change in the pattern and magnitude of task-related activity may reflect the initial phases of consolidating the motor memory for preparing and executing visuomotor skills.     

Visuomotor adaptation and intermanual transfer under different viewing conditions

Does the brain use a separate internal model for cursor mechanics during visuomotor adaptation? We compared the amount of adaptation and transfer to the opposite arm when subjects reached the targets under different viewing conditions of the arm during reaching. If the brain forms separate models, we predict a difference in the amount of adaptation and transfer for each viewing condition. If the brain forms one model, we predict equivalent amounts of adaptation and transfer between the two hands for each viewing condition. Separate groups of subjects performed a reaching task with either a rotated view of cursor motion representing their unseen hand or a rotated view of their actual hand. The two groups were further divided so that the magnitude of the rotation was either 45° or 75° counter-clockwise. After adapting to the rotation with one hand, subjects reached the same targets under the same viewing condition but with the opposite hand. Similar amounts of adaptation and intermanual transfer were found across the different magnitudes of rotation and across patterns of hand-order. Our results suggest that the brain may not be learning a distinct model for cursor mechanics, or if it is, it must be equivalent or overlapping with the arm model.     

Common processing constraints for visuomotor and visual mental rotations

Naive human subjects were tested in three different tasks: (1) a visuomotor mental rotation task, in which the subjects were instructed to move a cursor at a given angle from a stimulus direction; (2) a visual mental rotation task, in which the subjects had to decide whether a displayed letter was normal or mirror image regardless of its orientation in the plane of presentation; and (3) a visuomotor memory scanning task, in which a list of two to five stimuli directions were presented sequentially and then one of the stimuli (test stimulus), except the last one, was presented again. Subjects were instructed to move a cursor in the direction of the stimulus that followed the test stimulus in the previous sequence. The processing rate of each subject in each task was estimated using the linear relation between the response time and the angle (mental rotation tasks) or the list length (memory scanning task). We found that the processing rates in the mental rotation tasks were significantly correlated but that neither correlated significantly with the processing rate in the memory scanning task. These results suggest that visuomotor and visual mental rotations share common processing constraints that cannot be ascribed to general mental processing performances. Correspondence to: A.P. Georgopoulos, Brain Sciences Center     

Effects of Parkinson's disease on visuomotor adaptation

Visuomotor adaptation to a kinematic distortion was investigated in Parkinson's disease (PD) patients and age-matched controls. Participants performed pointing movements in which the visual feedback of hand movement, displayed as a screen cursor, was normal (pre-exposure condition) or rotated by 90° counterclockwise (exposure condition). Aftereffects were assessed in a post-exposure condition in which the visual feedback of hand movement was set back to normal. In pre- and early-exposure trials, both groups showed similar initial directional error (IDE) and movement straightness (RMSE, root mean square error), but the PD group showed reduced movement smoothness (normalized jerk, NJ) and primary submovement to total movement distance ratios (PTR). During late-exposure the PD subjects, compared with controls, showed larger IDE, RMSE, NJ, and smaller PTR scores. Moreover, PD patients showed smaller aftereffects than the controls during the post-exposure condition. Overall, the PD group showed both slower and reduced adaptation compared with the control group. These results are discussed in terms of reduced signal-to-noise ratio in feedback signals related to increased movement variability and/or disordered kinesthesia, deficits in movement initiation, impaired selection of initial movement direction, and deficits in internal model formation in PD patients. We conclude that Parkinson's disease impairs visuomotor adaptation. Electronic Publication     

Visual gravity influences arm movement planning

When submitted to a visuomotor rotation, subjects show rapid adaptation of visually guided arm reaching movements, indicated by a progressive reduction in reaching errors. In this study, we wanted to make a step forward by investigating to what extent this adaptation also implies changes into the motor plan. Up to now, classical visuomotor rotation paradigms have been performed on the horizontal plane, where the reaching motor plan in general requires the same kinematics (i.e., straight path and symmetric velocity profile). To overcome this limitation, we considered vertical and horizontal movement directions requiring specific velocity profiles. This way, a change in the motor plan due to the visuomotor conflict would be measurable in terms of a modification in the velocity profile of the reaching movement. Ten subjects performed horizontal and vertical reaching movements while observing a rotated visual feedback of their motion. We found that adaptation to a visuomotor rotation produces a significant change in the motor plan, i.e., changes to the symmetry of velocity profiles. This suggests that the central nervous system takes into account the visual information to plan a future motion, even if this causes the adoption of nonoptimal motor plans in terms of energy consumption. However, the influence of vision on arm movement planning is not fixed, but rather changes as a function of the visual orientation of the movement. Indeed, a clear influence on motion planning can be observed only when the movement is visually presented as oriented along the vertical direction. Thus vision contributes differently to the planning of arm pointing movements depending on motion orientation in space.     

