Common to both of Goodale et al.'s (1994b) pantomime tasks is an obvious requirement to pretend to pick up either the remembered or imagined target as if it were actually there and an absence of haptics-based object information. As we have already pointed out, the availability of real-time visual input following the cue to perform the grasp differed between the two tasks. Thus, this factor alone can reasonably account for any differences in DF's performance across the two pantomime grasping task variants. As such, DF's poor performance on the DPG task serves as a striking example of the dependence of some visuomotor tasks (pantomime grasps) on ventral stream processing, not only in DF but, presumably, in neurologically-intact individuals as well. One perhaps less obvious requirement of Goodale et al.'s tasks is the fact that the dimensions of the Efron blocks (only 1 cm in height) that were used in these experiments allowed the participants to receive tactile feedback from the surface of the table at the end of their reach. This was because the participants could not reasonably be expected to refrain from touching the surface of the table with their fingertips when simulating reaching out to pick up short rectangular blocks. Thus, the table may well act as a proxy when the grasps are directed next to the visible object. Importantly, haptics-based object information need not correlate with the visual size of targets for DF's grip scaling to be normal. Indeed, when the grasped object remains an intermediate size despite changes in the visual size from trial to trial, DF's grip aperture scales to the visual size (Whitwell et al., 2014a, in press). According to this view (see also Milner et al., 2012), both terminal tactile feedback and real-time visual input are critical for normal, dorsally-mediated prehension. Unfortunately, Goodale et al., did not compare DF's performance in the RPG task directly against the performance of the controls, presumably because there were differences between DF and the controls in terms of the stimulus set (six Efron blocks vs. three) and the presentation protocol (one target position vs. three). Determining whether DF's grip scaling in this task is in fact normal or abnormal would help rule out (or rule in) the importance of terminal tactile feedback for normal, real-time prehension. Therefore, in the first of our two experiments, we aimed to fill in this gap by revisiting DF's grip scaling in Goodale et al.'s RPG task. We tested a new group of control participants using the same stimulus set and protocol that was used by Goodale et al. to determine whether DF's real time-pantomime grasps were indeed as good as the controls and, more importantly, whether or not her grip scaling in this task would actually dissociate from that of her ''natural'' grasps as is commonly assumed.

# Experiment 1

# Methods Participants

Eight self-reported right-hand dominant age-appropriate and gender-matched control participants ranging from 31 to 46 years of age (M = 39.1, SD = 5.7), volunteered to take part in the experiment to compare DF's grip scaling in the natural grasping and RPG tasks. The controls provided written informed consent and were compensated \$20 for their time. All experiments were approved by the local ethics committee and were in accordance with the Declaration of Helsinki.

# Apparatus and Stimuli

Details of the apparatus and stimuli used to test the controls for patient DF's data set can be found in Goodale et al. (1994b). Briefly, the stimuli consisted of a set of Efron blocks that were 1 cm in height but varied in their lengths and widths as follows: l × w (in cm), 10 × 2.5, 8.3 × 3, 7.1 × 3.5, 6.3 × 4, 5.6 × 4.5, 5 × 5. The kinematic data was collected at 200 Hz using an optoelectronic recording system (OPTOTRAK 3020, Northern Digital, Waterloo, ON, Canada) that recorded the 3D spatial locations of three infrared emitting diodes (IREDs). The IREDs were attached with adhesive tape at three positions on the right (grasping) hand: the distal left corner of the nail of the index-finger, the distal right corner of the nail on the thumb, and the skin blanketing the metacarpophalangeal joint (MCP) of the index-finger. The experimenter ensured that the pads of skin on the tip of the thumb and index-finger were uncovered to ensure normal tactile feedback from the goal objects when grasped. The leads from the IREDs were taped to the right forearm to ensure complete freedom of movement. There was only one target position, 30 cm along a sagittal plane from the start position. The start position was a raised button located 5 cm from the edge of the table facing the participant (see **Figure 1**). Before the experiment began, the experimenter ensured that all of the participants were seated comfortably and positioned close enough to the table so that they could grasp the objects at the farthest position comfortably and without leaning forward.

# Procedure and Design

Details of the procedure and design used to test the controls can be found in Goodale et al. (1994b). Briefly, before each trial was initiated, the participants closed their eyes and held the tips of their right index-finger and thumb together while depressing the start button. The experimenter then gave a verbal prompt to the participant to open her eyes. The experimenter then waited approximately 2 s before giving a ''go'' signal for the participant to execute their response. For the natural grasping task, the participants were instructed to reach out, grasp across the width (near-far axis) of the Efron block, lift up, and put back down the Efron block using a precision grip (index-finger and thumb) as soon as they heard the go signal. At the beginning of the experiment, participants were asked to grasp the objects naturally: neither labored nor speeded. For the RPG task, the participants were instructed to imagine that the visible target to their left was positioned at the same distance along their midline (see **Figure 1**). They were further instructed to reach out to pick up the imaginary target as if it were physically there. The experimenter explained the procedure for the upcoming task before each block of trials. The experiment was comprised of 2 blocks of 36 trials each for a total of 72 trials. Each block

of trials was dedicated to a different task. The block of natural grasps were performed before the block of RPGs. As Goodale et al. (1994b) cautioned, this order was chosen to give DF the maximum likelihood of being able to use the experience of actually grasping the objects when performing the RPGs. The order of the blocks were the same across all of the participants, including DF. For each block of trials (i.e., for each task), each one of the six Efron blocks was presented 6 times in a pseudorandom order.

# Data Processing and Statistical Analysis

of the table at the end of the reach.

The data from the control participants were processed offline with custom software written in Matlab (Mathworks Inc., Natick, MA, USA). The positional information from the IREDs was lowpass filtered at 20 Hz using a 2nd order Butterworth digital filter. Grip aperture was computed as the Euclidean distance between the IRED placed on the thumb and the IRED placed on the index-finger, and the instantaneous velocities were computed for each of the three IREDs and for grip aperture. We analyzed three principal measures: peak grip aperture (PGA), the slope relating PGA to the target size, and the peak hand velocity (PHV). The PGA was defined as the largest grip aperture within a search window that was designed to capture the forward-reach component of the movement. The beginning of this window (the ''movement onset'') was operationally defined as the first of 30 consecutive sample frames (150 ms) in which the velocity of the MCP IRED exceeded a threshold of 50 mm/s. Normally, one could use the movement onset as a measure of reaction time. In this case, however, because the timing between the initiation of the data collection and the subsequent experimenter's verbal ''go'' command was free to vary (as was the case in Goodale et al., 1994b), reaction time (RT) could not be referenced to a fixed point in time. Thus, RT could not be computed reliably. Nevertheless, the end of the search window was defined as the first sample frame in which the velocity of the IRED fell below 75 mm/s. Linear regression of PGA on the widths of the Efron blocks was performed separately for each task and the resultant regression coefficient (i.e., the slope, b) relating the average increase in PGA (in mm) per incremental increase in Efron width (also in mm) was computed for DF and for the controls. The PHV was defined as the peak speed at which the MCP IRED travelled towards the target within the search window outlined above.

Notably, only DF's PGA was available from the data set reported by Goodale et al. (1994b). Thus, only PGA and the slopes relating PGA to target size could be compared against the control data set. The PHV of the control participants was analyzed to test Goodale et al.'s finding that the RPGs of the controls are executed more slowly than natural ones in this slightly modified version of that task (one target position and six target sizes). The comparisons of interest in the control data were the differences in the PHV, overall PGA, and grip scaling slopes between the natural grasps and the RPGs. The comparisons of interest that involved DF included those measures that were common to both DF and the controls: the difference in overall PGA between the natural grasps and RPGs and the grip scaling slopes. A comparison of the PGAs between DF and the controls for each of the natural grasping and RPG tasks was not carried out given that inter-individual differences in IRED positioning and hand anatomy could have yielded spurious results. Comparisons of intra-individual differences involving PGA should be far less susceptible to this influence (if at all). Accordingly, we used independent-samples t-tests to assess the normality/abnormality of (1) DF's slope on each of the two grasping tasks and (2) DF's difference scores for both the slope and the PGA between the two grasping tasks. Together, these contrasts constitute tests for ''strong/differential'' or ''classical'' dissociations (Crawford et al., 2003; Crawford and Garthwaite, 2005). For all statistical tests, the alpha criterion for statistical significance was set to 0.05.

# Results

# Peak Grip Aperture (PGA), Slopes, and the Peak Hand Velocity (PHV)

The controls' overall PGA was significantly larger when they performed natural grasps than when they performed RPGs, t(7) = 8.23, p < 8 × 10−5 (see **Figure 2A**). A comparison of the difference in the overall PGA across the two tasks yielded no significant difference, t(7) = −0.02, p = 0.98. In other words, the switch from natural grasps to RPGs affected DF's overall PGA no differently than it did the controls' overall PGA.

The controls' slopes relating PGA to target size did not depend on whether they executed natural grasps or RPGs, t(7) = 1.29,

p = 0.24 (see **Figure 2B**). DF's PGA was positively related to the size of the target in the natural grasping task (t(28) = 6.01, p < 2 × 10−6 ) and in the RPG task, t(28) = 2.98, p < 6 × 10−3 . Importantly, DF's slopes did not differ significantly from those of the controls when she performed natural grasps (t(7) = −0.69, p = 0.61) or when she performed RPGs, t(7) = −1.53, p = 0.17. Moreover, the test for dissociation yielded a null result, t(7) = 0.5, p = 0.62 (see **Figure 2C**). In other words, DF's slopes fell within the normal range regardless of whether she performed natural grasps or RPGs. Notably, DF's slopes on the natural grasping task and the RPG task contrasts sharply with an absence of grip scaling on the DPG task in which her pantomimes were based on a memory of the previewed target (slope based on data reported in Goodale et al., 1994b) (p = 0.9; see **Figure 2B**).

The controls' PHV was significantly slower when performing the RPGs than it was when they performed natural grasps, t(7) = 2.79, p < 0.05.

Finally, the time-normalized grip aperture (**Figure 3A**) and velocity (**Figure 3B**) profiles for the controls reveals a noticeable distinction between the natural and displaced-pantomime grasps that converges with the findings of Goodale et al. (1994b).

# Discussion

In this experiment we re-examined DF's natural grasps and RPGs from an earlier study by Goodale et al. (1994b) by contrasting her performance on these two tasks with the performance of a new sample of normally-sighted control participants. When compared to natural grasps, the controls' RPGs yielded smaller overall PGAs and slower PHVs. Thus, we replicated Goodale et al.'s findings but in a version of the task that the authors had modified before testing DF by reducing the number of possible target positions from three to one and increasing the number of targets from three to six. Although we were unable to examine DF's PHV, we

FIGURE 3 | Normalized grand mean grip aperture and velocity profiles for the natural grasps (black) and real-time pantomime grasps. (A) Grip aperture normalized to 100 time bins for qualitative comparisons. Note that, overall, the real-time pantomime grasps lack a distinct peak and achieve lower grip aperture values than the natural grasps. The error bars reflect average within-participant standard deviations (B) Velocity of the wrist normalized to 100 time bins for qualitative comparisons. Note that, overall, the displaced-pantomime grasps appear to be executed more slowly than the natural grasps. The error bars reflect the between-participant standard deviations.

found that the RPG task reduced DF's overall PGA just as much as it did for the controls. We also examined DF's grip scaling in terms of the slope relating PGA to target size and for the controls. Somewhat surprisingly, we found that DF's slopes fell within the control range in both tasks. Her intact performance on this task contrasts sharply with her performance on the DPG task in which (quite unlike controls) she shows no evidence of grip scaling at all. As we pointed out in the Introduction, one evident difference between the two tasks is the availability of real-time visual input about the target in the RPG task. In other words, in the RPG task, information about the target can be used in real time to program the movement parameters, including grip aperture. This is obviously not the case in the DPG task. Indeed, because the movement is being programmed in real time in the RPG task, the relatively intact visuomotor networks in DF's dorsal stream could presumably mediate this programming. Although this line of argument is appealing, recent experiments suggest that real-time visual input is not sufficient for ''normal'' prehension (e.g., Bingham et al., 2007; Schenk, 2012a; Whitwell et al., 2014a,b).