Allocation of attention for dissociated visual and motor goals

In daily life, selecting an object visually is closely intertwined with processing that object as a potential goal for action. Since visual and motor goals are typically identical, it remains unknown whether attention is primarily allocated to a visual target, a motor goal, or both. Here, we dissociated visual and motor goals using a visuomotor adaptation paradigm, in which participants reached toward a visual target using a computer mouse or a stylus pen, while the direction of the cursor was rotated 45° counter-clockwise from the direction of the hand movement. Thus, as visuomotor adaptation was accomplished, the visual target was dissociated from the movement goal. Then, we measured the locus of attention using an attention-demanding rapid serial visual presentation (RSVP) task, in which participants detected a pre-defined visual stimulus among the successive visual stimuli presented on either the visual target, the motor goal, or a neutral control location. We demonstrated that before visuomotor adaptation, participants performed better when the RSVP stream was presented at the visual target than at other locations. However, once visual and motor goals were dissociated following visuomotor adaptation, performance at the visual and motor goals was equated and better than performance at the control location. Therefore, we concluded that attentional resources are allocated both to visual target and motor goals during goal-directed reaching movements.     

Distribution of responses to visual cues for movement in precentral cortex of awake primates

Unit recordings were made from areas 4 and 6 of monkeys after they were trained to align a cursor over a vertical target line on a video screen by control of a manipulandum with wrist flexion or extension movement. The appearance of the cursor and line on the screen was the visual cue for movement. Responses were observed 150 (±40) msec after cue presentation. The responses were found only in the forelimb area of precentral cortex, which was most immediately involved in the control of the task, and the majority of them were uncorrelated with either the specific details of the visual cue, or with the direction of the subsequent wrist movement.     

Online and post-trial feedback differentially affect implicit adaptation to a visuomotor rotation

Multiple motor learning processes can be discriminated in visuomotor rotation paradigms. At least four processes have been proposed: Implicit adaptation updates an internal model based on prediction errors. Model-free reinforcement reinforces actions that achieve task success. Use-dependent learning favors repetition of prior movements, and strategic learning uses explicit knowledge about the task. The current experiment tested whether the processes involved in motor learning differ when visual feedback is altered. Specifically, we hypothesized that online and post-trial feedback would cause different amounts of implicit adaptation. Twenty subjects performed drawing movements to targets under a 45° counterclockwise visuomotor rotation while aiming at a clockwise adjacent target. Subjects received visual feedback via a cursor on a screen. One group saw the cursor throughout the movement (online feedback), while the other only saw the final position after movement execution (post-trial feedback). Both groups initially hit the target by applying the strategy. After 80 trials, subjects with online feedback had drifted in clockwise direction [mean direction error: 15.1° (SD 11.2°)], thus overcompensating the rotation. Subjects with post-trial feedback remained accurate [mean: 0.7° (SD 2.0°), TIME × GROUP: F = 3.926, p = 0.003]. We interpret this overcompensation to reflect implicit adaptation isolated from other mechanisms, because it is driven by prediction error rather than task success (model-free reinforcement) or repetition (use-dependent learning). The current findings extend previous work (e.g., Mazzoni and Krakauer in J Neurosci 26:3642–3645, 2006; Hinder et al. in Exp Brain Res 201:191–207, 2010) and suggest that online feedback promotes more implicit adaptation than does post-trial feedback.     

Target-, limb-, and context-dependent neural activity in the cingulate and supplementary motor areas of the monkey