Several years after Goodale et al.'s (1994b) investigation, Bingham et al. (2007) introduced a novel variant of a grasping task which was later adapted by Schenk (2012a) to re-test DF's grasps. Noting that movements that lack feedback are often more variable, Bingham et al. (2007) hypothesized that goal-directed movements, such as grasping, are precise because they can make use of haptic feedback (what we are referring to here as haptics-based object information) for calibrating each movement. Thus, Bingham et al. reasoned, the slower pantomime grasping movements that Goodale et al. (1994b) observed could be due to a decrease in precision and the lack of haptics-based object information in the DPG and RPG tasks. Bingham et al. set out to test how the provision of periodic haptic feedback about the target object would affect the grasps of normally-sighted individuals. To do this, Bingham et al. used an ingenious mirror apparatus that allowed the participants to view a virtual target in the mirror. This way, the participants could be instructed to reach out behind the mirror towards the apparent position of the virtual target to grasp it. An identical copy of the virtual target could be positioned behind the mirror such that the virtual and hidden targets were spatially coincident. Critically, the arrangement allowed the experimenter the choice to deny the participants an opportunity to grasp a real cylinder by refraining from positioning one behind the mirror. In short, this setup allowed Bingham et al. to preserve both the real-time visual information about the targets and the target-directedness of natural grasps in these new grasping task variants. Similar to Goodale et al.'s findings, Bingham et al. found that when participants were consistently denied an object to grasp, they showed slower hand velocities, longer movement times, and lower overall PGA.

Several years following Bingham et al.'s (2007) study, Schenk (2012a) used a similar mirror-apparatus to re-examine patient DF's grasps. He was motivated by the observation that the dissociation in grip scaling between DF's grasping and her explicit perceptual estimates of target size might be due to the difference in the availability of haptic feedback about the target between grasping and perceptual estimation tasks. As Bingham et al. has suggested, haptic feedback might normally be used to calibrate actions. Perhaps DF has developed some abnormal reliance on this source of information that allows her to calibrate the programming of her grasps (see also Schenk, 2012b). Rather than providing DF haptic feedback for her perceptual estimations of target size, however, Schenk opted to divorce it from the grasping task as Bingham et al. (2007) had done. Critically, he found that DF's grip scaling was abolished when haptic feedback was consistently denied and concluded that haptic feedback was required to calibrate DF's grasping movements. Curiously, however, he did not appeal to the same pantomime-based explanation as Bingham et al. and Goodale et al. (1994b) had done in the past. Instead, he argued that DF uses haptic feedback to ''compensate'' for her visual perceptual deficit when reaching out to grasp objects (Schenk, 2012a). According to this line of reasoning, no distinction between visual processes for perception and those for skilled goal-directed action is required, because DF's vision is merely degraded—haptics can help bootstrap her performance. As things turn out, this interpretation is incorrect, because DF's inaccurate perceptual estimates of Efron width show no improvement when haptic feedback is available to putatively calibrate her estimates: she was permitted to reach out to pick up the Efron blocks immediately after each of her explicit estimates (Whitwell et al., 2014a, in press). Thus, DF's dissociated performance on perceptual estimation and grasping tasks continues to support a fundamental distinction between dorsal and ventral stream object processing.

Nevertheless, one important factor was overlooked in both Schenk's and Bingham et al.'s experiments: the participants in the ''no haptic feedback'' tasks of both studies were unlikely to have encountered anything other than ''thin air'' at the end of their reaching movements. For example, in Schenk's study, the visual targets were vertically-standing cylinders 7 cm tall, requiring a horizontal grasping motion across the diameter of the visible target. In Bingham et al.'s investigation, the objects were shorter (though >3 cm in height), but the participants were explicitly instructed not to touch the surface of the table at the end of their reach and encouraged to adopt a particular approach that would minimize this possibility. At any rate, denying participants objects to grasp not only removed haptics-based object information in these studies but also any terminal tactile feedback about the end of the movement (Milner et al., 2012). This was not the case in Goodale et al.'s (1994b) study (and therefore in Experiment 1 of the present study) in which the participants, including DF, clearly made contact with the surface of the table next to the visible target. In fact, given DF's normal grip scaling, the results from Experiment 1 support an important distinction between hapticbased object information and the information derived from terminal tactile input. Adapting the term as it was used by Bingham et al. and Schenk, we hereafter use ''haptic feedback'' in an overarching sense to refer to the denial of an object or even a proxy at the end of the movement (i.e., terminal tactile/haptic feedback).

Notably, a critical role for terminal tactile feedback in maintaining DF's grip scaling is supported by the fact that DF scales her grip aperture to target size when she reaches out to ''grasp'' 2-D images of Efron blocks presented on a table top (Westwood et al., 2002). Furthermore, DF's normal grip scaling in this 2D-grasping task cannot reasonably be attributed to the availability of online visual feedback to update her movements as they unfold or to update the programming of subsequent movements or even some sort of ''visuo-manual matching'' strategy, because she continues to show grip scaling to Efron width in the absence of any online visual feedback whatsoever (Whitwell et al., in press). Additional support for the role of terminal tactile feedback in maintaining DF's grasps comes from the fact that her grip scaling is normal when she reaches out to grasp objects that vary in their visible (virtual) size but are always a constant, intermediate haptic size (Whitwell et al., 2014a, in press). In other words, hapticsbased object information need not provide veridical information about the target width or edges of the visible goal object to maintain normal dorsal-stream mediated grasping. Indeed, the results of Experiment 1 indicate that DF shows normal grip scaling when terminal tactile feedback from the table surface is available to her, even when she performs RPGs. Interestingly, the results of Experiment 1 promote the real-time nature of a natural grasping task over the target-directedness of it per se. Thus, the two critical factors underlying DF's grip scaling slope appear to be terminal tactile feedback and real-time visual input.

In the second experiment reported here, we addressed whether or not the task requiring DF and the control participants to reach out to a visible target that is not physically present results in grip scaling that resembles that of a more explicit pantomimed grasping task as Milner et al. (2012) suggest. After all, a desirable and novel feature of the grasping task used by Bingham et al. (2007) and Schenk (2012a) is that the resultant movements are programmed and executed in real-time and directed at the target—conditions under which the dorsal visuomotor system typically operates. Despite these similarities, there is some indication that the neurologically intact controls in Schenk's (2012a) experiment showed an increase in grip-scaling and inter-subject variability (Whitwell and Buckingham, 2013). DF's grip scaling to object size, as we pointed out earlier, was abolished in this task. Thus, the removal of haptic feedback appears to have changed DF's grip scaling and that of the controls, but in different ways. Unfortunately, however, the controls' grip-scaling with and without haptic feedback was never formally compared in that study. Thus, one aim of the second experiment reported here was to directly test whether removing haptic feedback from a target-directed grasping task influences grip scaling in neurologically-intact individuals. An additional aim (related to the first) was to directly contrast grasping in the target-directed task in which haptic feedback is removed against a variant of the RPG task in which the participants must imagine the visible target at a different location. This way, the responses when haptic feedback is denied in a target-directed grasping task could be compared to the responses in a task that quite obviously requires a pantomimed grasp. In order to implement these tasks, we adopted a mirror apparatus not unlike the one discussed above.

Finally, we took the opportunity that the mirror setup presented us to explore more systematically how the mirror itself might influence natural grasps. Although the mirror apparatus allows for the haptic and visual information about the target to differ, it has at least three possible drawbacks. (1) the mirror apparatus does not allow the participants to view their hand and limb throughout their grasping movement. The unavailability of any visual input about the hand and limb throughout the movement is of course quite different from what occurs with natural grasps. After all, normally when we reach out to pick things up, the hand and limb do not suddenly disappear from sight. A number of studies have shown that when vision is suppressed during the execution of a grasping movement in neurologically-intact individuals, grip aperture increases and, in many cases, the grip scaling slopes decrease (Fukui and Inui, 2006; Fukui et al., 2006; Whitwell et al., 2008, in press; Hesse and Franz, 2009, 2010; Whitwell and Goodale, 2009; Tang et al., 2014). In fact, DF shows similar changes in her grip aperture and grip scaling when vision is suppressed during the movement (Whitwell et al., in press). Presumably, these effects reflect an effort to ensure a sufficient margin of error in the absence of visual information that is normally used for online control. (2) When the participants make contact with the hidden object and pick it up, the virtual object remains stationary in the mirror. In short, there is a clear disconnect between what the participant sees in the mirror and what actually happens. (3) The mirror might be treated as an obstacle which has to be avoided. Any one or a combination of these three factors could have been responsible for reducing grip scaling in both normally-sighted individuals and in DF, because natural grasps that were directed at virtual targets in a mirror were contrasted against natural grasps that were directed at targets in plain view (Whitwell et al., 2014a). Thus, in an additional manipulation, we substituted a pane of glass in for the mirror to assess two effects of using a mirror: the removal of online visual input about the moving hand and limb, and the obvious disconnect between the behavior of viewed and hidden targets after contact. In total, therefore, we set out to test four tasks: grasping real targets (cylinders) viewed through a pane of glass (GG−H); grasping virtual targets viewed in a mirror with haptic feedback (GM−H); grasping virtual targets viewed in a mirror without any haptic feedback (i.e., no cylinder was present behind the mirror, GM−NH); and real-time pantomime grasps that were based on virtual targets viewed in a mirror but displaced to the side without any haptic feedback (RPGNH).

We grouped the task comparisons according to our apriori predictions: (1) that natural grasps directed at virtual targets (GM−H) would result in larger grip apertures than those directed at real targets viewed directly through glass (GG−H); and (2) in the absence of haptic feedback, target-directed grasping movements would resemble RPGNH grasps that are directed towards an an imagined copy of the virtual target.

# Experiment 2

# Methods

# Participants

Twenty-five self-reported right-hand dominant individuals (9 males) ranging from 17 to 33 years of age (M = 21.3, SD = 3.7), volunteered to take part in the second study. In a follow-up pair of control experiments that was prompted by some of our results, we tested an additional group of 18 self-reported righthand dominant individuals (6 males) ranging from 18 to 32 years of age (M = 21.4, SD = 3.5). The participants in both groups provided written informed consent and were compensated \$10 for their time. All experiments were approved by the local ethics committee and were in accordance with the Declaration of Helsinki.

# Apparatus and Stimuli

The apparatus and stimuli did not differ from that described in Experiment 1 except as noted below. The stimuli consisted of three pairs of black cylinders with diameters of 3.5 cm, 4.8 cm, and 6 cm and a height of 7 cm. Depending on the task, the workspace comprised either a mirror or a pane of glass positioned 45◦ from the edge of the table facing the participant. For all of the tasks that involved the mirror setup, the target cylinder was always positioned in front of the mirror. A vertically-standing occluding board was attached to the edge of the table that faced the participant. The occluding board was positioned to the left of the participants' midline so as to block them from viewing the target cylinder directly. This way, the participant could only see the reflection of the cylinder (i.e., its virtual image) placed in front of the mirror. The occluding board was left in place throughout the experiment. The cylinders could be placed at two different positions in front of and (at corresponding positions) behind the mirror. The ''near'' target position was located 14 cm away from the mirror along the participant's sagittal plane. The ''far'' position was located 10 cm farther away from the mirror along the same plane. The hand's resting start position was a small black button located 22 cm to the right and 7 cm in front of the nearest target position (see **Figure 4**). Before the experiment began, the experimenter ensured that all of the participants were positioned close enough to the table so that they could grasp the objects at the farthest distance comfortably and without leaning forward. The experimenter also ensured that the participants could see each of the target cylinders binocularly in the mirror.

# General Procedure and Design

Before each trial was initiated, the participants held the tips of their right index-finger and thumb together while depressing the start button. The participants were instructed to reach out, grasp, and lift up the cylinder using a precision grip (index-finger and

FIGURE 4 | A bird's eye view of the mirror setup used for Experiment 2. The cylinders (indicated by circles with a solid-line border) were placed in front of the mirror at one of two possible positions from trial to trial (the near position is indicated with a filled-in circle). The cylinder was hidden from direct view by an occluding board, and so the participants viewed a virtual cylinder in the mirror. An identical cylinder could be positioned behind the mirror (again indicated by circles with a solid-line border) such that it was spatially coincident with the apparent position of the virtual one. This way, haptic feedback about the object could be permitted (GM−H) or denied (GM−NH) by removing the cylinder from behind the mirror. In one of the tasks, the mirror was replaced with a pane of glass so that participants viewed the cylinder directly (GG−H). For the "real-time" pantomime grasping task (RPGNH), the participants imagined the virtual cylinder at the mirror-symmetrical position (dashed open circles) opposite a sagittal plane that was aligned with the start button. In a brief follow-up investigation, the RPGNH task was modified such that the target was imagined immediately next to the visible one (also indicated with dashed open circles).

thumb) as soon as the lenses of the goggles cleared. Participants were asked once they grasped and lifted the objects to simply move the objects to the center of the table. In all conditions, the lenses of the goggles remained transparent for 2.5 s following the participants' release of the start button before returning to their translucent state (i.e., visual closed-loop feedback). Participants were asked to grasp the objects naturally, neither labored nor speeded. The experimenter explained the procedure for the upcoming task before each block of trials. The experiment was comprised of 4 blocks of 24 trials each for a total of 96 trials. Each block was dedicated to a different task. For each block of trials (i.e., for each task), the six combinations of target-cylinder size and location were presented 4 times each. The block order (i.e., task order) was counterbalanced across participants.

# Grasping Real Targets Viewed Through a Pane of Glass

The participants viewed the cylinders through the pane of glass and were asked to reach out to pick them up as described in the previous section.

# Grasping Virtual Targets Viewed in a Mirror With Haptic Feedback

The participants viewed the cylinders in the mirror. The experimenter ensured that the cylinder behind the mirror matched the one that the participants viewed. The participants were asked to reach out behind the mirror to pick up the cylinder as described in the previous section. Note that the mirror blocked the participants' view of their hand during the movement. Thus, a comparison of this task with the one in which the participants grasped real targets viewed through a pane of glass tests the effect of online visual feedback of the hand and limb during the movement.

# Grasping Virtual Targets Viewed in a Mirror Without Haptic Feedback

This task was identical to the task described in the previous section in all respects, except that, after the matched cylinder was placed behind the mirror, it was immediately removed and the trial then initiated. Positioning a target behind the mirror was done simply to preserve the overall ''feel'' and timing of the events between trials. Neither haptics-based object information nor any terminal tactile feedback was available in this task. In accordance with the instruction to simulate a real grasp, the participants were asked to refrain from sending their fingers or hand through the imagined cylinder.

# Pantomime Grasping Visualized Copies of Virtual Targets Viewed in a Mirror

The participants viewed the cylinders in a mirror, but were asked to execute their grasps as if the cylinder was located to the right of where it appeared to be. This location was the right of the start button at a distance that equaled the distance from the visible cylinder to a sagittal plane aligned with the start button (see **Figure 4**). The experimenter explained this contingency to the participant and reinforced it by indicating the target locations for each of the two possible positions for the viewed cylinder. In accordance with the instruction to simulate a real grasp, the participants were asked to refrain from sending their fingers or hand through the imagined cylinder.

# Data Processing and Statistical Analysis

The data were processed offline with custom software written in Matlab (Mathworks Inc., Natick, MA, USA). The positional data from the IREDs was low-pass filtered at 20 Hz using a 2nd order Butterworth digital filter. Grip aperture was computed as the Euclidean distance between the IRED placed on the thumb and the IRED placed on the index-finger, and the instantaneous velocities were computed for each of the three IREDs and for grip aperture.

The PGA was defined as the largest grip aperture within a search window that was designed to capture the forwardreach component of the movement. The beginning of this window, the movement onset, was operationally defined as the first of 20 consecutive sample frames (100 ms) in which the velocity of the IRED attached to the knuckle of the indexfinger exceeded a threshold of 50 mm/s. The movement onset was also used to calculate the reaction time (RT). The end of the search window was defined as the first sample frame in which the velocity of the IRED fell below 150 mm/s. Linear regression of PGA on the widths of the cylinders was performed separately for each task and the resultant regression coefficient (slope, bPGA) relating the average increase in PGA (in mm) per incremental increase in cylinder width (also in mm) was computed. The PHV was defined as the peak velocity achieved by the knuckle IRED within the search window. One additional measure was operationally defined: the final grip aperture (FGA). The FGA was determined on the basis of grip stability (grip aperture velocity). Grip stability was used to identify the plateau phase of the grip aperture profile during which the participant holds the target (GG−H and GM−H tasks), pretends to hold a visible target (GM−H task), or pretends to hold an imagined copy of a visible target (in the case of the RPGNH). Linear regression of FGA on the widths of the cylinders was performed separately for each task and the resultant regression coefficient (slope, bFGA) relating the average increase in FGA (in mm) per incremental increase in cylinder width (also in mm) was computed. Note that the bFGA should be at or close to 1 for the natural grasps, and so the tests of this measure indicate how faithfully the participants reflected changes in target size from trial to trial in their FGA in the absence of haptic feedback.

To test for differences amongst the tasks, a one-way repeated measures Analysis of Variance (rmANOVA) was conducted separately for each of the dependent measures (RT, PHV, PGA, bPGA, FGA and bFGA) with Task as the main factor. The significant rmANOVAs were followed up with planned paired t-tests designed to test the specific effect of removing online visual feedback on the natural grasps and that of removing haptic feedback. The test of the former effect involved a comparison of the grasps directed at ''real'' cylinders viewed directly through a pane of glass (GG−H) and the grasps directed at ''virtual'' cylinders viewed in a mirror with haptic feedback (GM−H). The tests of the latter effect involved comparisons amongst the three tasks in which virtual cylinders were visible in the mirror: The GM−H task, the variant without haptic feedback (GM−NH), and the real-time pantomime grasps directed away from the virtual cylinders and towards imaged ones without haptic feedback (RPGM−NH). With respect to this set of contrasts, it should be noted that the RPGM−NH entailed online visual feedback. Therefore, we included a comparison of this task with the natural grasping task in which online visual feedback was available (i.e., RPGM−NH vs. GG−H). Greenhouse-Giesser epsilon multipliers were applied to the degrees of freedom to all ANOVAs to compensate for potential violations of sphericity of the variance-covariance matrices. The F-statistics which were adjusted in this way are reported in-text as Fadj. Violations of sphericity were assessed using Mauchley's test and assessed at a liberal alpha criterion of 0.15 as Kirk (1995) recommends for tests of underlying assumptions. For all other statistical tests, the alpha criterion for statistical significance was set to 0.05.

# Results

## Reaction Time

The rmANOVA of the reaction times (RTs) yielded a significant main effect of Task, F(3,72) = 26.7, p < 2 × 10−11 , η 2 p = 0.53 (see **Figure 5A**). There was no significant difference in the RTs between GG−H and GM−H (t(24) = 1.75, p = 0.09), indicating no effect of online visual feedback on the velocity of the reach.

The RTs were slower for GM−NH than the RTs for GM−H, t(24) = 2.81, p < 0.01. In turn, the RTs for RPGNH were significantly slower than those for GM−H, t(24) = 6.52, p < 1 × 10−6 . However, the RTs for RPGNH were significantly faster than the RTs for GM−NH, t(24) = 3.11, p < 5 × 10−3 . Thus, the removal of haptic feedback induced a partial shift in the RTs towards pantomimed grasps. In other words, removing haptic feedback slowed the RTs and displacing the grasps slowed the RTs further still. Finally, the RTs for GG−H were significantly faster than the RTs for RPGNH, t(24) = 8.57, p < 1 × 10−8 , suggesting that the slowing of RT that occurs when haptic feedback is denied occurs regardless of whether online visual feedback of the hand and limb is available or not.

#### Peak Hand Velocity (PHV)

The rmANOVA of PHV yielded a significant main effect of task, Fadj(2,43) = 21.2, p < 1 × 10−6 , η 2 p = 0.47 (see **Figure 5B**). The PHV was significantly slower for GM−H than for GG−H (t(24) = 5.34, p < 2 × 10−5 ), indicating a role for online visual feedback of the hand and limb in the velocity of the reach.

The PHV was significantly slower for GM−NH than the PHV for GM−H, t(24) = 5.87, p < 5 × 10−6 . Furthermore, the PHV was significantly slower for RPGNH than the PHV for GM−H, t(24) = 2.29, p < 0.04. Finally, the PHV did not differ significantly between GM−NH and RPGNH, t(24) = 1.75, p = 0.09. Thus, the removal of haptic feedback resulted in a complete shift in the PHV towards pantomime grasps. In other words, regardless of whether the grasps were target-directed or not, the velocity of the reach was slower when haptic feedback was denied.

FIGURE 5 | Reaction time (RT) and peak hand velocity (PHV) across the four variants of the grasping task arranged (within each panel) from left to right as follows: grasps directed at real (i.e., viewed through a pane of glass) targets (GG−H) with haptic feedback, grasps directed at virtual targets (i.e., viewed in a mirror) with haptic feedback (GM−H), grasps directed at virtual targets with no haptic feedback (GM−NH), and the "real-time" pantomime grasps directed at imagined copies of the virtual targets (RPGNH). Note that the error bars reflect 95% confidence intervals extracted from the mean square error term from the rmANOVA (corrected for violations of sphericity where appropriate). (A) RT increased when a mirror was used rather than a pane of glass for target-directed grasps with haptic feedback. The RT increased further when haptic feedback was denied and increased further still when the participants performed displaced-pantomime grasps. (B) PHV slowed when a mirror was used rather than a pane of glass for target-directed grasps with haptic feedback. PHV slowed further when haptic feedback was denied and when the participants performed displaced-pantomime grasps.

The PHV was significantly faster for GG−H than it was for RPGNH (t(24) = 4.54, p < 2 × 10−4 ), suggesting that the slowing of PHV when haptic feedback is denied occurs regardless of whether online visual feedback of the hand and limb is available or not.

# Peak Grip Aperture (PGA)

The rmANOVA of the mean PGA revealed a significant main effect of Task, Fadj(2,47) = 18.5, p < 2 × 10−6 , η 2 p = 0.44 (see **Figure 6A**). The PGA for GM−H was significantly larger than the PGA for GG−H (t(24) = 5.16, p < 3 × 10−5 ), indicating a significant effect of online visual feedback of the hand and limb in the offline and/or online updating of grip aperture in the natural grasping task.

The PGA was significantly smaller for GM−NH than the PGA for GM−H, t(24) = 3.4, p < 3 × 10−3 . In turn, the PGA for RPGNH was significantly smaller than the PGA for GM−H, t(24) = 6.43,

FIGURE 6 | The PGA, slopes relating PGA to target size (bPGA), the final grip aperture (FGA), and the slopes relating the FGA to the target size (bFGA) across the four tasks. (A) The overall PGA increased when a mirror was used (GM−H) rather than a pane of glass (GG−H) for target-directed grasps with haptic feedback. For grasps directed a virtual targets, removing haptic feedback (GM−NH) reduced the PGA. The PGA was reduced further for the real-time pantomimed grasps (RPGNH). For each task the mean PGA for each target size is plotted for each participant. Evidently, denying haptic feedback increased the slopes. (B) The participants' bPGAs (open circles) and the mean bPGA (dashes) for each task. The bPGAs for GM−H were smaller than those for GG−H, indicating a significant role for online visual feedback of the hand and limb. The bPGAs were larger, however, whenever haptic feedback was denied, regardless of whether the grasps were target-directed (GM−NH) or not (RPGNH). (C) The overall FGA was reduced when haptic feedback was not available. Plotted for each task is the mean FGA for each target size for each participant. Even in the absence of haptic feedback, the FGAs were well-related to target size. (D) The bFGA (open circles) did not differ amongst the four tasks. Note that the error bars reflect 95% confidence intervals extracted from the mean square error term from the rmANOVA (corrected for violations of sphericity where appropriate).

p < 2 × 10−6 . However, the PGA for RPGNH was significantly smaller than the PGA for GM−NH, t(24) = 4.01, p < 6 × 10−4 . Thus, removing haptic feedback induced a partial shift in the PGA towards pantomimed grasps. In other words, removing haptic feedback reduced the PGA, but displacing the grasp reduced the PGA further still.