Very little is known about the role of the cingulate motor area (CMA) in visually guided reaching compared to other cortical motor areas. To investigate the hierarchical role of the caudal CMA (CMAc) during reaching we recorded the activity of neurons in CMAc in comparison to the supplementary motor area proper (SMA) while a monkey performed an instructed delay task that required it to position a cursor over visual targets on a computer screen using two-dimensional (2D) joystick movements. The direction of the monkeys arm movement was dissociated from the direction of the visual target by periodically reversing the relationship between the direction of movement of the joystick and that of the cursor. Neurons that responded maximally with a particular limb movement direction regardless of target location were classified as limb-dependent, whereas neurons that responded maximally to a particular target direction regardless of the direction of limb movement were classified as target-dependent. Neurons whose activity was directional in one of the two visuomotor mapping conditions and non-directional or inactive in the other were categorized as context-dependent. Limb-dependent activity was observed more frequently than target-dependent activity in both CMAc and SMA proper during both the delay period (preparatory activity; CMAc, 17%; SMA, 31%) and during movement execution (CMAc, 49%, SMA, 48%). A modest percentage of neurons with preparatory activity were target-dependent in both CMAc (11%) and SMA proper (8%) and a similar percentage of neurons in both areas demonstrated target-dependent, movement activity (CMAc, 8%; SMA, 10%). The surprising finding was that a very large percentage of neurons in both areas displayed context-dependent activity either during the preparatory (CMAc, 72%; SMA, 61%) or movement (CMAc, 43%, SMA 42%) epochs of the task. These results show that neural activity in both CMAc and SMA can directly represent movement direction in either limb-centered or target-centered coordinates. The presence of target-dependent activity in CMAc, as well as SMA, suggests that both are involved in the transformation of visual target information into appropriate motor commands. Target-dependent activity has been found in the putamen, SMA, CMAc, dorsal and ventral premotor cortex, as well as primary motor cortex. This indicates that the visuomotor transformations required for visually guided reaching are carried out by a distributed network of interconnected motor areas. The large proportion of neurons with context-dependent activity suggests, however, that while both CMAc and SMA may play a role in the visuomotor transformation of target information into movement parameters, their activity is not solely coding parameters of movement, since their involvement in this process is highly condition-dependent.     

Separate adaptive mechanisms for controlling trajectory and final position in reaching

We examined control of the hand's trajectory (direction and shape) and final equilibrium position in horizontal planar arm movements by quantifying transfer of learned visuomotor rotations between two tasks that required aiming the hand to the same spatial targets. In a trajectory-reversal task ("slicing"), the hand reversed direction within the target and returned to the origin. In a positioning task ("reaching"), subjects moved the hand to the target and held it there; cursor feedback was provided only after movement ended to isolate learning of final position from trajectory direction. We asked whether learning acquired in one task would transfer to the other. Transfer would suggest that the hand's entire trajectory, including its endpoint, was controlled using a common spatial plan. Instead we found minimal transfer, suggesting that the brain used different representations of target position to specify the hand's initial trajectory and its final stabilized position. We also observed asymmetrical practice effects on hand trajectory, including systematic curvature of reaches made after rotation training and hypermetria of untrained slice reversals after reach training. These are difficult to explain with a unified control model, but were replicated in computer simulations that specified the hand's initial trajectory and its final equilibrium position. Our results suggest that the brain uses different mechanisms to plan the hand's initial trajectory and final position in point-to-point movements, that it implements these control actions sequentially, and that trajectory planning does not account for specific impedance values to be implemented about the final stabilized posture.     

Contributions of online visual feedback to the learning and generalization of novel finger coordination patterns

We explored how people learn new ways to move objects through space using neuromuscular control signals having more degrees of freedom than needed to unambiguously specify object location. Subjects wore an instrumented glove that recorded finger motions. A linear transformation matrix projected joint angle signals (a high-dimensional control vector) onto a two-dimensional cursor position on a video monitor. We assessed how visual information influences learning and generalization of novel finger coordination patterns as subjects practiced using hand gestures to manipulate cursor location. Three groups of test subjects practiced moving a visible cursor between different sets of screen targets. The hand-to-screen transformation was designed such that the different sets of targets (which we called implicit spatial cues) varied in how informative they were about the gestures to be learned. A separate control group practiced gesturing with explicit cues (pictures of desired gestures) without ongoing cursor feedback. Another control group received implicit spatial cueing and feedback only of final cursor position. We found that test subjects and subjects provided with explicit cues could learn to produce desired gestures, although training efficacy decreased as the amount of task-relevant feedback decreased. Although both control groups learned to associate screen targets with specific gestures, only subjects provided with online feedback of cursor motion learned to generalize in a manner consistent with the internal representation of an inverse hand-to-screen mapping. These findings suggest that spatial learning and generalization require dynamic feedback of object motion in response to control signal changes; static information regarding geometric relationships between controller and endpoint configurations does not suffice.     

Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements

People can learn to control the 8-12 Hz mu rhythm and/or the 18-25 Hz beta rhythm in the EEG recorded over sensorimotor cortex and use it to control a cursor on a video screen. Subjects often report using motor imagery to control cursor movement, particularly early in training. We compared in untrained subjects the EEG topographies associated with actual hand movement to those associated with imagined hand movement. Sixty-four EEG channels were recorded while each of 33 adults moved left- or right-hand or imagined doing so. Frequency-specific differences between movement or imagery and rest, and between right- and left-hand movement or imagery, were evaluated by scalp topographies of voltage and r spectra, and principal component analysis. Both movement and imagery were associated with mu and beta rhythm desynchronization. The mu topographies showed bilateral foci of desynchronization over sensorimotor cortices, while the beta topographies showed peak desynchronization over the vertex. Both mu and beta rhythm left/right differences showed bilateral central foci that were stronger on the right side. The independence of mu and beta rhythms was demonstrated by differences for movement and imagery for the subjects as a group and by principal components analysis. The results indicated that the effects of imagery were not simply an attenuated version of the effects of movement. They supply evidence that motor imagery could play an important role in EEG-based communication, and suggest that mu and beta rhythms might provide independent control signals.     