The PGA for the GG−H task was significantly larger than the PGA for the RPGNH task (t(24) = 4.36, p < 3 × 10−4 ), suggesting that the reduction in PGA when haptic feedback is denied also occurs regardless of whether online visual feedback of the hand and limb is available or not.

# Regression Coefficients (Slopes) Relating PGA to Target Width

The rmANOVA performed on the slopes (bPGA) revealed a significant main effect of Task, Fadj(2,52) = 24.4, p < 2 × 10−8 , η 2 p = 0.5 (see **Figure 6B**). The bPGA for GM−H was significantly smaller than the bPGA for GG−H, t(24) = 4.46, p < 2 × 10−4 .

The bPGA for GM−NH was significantly larger than the bPGA for GM−H, t(24) = 7.31, p < 2 × 10−7 . In turn, the bPGA for RPGNH was significantly larger than the bPGA for GM−H, t(24) = 6.33 p < 2 × 10−6 . Finally, the bPGA did not differ significantly between GM−NH and RPGNH, t(24) = 1.79, p = 0.09. Thus, the removal of haptic feedback resulted in a complete shift in the grip scaling slopes toward pantomime grasps. In other words, regardless of whether the grasps were target-directed or not, the slopes were larger when haptic feedback was denied.

The bPGA for GG−H was significantly smaller than the bPGA for RPGNH (t(24) = 4.06, p < 5 × 10−4 ), suggesting that the increase in bPGA when haptic feedback is denied also occurs regardless of whether online visual feedback of the hand and limb is available or not.

Finally, we opted to test for a difference in the bPGA between the controls' of Experiment 1 and the participants in the GG−H task of Experiment 2 using an independent samples t-tests with appropriate adjustments for violations of homogeneity where necessary. We found no significant difference in the bPGA across the two groups (p = 0.64), suggesting that the pane of glass did not affect the bPGA in Experiment 2. Interestingly, pooling the no haptic feedback conditions in Experiment 2 (i.e., GM−NH and RPGNH) to test for an effect of the absence of haptic feedback compared to terminal tactile feedback (i.e., the RPG task of Experiment 1) revealed an increase in the bPGA for the former, t(28) = 3.36, p < 3 × 10−3 . Thus, the results of these additional tests support the findings of Experiment 1 that terminal tactile feedback helps ''normalize'' grip scaling slopes.

# Final Grip Aperture (FGA)—Grip Stability at the End of the Reach

The rmANOVA of FGA revealed a significant main effect of Task, Fadj(2,43) = 20.1, p < 2 × 10−6 , η 2 p = 0.46 (see **Figure 6C**). Not surprisingly, the FGA for GM−H and GG−H did not differ significantly (t(24) = 1.41, p = 0.17.), presumably because this measure was constrained by the widths of the cylinders in these tasks. Thus, the removal of haptic feedback resulted in a complete shift in the FGA toward pantomime grasps. In other words, regardless of whether the grasps were target-directed or not, the FGA was smaller when haptic feedback was denied.

The FGA for GM−NH was smaller than the FGA for GM−H, t(24) = 5.3, p < 2 × 10−5 . In turn, the FGA for RPGNH was significantly smaller than the FGA for GM−H, t(24) = 3.72, p < 2 × 10−3 . However, the FGA for GM−NH did not differ significantly from the FGA RPGNH, t(24) = 1.69, p = 0.1. Thus, the removal of haptic feedback resulted in a complete shift in the FGA toward pantomime grasps. In other words, regardless of whether the grasps were target-directed or not, the FGA was smaller when haptic feedback was denied.

The FGA for GG−H was significantly larger than the FGA for RPGNH (t(24) = 4.19, p < 4 × 10−4 ), suggesting that the reduction in FGA when haptic feedback is denied also occurs regardless of whether online visual feedback of the hand and limb is available or not.

# Regression Coefficients (Slopes) Relating FGA to Target Width

The rmANOVA performed on the slopes relating FGA to target size (bFGA) indicated no significant main effect of Task, Fadj(2,38) = 1.6, p = 0.22 (see **Figure 6D**), suggesting that, even in the absence of haptic feedback, participants on the whole took into account differences in the widths of the virtual cylinders when simulating their grip on them (in the case of GM−NH) or on imagined copies of the virtual cylinders (in the case of the RPGNH).

Finally, we examined the change in the slopes relating PGA to target size (bPGA) and those relating FGA to target size (bFGA) for each task (i.e., ∆b = bFGA − bPGA). This analysis provides an indication of how consistent the slope was from the point in the response at which PGA was achieved (i.e., while the hand was in-flight) to the point at which the FGA occurred (i.e., while the fingers held the object tor simulated holding one). A significant ∆b was observed for GG−H (M = 0.25, SD = 0.18, t(24) = 6.88, p < 5 × 10−7 ) and GM−H, M = 0.4, SD = 0.21, t(24) = 9.7, p < 9 × 10−10. In contrast, the ∆b for GM−NH (M = −0.03, SD = 0.18, t(24) = 0.76, p = 0.47) and RPGNH (M = −0.06, SD = 0.28, t(24) = 1.13, p = 0.27) failed to differ significantly from zero. Thus, the ∆b appeared to be largely driven by the availability of haptic feedback. To confirm this, a rmANOVA performed on the ∆b indicated a main effect of Task, F(3,72) = 39.9, p < 3 × 10−15 . Given the null findings amongst the tasks with respect to the bFGA, the differences in ∆b amongst the tasks are quite likely to have been driven by the differences in the bPGA we reported above. Indeed, follow up tests (not reported) showed that this was true. Thus, the analysis of the ∆b indicates that in the absence of haptic feedback, the participants grip aperture faithfully reflected differences in the widths of the targets while their hand was inflight and when it was simulating holding a visible or imagined cylinder.

# Testing for Possible Methodological Issues With Respect to the Use of the Mirror

Given the significant differences between GG−H (natural grasps directed at real targets viewed through a pane of glass) and GM−H (natural grasps directed at virtual targets viewed in a mirror) tasks across a number of measures, we tested an additional group of participants (see Section Participants) to test for factors other than the online visual feedback of the hand and limb that could be driving this effect. In this follow-up experiment, we employed the GG−H and GM−H tasks (see Sections Participants, Apparatus and Stimuli, General Procedure and Design, Grasping Real Targets Viewed through a Pane of Glass, Grasping Virtual Targets Viewed in a Mirror with Haptic Feedback) however, the grasps in this additional experiment were performed entirely in open loop. In other words, the lenses of the goggles switched from a transparent state to a translucent one as soon as the participants' fingers left the start button. Thus, the only difference between the tasks was that nature of the target image (one being virtual and the other real). If other methodological factors (e.g., subtle mismatch in the placement of the copy of the virtual target or differences in lighting) were responsible for the differences in grasping in the two tasks (rather than the differences in online sources of visual input) then we should replicate the pattern of results that we observed, because these differences would still be present despite the loss of online visual feedback throughout the movements.

The results were clear: in the absence of any visual input throughout the grasping movements, viewing virtual or real targets did not significantly affect the RTs (t(17) = 1.22, p = 0.24), PHVs (t(17) = 1.16, p = 0.26), PGAs (t(17) = 0.26, p = 0.8), or the bPGA, t(17) = 0.14, p = 0.89 (see **Figure 7A**). Thus, the differences in the PHVs, PGAs, and bPGA in Experiment 2 are unlikely to have been driven by methodological factors putatively introduced by using a mirror.

# Removing Online Visual Feedback from the "Real-Time" Pantomime Grasping Task

As we have seen, the PGA for GM−NH was smaller than the PGA for GM−H yet larger than the PGA for the RPGNH. A similar result was observed for the RTs. Specifically, the RTs for GM−NH were slower than those for GM−H yet faster than those for RPGNH. The partial shifts in these measures for GM−NH towards those observed in the pantomime grasping task (i.e., RPGNH) suggest that the target-directed nature of the GM−NH task might have partially compensated for the effect of removing haptic feedback. It also possible, however, that the availability of online visual feedback of the hand and limb or the added shift in gaze or attention that the RPGNH task demanded (as participants looked to towards the empty workspace to imagine a copy of the target) increased the RT. To test these possibilities, we carried out an additional experiment. We reasoned that altering RPGNH so that the grasps were directed to a location immediately next to the virtual target should minimize differences between the two tasks in terms of the availability of visual feedback, shifts in attention, and other factors such as a difference in biomechanical constraint. A difference in PGA or RT following a comparison of the GM−NH and modified RPGNH (i.e., the grasps were directed to the side of the virtual target) would support the suggestion that the target-directed nature of the GM−NH can at least partially compensate for an absence of haptic feedback.

Compared to GM−NH, the modified RPGNH showed slower PHVs (t(17) = 3.73, p < 2 × 10−3 ) (attributable to the modest

grasps in the absence of any online visual feedback (visual open loop). Grasps were directed at "real" (i.e., viewed through a pane of glass) targets (GG−H) and virtual (i.e., viewed in a mirror) targets (GM−H) in visual open loop (vision was occluded at the start of the movement). In the case of GM−H, haptic feedback was available when participants made contact with a spatially coincident duplicate that was positioned behind the mirror. Whether the grasps were directed at virtual targets or real

overall reduction in distance the hand travelled in this task) and, importantly, a smaller PGA, t(17) = 2.75, p < 0.02. Thus, directing the hand away from the target and towards an imagined copy appears to reduce the PGA no matter how far away from the visible object the hand is directed. The results also indicated no significant differences in the RT (t(17) = 0.23, p = 0.82) or in the bPGA, t(17) = 0.14, p = 0.89 (see **Figure 7B**) between GM−NH and modified RPGNH.

## Between-Groups Tests of the Regression Coefficients (Slopes) Relating PGA to Target Width

Testing the additional group of participants also afforded us an opportunity to test for a replication of one of the critical finding of Experiment 2 concerning the grip scaling slopes (bPGA). In a series of independent–samples t-tests involved the bPGA of the GM−NH and RPGNH tasks from the first and second group of participants, and the GM−H task from the first group, and in the series of independent–samples t-tests for the tasks in which the targets were virtual (i.e., viewed in a mirror) and the goggles remained clear for the duration of the movement (i.e., closed-loop with respect to the target). We adjusted the multiple post hoc independent–samples ttests using Holm's step-down Bonferroni procedure (Holm, 1979). The results, again, showed that the critical factor for this measure was the absence of haptic feedback. The bPGA for GM−NH (p < 7 × 10−7 ) and RPGNH (p < 5 × 10−8 ) from the second group of participants were significantly steeper than the bPGA for the GM−H from the first group of participants. Furthermore, none of the tasks in which haptic feedback was denied differed between the two groups of participants (pmax = 0.14, uncorrected).

ones made no difference across any of the dependent measures, including PGA and the slopes. (B) The PGA and slopes for the GDVT task in which haptic feedback was denied and the displaced-pantomime grasping (RPGNH) task in which the grasps were directed immediately to the right of the visible location of the target towards an imagined copy. Whether the grasps were directed towards or beside the virtual target did not affect the slopes, which appear to be quite steep in both tasks. Sending the hand to a location right beside the object did, however, reduce the overall PGA, just as it did for displaced-pantomime grasps to locations more distant from the location of the virtual target.

# Discussion and Conclusions

One of the principal aims of Experiment 2 was to determine whether or not removing both haptics-based object information and terminal tactile feedback (together referred to here as ''haptic feedback'') from a target-directed grasping task shifts the response mode away from a natural one and towards a more pantomimed (i.e., simulated) kind as has been suggested by Milner et al. (2012). To do this, we compared target-directed grasps with (GM−H) and without haptic feedback (GM−NH) to pantomime grasps (RPGNH) in which the participants were asked to imagine a copy of the target in another location in the workspace and to grasp that imaginary object as if it were actually there (e.g., Goodale et al., 1994b; Holmes et al., 2013). We found that when participants reached out to grasp virtual targets, removing haptic feedback slowed RT and PHV, reduced PGA, increased the slopes relating PGA to the width of the target, and reduced the FGA. Just as important was the fact that the grasps directed at virtual targets (viewed in a mirror) without haptic feedback were statistically indistinguishable from the pantomime grasps in terms of the PHV, the slopes relating PGA to target size, the slopes relating FGA to target size, and the FGA, suggesting a complete shift across these measures towards pantomimed grasping following the removal of haptic feedback. The only measures that differed between the two ''no haptic feedback'' tasks were the RT and the magnitude of the PGA. It is important to acknowledge, however, that removing haptic feedback from grasps directed at virtual targets slowed the RTs and reduced the PGAs. In other words, both of these measures registered a shift in the direction away from natural grasps and towards the pantomimed ones.

An additional aim of Experiment 2 was to determine whether or not the mirror itself has an effect on the kinematics of target-directed grasps. After all, the mirror introduces three key differences when compared to natural grasps: First, the mirror blocks the participant's view of their hand and limb as soon as the participant reaches behind it (removing re-afferent online visual feedback). Second once the participants make contact with the hidden object and the virtual target, the mirror imposes a disconnect between the felt movements of the hidden object and the apparently stationary target visible in the mirror. Although this effect might startle the participants at first, it is reasonable to suggest that the participants acclimate to this situation, growing more comfortable on subsequent trials. This says nothing, however, about any possible effects all of this might have on the unconscious ''automatic'' online control mechanisms that normally mediate grasping. Third, the mirror might act as an obstacle that the participants attempt to avoid. Given these considerations, we implemented an additional task in which the participants reached out to grasp target they viewed through a pane of glass. The pane of glass was the same size as the mirror and was positioned in the same way with respect to the participant. Compared to natural grasps directed behind the pane of glass, the ones directed behind the mirror resulted in slower PHVs, larger PGAs, and shallower slopes. Nevertheless, it was possible that some other aspects of the mirror task may have played a role. We ruled these factors out in a control experiment in which we removed online visual feedback altogether for both tasks. In this control experiment, all the differences between the grasps directed behind the mirror and the grasps directed behind the pane of glass completely disappeared, strongly implicating a role for one or more of the sources of online visual feedback outlined above in the programming and updating of targetdirected grasps. Given Connolly and Goodale's (1999) null findings concerning the magnitude of the PGA and the fact that the participants in that study were permitted a view of the target and the hand making contact with the target, then the results of the current investigation suggest a significant role for vision during the contact and manipulation phase of the grasping movement in the programming of grip aperture on subsequent grasps.

Many of the additional findings in the present investigation can be explained through the changes in task demands and differences in the availability of visual and haptic input. For example, the overall reduction in PGA and FGA in the absence of haptic feedback (see also Bingham et al., 2007; Fukui and Inui, 2013) is likely due to the removal of the physical constraints that the object imposes on the fingers and, therefore, the minimum magnitude that the grip aperture would normally be required to achieve a suitable grasp. Without the physical constraints imposed on the fingers and hand by an actual object, there would be (1) no consequences for consistently under-sizing grip aperture, such as knocking the object away; and (2) less effort (and perhaps even more comfort) in opening the hand a smaller amount. The FGA, being a measure of grip stability when the target is being held, would necessarily be restricted by the sizes of the cylinders. We speculate that the selective removal of haptics-based object information might also lift this restriction and result in a similar reduction in FGA. Nevertheless, unlike the FGA, the PGA was affected by both haptic feedback and online visual feedback of the hand and limb. Specifically, providing online visual feedback and removing haptic feedback each effected reductions in the PGA. The effect of online visual feedback of the hand and limb on PGA observed in the present study is in line with previous findings following a comparable manipulation (Whitwell et al., 2014a,b) and is also in line with the broader literature on the effects of removing online visual feedback entirely (e.g., Jakobson and Goodale, 1991; Whitwell et al., 2008; Whitwell and Goodale, 2009; Hesse and Franz, 2010). The effect of removing haptic feedback on PGA observed in the present study is also in line with previous reports in which terminal tactile feedback was almost certainly denied (Bingham et al., 2007; Fukui and Inui, 2013). Interestingly, (although not always explicitly tested), a similar effect on PGA appears to occur in the absence of haptics-based object information when short (e.g., ∼1 cm in height) block-like stimuli (or even 2D images) are used in which the fingers are highly unlikely to avoid touching the surface of the table at the end of the reach (e.g., Westwood et al., 2002; Cavina-Pratesi et al., 2011; Holmes et al., 2013). If we assume an additive model of the effects of online visual feedback and haptic feedback, then consideration of the details of the tasks of the present study readily explain the findings involving FGA and PGA.

In keeping with an appeal to differences in task demands, we should point out that we required the participants to refrain from sending their hand and fingers through the visible or imagined target for the tasks in which haptic feedback was removed. We would argue that most (if not all) tasks in which the participants simulate grasps carry with them analogous instructions, regardless of whether such instructions are stated explicitly by the experimenter or are tacitly understood by the participant. Critical to this is (1) any consideration the participants might give to the sizes and positions of the target in a situation in which the target is not actually there; and (2) how well the participants understand what their hand does when they reach out to pick up a goal object. It seems likely that these factors account for the increase in RT when haptic feedback was denied. A similar appeal to differences in task demands can explain the additional increase in RT that occurred when the grasps were directed at an imagined copy of the visible object. Unlike the other grasping tasks, the instructions for the pantomime grasps required the participants to imagine a copy of the visible target at a different location. Presumably, participants would first look at the visible target and then look towards the location where they were to imagine a copy of that object before or shortly after they initiated their response. In contrast, in the target-directed grasping tasks (with and without haptic feedback), the target's viewed position and the location to which the participants sent their hand are one and the same. We suspect that the addition of a preparatory shift in gaze in the pantomime grasping task likely increased the RT relative to the targetdirected grasping task in which haptic feedback was denied. It is possible that the biomechanical difference in the direction that the participants sent their hand and limb in pantomime grasping task or the availability of online visual feedback might also play a role in the increase in RT. We should point out, however, that in the control experiment in which haptic feedback was denied, RT did not depend on whether the participants directed their hand towards the virtual target or beside it. In other words, the difference in RT between pantomimed and target-directed grasps without haptic feedback was abolished when the pantomime task was modified to minimize differences in shifts in gaze or attention, biomechanical constraints, and online visual feedback. Furthermore, we note that online visual feedback did not influence the RT of natural grasps in the current study—a finding consistent with previous investigations of natural grasps with and without online vision (e.g., Whitwell et al., 2008; Hesse and Franz, 2010). Thus, it seems unlikely that this factor can account for differences in RT in the absence of haptic feedback.

In contrast to the RTs and the PGA, the PHV, the slopes relating PGA to target size, the slopes relating FGA to target size, and the FGA were not affected by the added requirements of pantomime grasps when compared to the target-directed grasps without haptic feedback. In other words, for these measures of movement execution, the target-directedness of the response was not a critical factor. Instead, the removal of haptic feedback about the object appeared to dominate, independent of whether the grasp was directed to a visible or an imagined target. In line with Bingham et al.'s (2007) finding, without haptic feedback the PHVs were slower. The participants likely approached the targets more cautiously and deliberately, presumably because they were simulating what they would do if an object was actually there, making sure that their fingers did not go through the visible or imagined object. Importantly, the slopes increased relative to the slopes for grasps that received haptic feedback, approaching a 1:1 relationship between changes in the width of the target and changes in PGA. In fact, the slopes in these tasks resemble those observed during manual estimation tasks in which the participants indicate the width of a visible object by opening their thumb and index-finger a matching amount (e.g., Daprati and Gentilucci, 1997; Haffenden and Goodale, 1998; Pettypiece et al., 2010; Schenk, 2012a; Whitwell et al., 2014a, in press). Thus, the increase in the grip scaling slope when haptic feedback is not available would appear to reflect the deliberate consideration given to the sizes of the targets in these simulated grasps. As Whitwell and Buckingham (2013) noted, removing haptic feedback from a real-time grasping task appears to increase the grip-scaling slopes (Schenk, 2012a). In our experiment (see also Byrne et al., 2013) we explicitly tested this and found that, in the absence of haptic feedback, the slopes do, in fact, increase relative to natural grasping tasks. Interestingly, on a task that is not unlike the delayedpantomimed grasping task devised by Goodale et al. (1994b), the slopes appear to increase relative to those observed on a natural grasping task regardless of whether vision of the workspace is available at the time of the movement or not (see Fukui and Inui, 2013). Overall, it seems reasonable to conclude that in the absence of haptic feedback, the geometric properties of the target are taken into explicit consideration when planning and programming the grasp. Thus, DF's poor grip scaling slope when haptic feedback is consistently denied (Schenk, 2012a) can be attributed to a switch in the kind of response she provided towards a more pantomimed or simulated one as Milner et al. (2012) suggested. Interestingly, as we showed in Experiment 1, the provision of some proxy next to the visible target (in our case the surface of the table) has a normalizing influence on DF's and the controls' slopes. This finding adds to a growing body of work indicating that DF's slope remains normal provided that real-time visual input is available along with tactile feedback from a proxy of the target (Westwood et al., 2002; Whitwell et al., 2014a,b).

Additional support for a distinction between haptics-based object information from a real (3D) object and tactile feedback from a proxy comes from studies of the influence of a mismatch between the haptic and visual size of target objects. When normally-sighted participants reach out to grasp objects in which the apparent visual width of the objects differs from their felt width, they typically show some adaptation in their PGA to the actual (i.e., the felt) size of the target—even though they continue to scale their grip aperture to the visual width of the target (e.g., Gentilucci et al., 1995; Säfström and Edin, 2004, 2008; Pettypiece et al., 2010). In fact, DF responds in an identical manner, suggesting that (1) the ventral stream is not required for the updating of grip aperture to reflect the real size of a target and that (2) veridical haptics-based object information is not required for DF to maintain normal grip scaling to trial-to-trial changes in the visual sizes of targets (Whitwell et al., 2014a,b). Rather, DF's dorsal stream can exploit terminal tactile feedback to update her grip aperture on subsequent grasping movements and to maintain normal visuomotor processing of target shape to program movements parameters like grip aperture. Thus, it seems reasonable to conclude that (1) provided real-time visual input is available, tactile feedback from the surface of the table is sufficient to keep the visuomotor networks in DF's dorsal stream engaged; and that (2) the damaged areas of DF's ventral stream are not necessary for grip scaling for grasps that are directed towards the table surface next to a visible object.