An SSVEP-Actuated Brain Computer Interface Using Phase-Tagged Flickering Sequences: A Cursor System

This study presents a new steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI). SSVEPs, induced by phase-tagged flashes in eight light emitting diodes (LEDs), were used to control four cursor movements (up, right, down, and left) and four button functions (on, off, right-, and left-clicks) on a screen menu. EEG signals were measured by one EEG electrode placed at Oz position, referring to the international EEG 10-20 system. Since SSVEPs are time-locked and phase-locked to the onsets of SSVEP flashes, EEG signals were bandpass-filtered and segmented into epochs, and then averaged across a number of epochs to sharpen the recorded SSVEPs. Phase lags between the measured SSVEPs and a reference SSVEP were measured, and targets were recognized based on these phase lags. The current design used eight LEDs to flicker at 31.25 Hz with 45° phase margin between any two adjacent SSVEP flickers. The SSVEP responses were filtered within 29.25–33.25 Hz and then averaged over 60 epochs. Owing to the utilization of high-frequency flickers, the induced SSVEPs were away from low-frequency noises, 60 Hz electricity noise, and eye movement artifacts. As a consequence, we achieved a simple architecture that did not require eye movement monitoring or other artifact detection and removal. The high-frequency design also achieved a flicker fusion effect for better visualization. Seven subjects were recruited in this study to sequentially input a command sequence, consisting of a sequence of eight cursor functions, repeated three times. The accuracy and information transfer rate (mean ± SD) over the seven subjects were 93.14 ± 5.73% and 28.29 ± 12.19 bits/min, respectively. The proposed system can provide a reliable channel for severely disabled patients to communicate with external environments.     

An event-related potential evoked by movement planning is modulated by performance and learning in visuomotor control

Based on a previous exploratory study, the functionality of event-related potentials related to visuomotor processing and learning was investigated. Three pursuit tracking tasks (cursor control either mouse, joystick, or bimanually) revealed the greatest tracking error and greatest learning effect in the bimanual task. The smallest error without learning was found in the mouse task. Error reduction reflected visuomotor learning. In detail, target–cursor distance was reduced continuously, indicating a better fit to a changed direction, whereas response time remained at 300 ms. A central positive ERP component with an activity onset 100 ms after a directional change of the target and most likely generated in premotor areas could be assigned to response planning and execution. The magnitude of this component was modulated by within-and-between-task difficulty and size of the tracking error. Most importantly, the size of this component was sensitive to between-subject performance and increased with visuomotor learning. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Visuomotor adaptation and generalization with repeated and varied training

Many studies have shown that reaching movements to visual targets can rapidly adapt to altered visual feedback of hand motion (i.e., visuomotor rotation) and generalize to new target directions. This generalization is thought to reflect the acquisition of a neural representation of the novel visuomotor environment that is localized to the particular trained direction. In these studies, participants perform movements to a small number of target locations repeatedly. However, it is unclear whether adaptation and generalization are comparable when target locations are constantly varied and participants reach to visual targets one time only. Here, we compared performance for reaches to a 30° counter-clockwise visuomotor rotation to four targets, spaced 90° apart across four areas of workspace 18 times each (repeated practice (RP)) with one time only reaching movements to 72 targets, spaced 5° apart (varied practice (VP)). For both training groups, participants performed 18 reaches to radial targets (either at the repeated or varied location) in a specific area of the workspace (i.e., one of four quadrants) before reaching in the adjacent workspace. We found that the RP group adapted more completely compared to the VP group. Conversely, the VP group generalized to new target directions more completely when reaching without cursor feedback compared to the RP group. This suggests that RP and VP follow a mainly common pattern of adaptation and generalization represented in the brain, with benefits of faster adaptation with RP and more complete generalization with VP.     