Since Goodale et al.'s (1994a) study, pantomime grasps have been used in many kinematic investigations and is considered a tool to test the role that perception plays in the visual control of skilled actions. For example, the PGAs of pantomime grasps have been shown to be more susceptible to the Muller-Lyer illusion than natural grasps (Westwood et al., 2000). In addition, the within-subject variability of the PGAs of pantomimed grasps, but not natural ones, obeys Weber's Law (i.e., the variability of the PGA increases linearly with target width; Holmes et al., 2013; although see Foster and Franz, 2013). In fact, even the movement preparatory time for pantomimed grasps, but not for natural gasps, is increased by the holistic object-perception that is thought to underlie Garner interference (Ganel and Goodale, 2003, 2014). Moreover, patient IG, who suffers from optic ataxia following damage to her PPC shows a paradoxical improvement in the correlation between her PGA and target width when she executes pantomime grasps following a delay period compared to natural grasps (Milner et al., 2001). Finally, provided the object is visible, the hand kinematics of magicians (who routinely pantomime actions to deceive their audiences) look far more like those of natural grasps than they do those of non-magicians (Cavina-Pratesi et al., 2011). In all of these studies, haptic feedback about the object was denied but not terminal tactile feedback about the end of the movement. Thus, tactile feedback from the tabletop is not enough to preserve all of the kinematics of a real grasping movement. Indeed, when neurologically-intact individuals pretend to pick up 2D images, the variability of their grip aperture scales with target size as Weber's law would predict (Holmes and Heath, 2013), just as it does for pantomimed grasps (Holmes et al., 2013). Furthermore, grasps that are directed towards 2D objects invoke holistic processing (Freud and Ganel, 2015) in which the irrelevant and relevant target dimensions interact to influence processing times. This is not so for grasps that are directed at 3D objects (e.g., Janczyk and Kunde, 2012; Eloka et al., 2014; Freud and Ganel, 2015).

Importantly, it remains to be seen whether the cognitive or perceptual effects associated with pantomimed grasps are indeed mediated by ventral stream processing as is commonly assumed. An interesting future direction might be to test DF's pantomime grasps for evidence of holistic processing (e.g., Garner interference) and relative sensitivity to stimulus magnitude (e.g., Weber's law). Interestingly, pantomime grasps directed to the workspace next to a visible object fail to elicit preferential activity in the temporaloccipital areas in healthy participants, uniquely recruiting, instead, regions in the supramarginal gyrus, middle intraparietal sulcus, and supplementary motor area of the right hemisphere (Króliczak et al., 2008)—areas that remain intact in DF. These findings, combined with those of Goodale et al. (1994b) and the present study, suggest that a delayed pantomime grasping task would invoke preferential activity in areas of the occipito-temporal cortex of healthy individuals that

# References

are damaged in DF. Interestingly, these areas are in fact recruited when reach-to-grasp movements are based on a memory of the target, albeit in the context of a delayed grasp (as opposed to a delayed pantomime grasp) which received haptic feedback about the remembered object at the end of the reach (Singhal et al., 2013). Thus, although pantomime grasps with tactile feedback invoke cognitive and perceptual influences that are absent in natural grasps, some of these influences (e.g., the effects of holistic processing on movement preparation time, or of stimulus magnitude) might well emerge from a combination of visual processes in the ventral stream and the inferior parietal cortex of the right hemisphere.

In summary, the current study shows clear evidence that the removal of haptic feedback induces a shift from natural towards pantomimed (simulated) grasps, as suggested by Milner et al. (2012). The pattern of changes in the grasps kinematics, longer initiation times, slower movements, and steeper slopes were indicative of a more deliberate process of responding in which the participants explicitly took into account the metrics of the object, the location to which they were directing their hand, and the path that their hand and fingers would take. Furthermore, as Fukui and Inui (2013) have pointed out, the reduction in grip aperture that followed the removal of haptic feedback presumably reflects a natural consequence of the removal of a physical object, which, normally, would impose a constraint on the grip aperture of a natural grasp. Thus, the removal of haptic feedback also changes the task incentives. Without haptic feedback, there is no obvious consequence for an inaccurate grasp. These results and those of other investigations highlight the importance of haptics-based object information, or, at the least, terminal tactile feedback, in maintaining normal grasps which, we have shown here with patient DF, depend on intact dorsal pathways.

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Whitwell, Ganel, Byrne and Goodale. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Dissociable contribution of the parietal and frontal cortex to coding movement direction and amplitude

Marco Davare1,2,3† , Alexandre Zénon1† , Michel Desmurget 4 and Etienne Olivier 1 \*

1 Institute of Neuroscience (IoNS), School of Medicine, University of Louvain, Brussels, Belgium, 2 Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, University College London, London, UK, 3 Department of Kinesiology, Movement Control and Neuroplasticity Research Group, Biomedical Sciences Group, KU Leuven, Leuven, Belgium, 4 Centre de Neurosciences Cognitives, CNRS, UMR 5229, Bron, France

To reach for an object, we must convert its spatial location into an appropriate motor command, merging movement direction and amplitude. In humans, it has been suggested that this visuo-motor transformation occurs in a dorsomedial parieto-frontal pathway, although the causal contribution of the areas constituting the "reaching circuit" remains unknown. Here we used transcranial magnetic stimulation (TMS) in healthy volunteers to disrupt the function of either the medial intraparietal area (mIPS) or dorsal premotor cortex (PMd), in each hemisphere. The task consisted in performing steptracking movements with the right wrist towards targets located in different directions and eccentricities; targets were either visible for the whole trial (Target-ON) or flashed for 200 ms (Target-OFF). Left and right mIPS disruption led to errors in the initial direction of movements performed towards contralateral targets. These errors were corrected online in the Target-ON condition but when the target was flashed for 200 ms, mIPS TMS manifested as a larger endpoint spreading. In contrast, left PMd virtual lesions led to higher acceleration and velocity peaks—two parameters typically used to probe the planned movement amplitude—irrespective of the target position, hemifield and presentation condition; in the Target-OFF condition, left PMd TMS induced overshooting and increased the endpoint dispersion along the axis of the target direction. These results indicate that left PMd intervenes in coding amplitude during movement preparation. The critical TMS timings leading to errors in direction and amplitude were different, namely 160–100 ms before movement onset for mIPS and 100–40 ms for left PMd. TMS applied over right PMd had no significant effect. These results demonstrate that, during motor preparation, direction and amplitude of goal-directed movements are processed by different cortical areas, at distinct timings, and according to a specific hemispheric organization.

Keywords: action planning, prehension, goal-directed, step-tracking, sensorimotor transformation, posterior parietal cortex, reaching, transcranial magnetic stimulation

# Introduction

Visually-guided movements require sensory information about the target to be extracted and transformed into an appropriate motor command (Crawford et al., 2011; Vesia and Crawford, 2012). In the particular instance of arm movements aimed at grabbing an object, two types of

#### Edited by:

Simona Monaco, University of Trento, Italy

#### Reviewed by:

Patrizia Fattori, University of Bologna, Italy Neil Albert, Colgate University, USA

#### \*Correspondence:

Etienne Olivier, Institute of Neuroscience (IoNS), School of Medicine, University of Louvain, 53, Avenue Mounier COSY-B1.53.04, B-1200 Brussels, Belgium<EMAIL_ADDRESS>
†These authors have contributed equally to this work.

> Received: 29 January 2015 Accepted: 14 April 2015 Published: 06 May 2015

#### Citation:

Davare M, Zénon A, Desmurget M and Olivier E (2015) Dissociable contribution of the parietal and frontal cortex to coding movement direction and amplitude. Front. Hum. Neurosci. 9:241. doi: 10.3389/fnhum.2015.00241 visual information related to object's extrinsic and intrinsic features need to be extracted to feed two independent transformation processes, which lead to two separate movement components i.e., a reaching (transport of the hand) and a grasping (pre-shaping of the hand posture) component (Jeannerod et al., 1995; Jeannerod, 1997). A large number of experiments in both human and non-human primates corroborated the view that two separate pathways, connecting the posterior parietal cortex (PPC) to the premotor cortex, subserve the two movement components underlying prehension, allowing primates to interact so skillfully with their environment (Jeannerod et al., 1995; Castiello, 2005; Culham et al., 2006). According to this classical view, the reaching component is subserved by a dorsomedial pathway, connecting the medial part of IPS (mIPS) and parieto-occipital junction (POJ) to the dorsal premotor cortex (PMd) and the grasping component relies on a dorsolateral circuit connecting the anterior intraparietal (AIP) and ventral premotor (PMv) cortex (Jeannerod et al., 1995; Castiello, 2005; Davare et al., 2011) although some findings challenge the view that these two circuits process reach and grasp independently (Raos et al., 2004; Fattori et al., 2012). A comparable organization has been evidenced in non-human primates although homologies between human and monkey PPC remain debated (Mars et al., 2011; Vesia and Crawford, 2012; Andersen et al., 2014; Turella and Lingnau, 2014).

However, the question of the causal contribution of individual areas belonging to this ''reaching pathway'' remains open. In humans, the technique of choice to address this issue is transcranial magnetic stimulation (TMS), and like pharmacological inactivation in monkeys, this approach has the advantage of having a relatively good spatial resolution and of precluding long-term compensation, which might hamper the conclusions of clinical studies. Additionally, TMS offers the unique possibility of establishing the time course of the contribution of the studied area to the task at hand, a piece of information normally unavailable from functional imaging studies (but see Gallivan et al., 2011a,b). We have already used TMS to investigate the grasping circuit in details, deciphering both the causal role of the anterior part of the intraparietal sulcus (aIPS) and PMv, and the time course of their respective contribution when planning a grasping movement (Davare et al., 2006, 2007a; Olivier et al., 2007). More recently we applied the same approach to mIPS, one of the key nodes of the reaching circuit, and found that, during motor preparation, this area encodes the direction of goal-directed movements performed towards contralateral targets (Davare et al., 2012).

However, in addition to movement direction, planning a reaching movement also requires specifying its amplitude. Interestingly during the last two decades, many behavioral studies have suggested that movement direction and amplitude are processed separately, by showing, for instance, that the variability and systematic biases of direction and amplitude errors are independent (Gordon et al., 1994; Messier and Kalaska, 1997, 1999; Vindras et al., 2005). However, whereas, in both humans and monkeys, the direction coding in the primary motor cortex (M1), premotor areas, and the PPC has been well documented (Caminiti et al., 1991; Georgopoulos, 1995; Kakei et al., 1999, 2001; Eisenberg et al., 2010; Fabbri et al., 2010), the neural correlates of amplitude coding are less clear (Riehle and Requin, 1989; Kurata, 1993; Riehle et al., 1994; Fu et al., 1995; Desmurget et al., 1998; Messier and Kalaska, 2000). In monkeys, it has been shown that most cells in PMd encode both movement direction and amplitude (Kurata, 1993), and that this coding possibly occurs serially at a single-cell level, with direction coding occurring first during movement preparation, followed by amplitude coding, spreading over movement execution (Fu et al., 1995). More recently, it has been confirmed that cells coding only for movement amplitude are very uncommon in monkey PMd and that most cells encode both direction and amplitude at some points during the performance of an instructed-delay reaching task (Messier and Kalaska, 2000). Similarly in the parietal cortex, recent studies have also found cells encoding amplitude when monkeys performed reaching movements in 3D requiring different depths (Bhattacharyya et al., 2009). Again, direction and amplitude jointly influenced cell-spiking activity in the earlier planning stages of the reach, while amplitude coding became stronger towards movement execution (Hadjidimitrakis et al., 2014). In humans, sensitivity to movement direction has been demonstrated in a large number of cortical areas including M1, PMd and the parietal reach region (PRR; Eisenberg et al., 2010; Fabbri et al., 2010, 2014), with the strongest directional selectivity in the right PRR, then decreasing in the frontal areas (Fabbri et al., 2010). In an attempt to identify cortical areas involved in amplitude coding, Fabbri and collaborators also investigated the sensitivity to movement amplitude in those parietal and frontal regions known to be tuned to movement direction (Fabbri et al., 2012). This study demonstrated that all PPC regions showing directional tuning for reaching movements (inferior parietal lobule (IPL), aIPS, posterior intraparietal sulcus (pIPS) and superior parieto-occipital cortex (SPOC)) are also sensitive to movement amplitude, in contrast with the conclusion of a TMS study showing no evidence for amplitude coding in mIPS (Davare et al., 2012). In addition, the authors reported that the frontal areas, including PMd, show only a partial transfer from the adapted to the non-adapted amplitude, suggesting that the amplitude might be processed differently in parietal and frontal areas (Fabbri et al., 2012). However, these approaches are correlative and a covariation of neural activity with the movement direction and/or amplitude does not prove that the area under investigation is causally involved in processing these parameters (Messier and Kalaska, 2000).

In order to establish the causal contribution of the dorsomedial pathway to the movement amplitude and/or direction coding in humans, we used TMS to interfere with the function of two of the key nodes belonging to the ''reaching'' circuit, namely mIPS and PMd, on both sides. TMS was applied in healthy volunteers while performing goal-directed wrist movements with a manipulandum (Hoffman and Strick, 1986) operated with the right hand. The movements were executed towards visual targets located at different direction and amplitude in both visual hemifields; the targets either remained visible for the whole movement duration (Target-ON) or were flashed for 200 ms before movement onset (Target-OFF). The latter condition was chosen to further validate our conclusion concerning the causal role of mIPS in coding movement direction, since in our previous study, movements were performed under constant visual feedback (Davare et al., 2012). As far as PMd is concerned, we reasoned that if, as suggested in monkeys, this area encodes both movement direction and amplitude, interfering with the functioning of this area should alter these two movement parameters, possibly at different delays during movement preparation or execution. TMS has already been shown to allow the identification of two different, temporally dissociated, movement parameters within the same cortical area (Davare et al., 2007a).

# Materials and Methods

# Subjects

Six healthy right-handed (Oldfield, 1971) subjects (range 23–29 years) with normal, or corrected to normal, vision gave their informed consent to participate in the present study. None had history of neurological disease. Potential risks of adverse reactions to TMS were evaluated by means of the TMS Adult Safety Screen questionnaire (Keel et al., 2001). The present experiment was approved by the local ethical committee of the Université catholique de Louvain.

# Experimental Setup

Subjects sat comfortably in front of a 19 inches computer screen located at a distance of 60 cm. Their right forearm was fastened midway between pronation and supination while the right hand grasped the handle of a two-axis manipulandum (Hoffman and Strick, 1986; Davare et al., 2007b, 2012). A potentiometer placed on each axis of the manipulandum allowed us to measure wrist displacements in the horizontal (flexion-extension (FE)) and vertical (radial-ulnar (RU)) planes. A yellow cursor (0.4◦ wide dot) displayed on the screen continuously indicated the manipulandum position. Eye position was monitored by means of an infrared camera (Thomas Recording GmbH) with a 4 ms temporal resolution. Trials were interrupted whenever a saccade occurred during the fixation period (frequency: 4.1 ± 1.2%, mean ± SD, n = 6). Those aborted trials were then replayed at the end of the experiment until all trials had been executed correctly.

# Transcranial Magnetic Stimulation

Single-pulse TMS was delivered through a 70 mm figure-ofeight coil connected to a Magstim 200 stimulator (Magstim, Whitland, UK). Before each experiment, the resting motor threshold—defined as the intensity for which single-pulse TMS applied over the primary motor cortex produced a wrist movement in 50% of the cases—was estimated while the subjects were comfortably seated with their hand relaxed on their lap. TMS intensity was then set at 120% of the resting motor threshold for the whole experimental session. Trials with and without TMS were randomly intermixed during each experimental block.

We used a neuronavigation technique (Noirhomme et al., 2004; Zosso et al., 2006) to place the TMS coil either over mIPS or PMd in either the left or right hemisphere, as identified in the literature (Connolly et al., 2003; Prado et al., 2005; Davare et al., 2012). Anatomical landmarks were used to guide coil placement: PMd was located as the most caudal portion of the superior frontal gyrus at the level of its intersection with the precentral gyrus; mIPS was located over the medial portion of the IPS, near the caudal part of the angular gyrus (see Davare et al., 2012). The mean normalized MNI coordinates of our stimulations points were −33, −47, 48 and 31, −45, 53 mm for left and right mIPS, respectively, and −21, −6, 68 and 22, −7, 65 for left and right PMd, respectively (x, y, z, n = 6) (**Figure 1A**).

# Experimental Procedure

Each trial started with the wrist in a neutral position, a condition fulfilled when the cursor indicating the position of the manipulandum (yellow circle) was at the center of the screen, indicated by a 15 mm (1.5◦ of field of view) blue square (**Figure 1B**). Subjects were instructed to fixate this central cue at the beginning of each trial. After a 700 ms delay, the central cue was turned off, replaced by a fixation cross, and a 15 mm red square target was randomly displayed in one out of the 20 possible locations. There were 5 possible target locations in each quadrant, corresponding either to a 45◦ direction and to a wrist movement amplitude of 10, 15 or 20◦ , or to a fixed 15◦ movement amplitude in a 33, 45 or 57◦ direction (see **Figure 1B**). We also varied the duration of visual feedback to disentangle online corrective mechanisms from movement planning processes. Targets were displayed either briefly, for 200 ms (Target-OFF condition) or during the whole trial duration (Target-ON condition). Subjects were instructed to move the cursor into the target as rapidly and as accurately as possible. In the Target-ON condition, subjects had to keep the cursor inside the target for at least 700 ms to complete the trial. In the Target-OFF condition, the end of the trial occurred when the cursor velocity dropped below 5% of the velocity peak and remained stable for at least 700 ms. Inter-trial interval varied randomly from 3.5 to 5 s. Throughout the trial, subjects had to maintain eye fixation on the central cross in order to prevent any confounding effects of TMS on eye movements, which could have had indirect consequences on the planning of hand reaching movements.

The experiment consisted of eight blocks of 200 trials in which TMS was applied either over the left or right mIPS or PMd (in separate blocks, 2 blocks each). The order of the 8 blocks was pseudorandomly distributed across subjects. In 80% of the cases, single pulse TMS was delivered either at 100 or 200 ms after target presentation and, in 20% of the cases, no TMS pulse was delivered but the coil remained in place over the current stimulation site.

# Data Acquisition and Analysis

The position signal from the 2 potentiometers was digitized (sampling rate: 1 kHz; PCI-6023E, National Instruments, Austin, TX), and stored on a personal computer for offline analysis. Then, these signals were low-pass filtered offline (16 Hz) with a

Frontiers in Human Neuroscience | www.frontiersin.org May 2015 | Volume 9 | Article 241 |

FIGURE 1 | (A) Location of transcranial magnetic stimulation (TMS) sites over mIPS and PMd in the left and right hemispheres after normalization into the MNI coordinate system (n = 6 for each site). The template used for rendering the cortical surface is Colin27 and each normalized stimulation point has been projected on the template cortical surface using the Brainsight stick tool (Rogue research, Montreal). (B) Location of the 20 possible target locations on the computer screen; only one target was randomly displayed during a given trial. The central square represents the starting point. The horizontal axis is the flexion-extension axis of the wrist, the vertical one the radial-ulnar deviation axis. In each quadrant, there were 5 possible target locations: 3 directions (45 ± 12◦ ) and for the target along the 45◦ axis, 3 possible eccentricities (10, 15 or 20◦ ). Targets were either displayed for the whole movement duration (Target-ON condition) or flashed only for 200 ms (Target-OFF condition).

fourth order, zero-phase-lag, Butterworth filter (see Davare et al., 2007b for details).

We measured the following movement variables: (1) the reaction time (RT), defined as the delay between target onset and the moment when wrist position first exceeded the mean baseline position by 2 SD or more; (2) The movement time (MT), defined as the delay between wrist movement onset and the time the cursor velocity dropped below 5% of the velocity peak and remained stable for at least 700 ms in the Target-OFF condition or inside the target for the Target-ON condition; (3) The displacement ratio (DR), measured by computing the ratio between the total distance traveled by the wrist and the distance between the start and end point of the trajectory. DR provides a reliable estimate of the length of movement trajectory; a DR value equal to 1 corresponds to a straight wrist displacement from the screen center to the target (Davare et al., 2007b, 2012); (4) The value of acceleration and velocity peaks, both known to be linearly related to movement amplitude (Hoffman and Strick, 1986); (5, 6) The mean value and standard deviation (SD) of the initial movement direction were computed to estimate, respectively, the constant (DIRCE) and variable direction errors (DIRVE). The initial movement direction was measured by computing the direction of the velocity vector at the peak acceleration. This parameter allowed us to determine the direction of the movement initially planned, before any visual feedback may take place (Prablanc and Martin, 1992; Desmurget et al., 2005), as indicated by the early occurrence of the peak acceleration (28.7 ± 8 ms in the present study, mean ± SD). DIRCE was computed by taking the difference between the initial movement direction and the direction of the target; (7) Endpoints of step-tracking movements were only computed for the Target-OFF condition. They were measured at the end of the movement and segregated into endpoint constant and variable errors (Desmurget et al., 1999). Endpoint constant errors were computed as the Euclidean distance between the target and endpoint locations. Endpoint variable errors were estimated by measuring the area of the isodensity ellipsoid in which 95% of the endpoints were located; and (8) The shape of the isodensity ellipsoid was determined by computing the ratio between the length of the long and short axes. In order to group the endpoints for the 10 target locations presented in each hemifield, endpoint coordinates were normalized to the 15◦ target amplitude and 45◦ target direction (see **Figure 1B**) by rotating and scaling the horizontal and vertical coordinates of the endpoints.

In order to determine more precisely the time course of the effects of TMS applied over mIPS and PMd on those different movement parameters, each trial was classified with respect to the actual delay between TMS and movement onset and was assigned to a 20 ms time bin (12 bins, ranging from 200 ms before movement onset to 40 ms after movement onset). For each subject and for each bin, an averaged value of the studied parameter was computed, provided that at least 3 data points were available; mean values in each bin were then averaged for all subjects (Davare et al., 2007b). **Figures 2**–**4** only show data for the specific time window TMS was effective (i.e., 100–160 ms bins for mIPS and 40–100 ms bins for PMd.

# Statistical Analysis

First, we performed repeated-measure analyses of variance (ANOVA-RM) for each TMS site separately (left mIPS, right

when no TMS effect was revealed. Only TMS applied over mIPS increased the variability in the initial movement direction, and only for movements directed towards contralateral targets, as shown in purple for left mIPS and in green for

movement onset; the gray shading area highlights data points significantly different from control (no TMS) conditions, shown in the right-hand side of the graph. Error bars illustrate ±1 SD. Results for right mIPS TMS were the same, but only for left targets (not shown).

mIPS, left PMd and right PMd), with TMS DELAY (13 levels: no-TMS and 12 bins), HEMIFIELD (ipsi- or contralateral) and target DISPLAY conditions (Target-ON or Target-OFF) as within-subject factors. In order to increase the number of data points per condition and per subject when analysing the movement variables, we pooled together all targets of

right mIPS (averages were only computed for the specific TMS timing showing a

different eccentricities and directions from the same hemifield. All movement parameters were averaged per condition within each subject. The ANOVA-RM analyses were performed with the SAS Enterprise Guide software, Version 5.1. (Copyright © 2012 SAS Institute Inc., Cary, NC, USA). We performed planned comparisons, investigating selectively the relevant contrasts,

and used Tukey correction for multiple comparisons. Then, for each movement parameter, we performed a global analysis including data from all TMS SITES, including them as additional factor.

We analyzed the effects of target eccentricity (10, 15 or 20◦ ) on the peaks of movement derivatives (velocity and acceleration) by means of multiple linear regressions including subjects dummy variables to take into account the correlation between repeated measures. For this analysis, we pooled all target directions together so as to find the global correlation between these movement derivatives and target eccentricity.