Reorganization of finger coordination patterns during adaptation to rotation and scaling of a newly learned sensorimotor transformation

We examined how people organize redundant kinematic control variables (finger joint configurations) while learning to make goal-directed movements of a virtual object (a cursor) within a low-dimensional task space (a computer screen). Subjects participated in three experiments performed on separate days. Learning progressed rapidly on day 1, resulting in reduced target capture error and increased cursor trajectory linearity. On days 2 and 3, one group of subjects adapted to a rotation of the nominal map, imposed either stepwise or randomly over trials. Another group experienced a scaling distortion. We report two findings. First, adaptation rates and memory-dependent motor command updating depended on distortion type. Stepwise application and removal of the rotation induced a marked increase in finger motion variability but scaling did not, suggesting that the rotation initiated a more exhaustive search through the space of viable finger motions to resolve the target capture task than did scaling. Indeed, subjects formed new coordination patterns in compensating the rotation but relied on patterns established during baseline practice to compensate the scaling. These findings support the idea that the brain compensates direction and extent errors separately and in computationally distinct ways, but are inconsistent with the idea that once a task is learned, command updating is limited to those degrees of freedom contributing to performance (thereby minimizing energetic or similar costs of control). Second, we report that subjects who learned a scaling while moving to just one target generalized more narrowly across directions than those who learned a rotation. This contrasts with results from whole-arm reaching studies, where a learned scaling generalizes more broadly across direction than rotation. Based on inverse- and forward-dynamics analyses of reaching with the arm, we propose the difference in results derives from extensive exposure in reaching with familiar arm dynamics versus the novelty of the manual task.     

Abnormal Purkinje cell activity in vivo in experimental allergic encephalomyelitis

It has been shown that learning visuomotor rotations with multiple target directions, compared with a single target direction, leads to greater generalization to untrained targets within the same limb. This implies that multiple direction learning results in a more complete internal model of the visuomotor transform. It has also been documented that the extent of transfer of movement information regarding visuomotor adaptations between the limbs is limited, relative to that between different configurations of the same limb. The present study thus investigated the origin of this restriction in interlimb transfer, by comparing the effects of eight-direction and one-direction training conditions with one arm on the subsequent performance with the other arm. It was hypothesized that if multiple direction learning leads to a more complete model of the novel visuomotor transform, interlimb transfer should be enhanced relative to that following single direction training. However, if no differences are observed between single and multiple direction training conditions, this would suggest that such learning is effector dependent. We also tested the hypothesis that interlimb transfer of visuomotor adaptation is not obligatory, by examining the effects of visual rotation direction (same or oppositely directed visuomotor rotations for the two arms). All subjects first adapted to a 30° rotation, either clockwise or counterclockwise, in the visual display during reaching movements. Following this, they adapted to a 30° rotation in either the same or opposing direction with the other arm. Results showed that initial training with the non-dominant arm facilitated subsequent performance with the dominant arm in terms of initial direction control, but only under the same rotation condition. Both single and eight direction training conditions led to substantial transfer in subsequent performance with the other arm, but multiple direction training was no more beneficial than single direction training. This finding suggests that the previously reported intralimb advantages of multiple direction learning are effector specific. Our findings are discussed in the context of hierarchical models of motor control to explain the intralimb advantages of multiple direction training.     

Effects of altering initial position on movement direction and extent

The purpose of this study was to examine the relative influence of initial hand location on the direction and extent of planar reaching movements. Subjects performed a horizontal-plane reaching task with the dominant arm supported above a table top by a frictionless air-jet system. A start circle and a target were reflected from a horizontal projection screen onto a horizontally positioned mirror, which blocked the subject's view of the arm. A cursor, representing either actual or virtual finger location, was only displayed between each trial to allow subjects to position the cursor in the start circle. Prior to occasional "probe trials," we changed the start location of the finger relative to the cursor. Subjects reported being unaware of the discrepancy between cursor and finger. Our results indicate that regardless of initial hand location, subjects did not alter the direction of movement. However, movement distance was systematically adjusted in accord with the baseline target position. Thus when the hand start position was perpendicularly displaced relative to the target direction, neither the direction nor the extent of movement varied relative to that of baseline. However, when the hand was displaced along the target direction, either anterior or posterior, movements were made in the same direction as baseline trials but were shortened or lengthened, respectively. This effect was asymmetrical such that movements from anterior displaced positions showed greater distance adjustment than those from posterior displaced positions. Inverse dynamic analysis revealed substantial changes in elbow and shoulder muscle torque strategies for both right/left and anterior/posterior pairs of displacements. In the case of right/left displacements, such changes in muscle torque compensated changes in limb configuration such that movements were made in the same direction and to the same extent as baseline trials. Our results support the hypothesis that movement direction is specified relative to an origin at the current location of the hand. Movement extent, on the other hand, appears to be affected by the workspace learned during baseline movement experience.