# Results

# Effects of TMS on Initial Movement Direction

TMS applied over the left or right mIPS, but not the left or right PMd, altered MT, DR and DIRVE, in agreement with our previous findings (Davare et al., 2012). For these parameters, we found a significant DELAY × HEMIFIELD interaction for both mIPS sites (all F(12,260) > 3.80, all p < 0.0001 for left mIPS TMS; all F(12,260) > 2.20, all p < 0.012 for right mIPS TMS). Planned comparisons of all bins against the no-TMS condition, for each hemifield, revealed that, TMS applied over the left mIPS, led to an increase in MT, DR and DIRVE only for movements

directed towards contralateral targets (**Figure 2A**), and only when TMS was delivered 160–100 ms before movement onset (all t(260) > 3.06, all p < 0.0025; **Figure 2B**). Identical results were found following right mIPS TMS: MT, DR and DIRVE increased only for movements performed towards contralateral targets (**Figure 2A**) when TMS was applied 160–100 ms before movement onset (all t(260) > 2.54, all p < 0.012). There was no significant main effect of DISPLAY (Target-ON vs. Target-OFF condition) nor DELAY × DISPLAY effect on these movement parameters (DISPLAY: all F(1,260) < 2.0, all p > 0.15; DELAY × DISPLAY: all F(12,260) < 1.6, all p > 0.1). Left or right PMd virtual lesions did not affect MT, DR and DIRVE (**Figure 2A**), as shown by an absence of significant main effect of DELAY or interaction between DELAY and HEMIFIELD on these parameters (all

mIPS and 100–40 ms for left PMd. The ellipses illustrate the 95% confidence interval of endpoint locations. The long axis of ellipses is shown to highlight the direction of the main component of endpoint variability. The black triangles represent DIRVE (as in Figure 2), extrapolated up to the target location (purple

p > 0.2). A global analysis including all TMS sites confirmed a significant SITE × HEMIFIELD × DELAY interaction (F(12,520) = 2.56, p = 0.0027), with a planned comparison confirming that the difference between the no-TMS condition and the 160–100 ms bins was significantly higher for both mIPS sites than for both PMd sites (t(520) = 4.03, p < 0.0001).

the targets, irrespective of their location in the right or left hemifield; this effect is illustrative of an inappropriate amplitude programing. (A) Representative subject, each dot represents an individual trial. (B) Average ellipses across all subjects (n = 6). The dot represents the center of each average ellipse.

# Effects of TMS on Movement Acceleration and Velocity

We found that only TMS applied over left PMd affected the acceleration and velocity peaks; TMS applied over right PMd and over either mIPS had no effect on these parameters. ANOVA-RM showed a significant main effect of DELAY for the acceleration and velocity peaks only when TMS was applied over left PMd (F(12,260) = 9.00, p < 0.001 and F(12,260) = 28.99, p < 0.0001, respectively; **Figure 3A**). Planned comparisons revealed that applying TMS over the left PMd 100–40 ms before movement onset led to an overall increase in the acceleration and velocity peaks (all t(260) > 3, all p < 0.0027, **Figure 3B**); this effect was observed for all target locations (main effect of HEMIFIELD and DELAY × HEMIFIELD interaction: all p > 0.2). TMS applied over left or right mIPS did not affect the peak of velocity or acceleration as denoted by a lack of main effect of DELAY and of interaction between DELAY and HEMIFIELD (all p > 0.18) on these parameters. A global analysis on all TMS sites confirmed a significant SITE × DELAY interaction for both velocity and acceleration peaks (F(36,1040) > 3.29, p < 0.0001), and the planned comparison showed that the difference between the no-TMS condition and the bins between 100 and 40 ms was significantly larger for left PMd than for all other stimulation sites (t(1144) = 2, p = 0.0455). There was no main effect of DISPLAY (Target-ON vs. Target-OFF condition) on acceleration and velocity peaks (F(1,260) < 2.0, p > 0.16).

In the no-TMS condition, linear regressions between acceleration or velocity peaks and target eccentricity showed the typical relationship between the peaks of movement derivatives and movement amplitude (see **Figure 3A**; Acceleration: Intercept = 0.83 × 103 , Slope = 5.32, p < 0.001; Velocity: Intercept = 105.15, Slope = 4.304, p < 0.001). When TMS was applied over the left PMd 100–40 ms before movement onset, the intercept of the linear regression increased significantly (acceleration: Intercept = 1.34 × 103 , p < 0.001; velocity: Intercept = 147.28, p < 0.001, **Figure 3A**), mainly due to the increased peak of movement derivatives after left PMd virtual lesions. This effect was also found irrespective of target location (HEMIFIELD main effect or SITE × HEMIFIELD interaction: all F < 1) and was comparable for both target display conditions ( DISPLAY main effect or SITE × DISPLAY interaction: all F < 1). In contrast, the slope of the regression line relationship between peak derivatives and target eccentricity was not significantly affected (Acceleration: Slope = 5.902; Velocity: Slope = 4.523, all p > 0.05; **Figure 3A**).

# Effects of TMS on Movement Endpoints

The Target-OFF condition allowed us to separate the effect of TMS on movement planning from the online corrective mechanisms (Prablanc et al., 2003). Indeed in the target-OFF condition, hand movements started well after the target was switched off, which prevented the hand trajectory to be updated based on visual feedback about target location, thus mostly relying on the initial movement plan. The endpoint constant error increased following left PMd virtual lesions only when TMS was delivered 100–40 ms before movement onset (main effect of DELAY : F(12,130) = 21, p < 0.0001, planned comparisons: all t(130) > 1.86, p < 0.0001), irrespective of target location ( HEMIFIELD main effect and DELAY × HEMIFIELD interaction, both F < 1.2, p > 0.3). **Figure 4** shows that following left PMd virtual lesions the step-tracking movements systematically overshot the target whereas the endpoint constant error was not affected by TMS applied over the right PMd nor over left mIPS or right mIPS (not shown). A global ANOVA-RM performed on all TMS sites showed a significant SITE × DELAY interaction (F(36,520) = 6.84, p < 0.0001). A planned comparison confirmed that the difference between the no-TMS condition and the 100–40 ms bins was significantly larger for left PMd than for the other stimulation sites (t(1144) = 4.85, p < 0.0001).

The endpoint variable error was affected when TMS was applied over left mIPS, right mIPS (not shown) or left PMd (all F(12,130) > 28, p < 0.0001; see **Figure 4**). Following left or right mIPS TMS, the endpoint variable errors increased when TMS was applied 160–100 ms before movement onset and for movements directed towards contralateral targets (DELAY × HEMIFIELD interaction: F(12,130) > 29, both p < 0.0001, all planned comparisons, t(130) > 10, p < 0.0001). Importantly, the shape of the isodensity ellipsoid (ratio between the lengths of long and short axes) was different for both left and right mIPS TMS conditions when compared to controls (both t(5) > 5.24, all p < 0.025). For both mIPS TMS conditions, the axis that explains the largest endpoint variability was nearly orthogonal (85 ± 17◦ , mean ± SD, n = 6) to the optimal movement direction (**Figure 4**). This peculiar ellipsoid shape was found only when TMS was delivered 160–100 ms before the onset of movements directed towards contralateral targets (all p < 0.043).

For left PMd virtual lesions, the endpoint variable error increased only when TMS was applied 100–40 ms before movement onset and for movements towards all targets (F(12,130) = 55.64, p < 0.0001; planned comparisons: all t(130) > 10, all p < 0.0001). In contrast to results gathered for mIPS, TMS applied over left PMd led to ellipsoid shapes that extended along the target direction (all t(5) > 6.76, all p < 0.004), with their long axis roughly aligned with the optimal movement path to reach the target (**Figure 4**, 3 ± 12◦ , mean ± SD, n = 6). Finally, a global ANOVA-RM on endpoint variable error confirmed a significant SITE × HEMIFIELD × DELAY effect (F(36,520) = 17.01, p < 0.0001). We performed a planned comparison highlighting the specific mIPS effect, comparing the no-TMS vs. 160–100 ms bins difference between mIPS and PMd (t(520) = 3.09, p = 0.0021) and another comparison focused on the specific effect obtained on left PMD (no-TMS vs. 100–40 ms bins, left PMD vs. other areas: t(520) = 10.19, p < 0.0001).

# Discussion

The present study indicates that direction and amplitude coding of goal-directed movements is performed by two distinct areas belonging to the dorsomedial ''reaching'' pathway, with direction being processed, first, in the posterior parietal cortex (mIPS) and amplitude being implemented later in the premotor cortex (PMd). In addition, we found that both mIPS are involved in coding the direction of right hand movements when performed in the contralateral hemifield, whereas left PMd processes the amplitude of all right hand displacements, whatever the location of the target, in the right or left hemifield. These results shed new light onto the cascade of visuo-motor transformations performed in the dorsomedial pathway of the prehension circuit.

The finding that both mIPS process the direction of goaldirected movements performed towards contralateral targets during movement preparation corroborates and extends our previous study (Davare et al., 2012), by showing that, when no visual feed-back was available during the task performance (Target-OFF condition), the movement endpoint distribution was biased along a direction orthogonal to the optimal movement path, and again only for movements directed towards contralateral targets and within a precise time window (160–100 ms) before movement onset. In our previous study, a series of control studies allowed us to conclude that the effect of mIPS TMS on movement direction could not be explained by an incorrect target spatial representation but was due to an inaccurate computation of the reach ''motor'' vector, or more precisely of the direction of this vector since our previous, and current, results have failed to reveal any evidence that mIPS is involved in coding the norm (i.e., amplitude) of this movement vector (Davare et al., 2012). Importantly, because we were able to demonstrate a deficit in coding the movement amplitude when TMS was applied over PMd (see below), the negative results we obtained following mIPS TMS do not results from a lack of accuracy or sensitivity of the measurements and/or analyses we performed.

As previously mentioned in the Introduction, the contribution of the PPC to movement direction coding has already been suggested in humans, in particular by using fMRI adaptation protocols (Fabbri et al., 2010). In this study, the same movement was repeated several times in one particular direction, followed by a test trial consisting of a movement executed in a different direction. The rationale of this approach is that, if the investigated area contains a directionally tuned cell population, its activation during the test trial should be proportional to the angular difference between the adapted and tested directions (Fabbri et al., 2010). Such an approach has allowed the authors to identify an extensive network of cortical areas sensitive to the direction of reaching movements, namely bilateral PMd, mIPS, aIPS and PRR (Fabbri et al., 2010), but also SMA and anterior precuneus (Fabbri et al., 2012); additionally, an interaction between reach direction and grip type has been found in a large number of areas belonging to the dorsolateral ''grasping'' circuit, including PMv (Fabbri et al., 2014). The large number of areas that this technique has revealed as being directionally sensitive, together with the fact that eye movements and/or attention allocation might have influenced these results, raises the issue of the causal role of all these areas in movement direction processing. Another possible drawback of these studies is that they did not try to disentangle movement direction from the other parameters that systematically covary with it, such as kinematics, EMG activity, or pattern of joint rotations. In any case, a modulation of the BOLD signal with movement direction alone cannot be viewed as evidence for a causal role in coding this parameter, as proved by the discrepancy between, for example, the lack of evidence for direction coding in PMd (current study) and the results of these fMRI adaptation studies, all showing consistently that this area contains directionally tuned cell populations.

Surprisingly, a lot fewer studies have investigated the role of cortical areas in coding movement amplitude. In monkeys, cells coding for amplitude have been mainly reported in PMd, but it has been repeatedly shown that most PMd cells encode both direction and amplitude of reaching movements (Kurata, 1993), in a sequential order, with movement direction being coded first, then followed by amplitude (Fu et al., 1995). The scarcity (around 2–4%) of cells coding only for movement amplitude in PMd has been confirmed by the group of Kalaska, supporting the view that most PMd cells in monkeys have an activity serially related to both direction and amplitude, during the performance of an instructed-delay reaching task (Messier and Kalaska, 2000). More recently, it has been shown in the monkey that the activity of most cells in V6A is also modulated by both depth and direction during reaching (Hadjidimitrakis et al., 2014), in contrast with the conclusion of the only study showing the existence of distinct cell populations coding the distance, azimuth and elevation in area 5 in monkey PPC (Lacquaniti et al., 1995).

Likewise, in humans, only very few studies have directly explored the coding of movement amplitude, possibly because of the even larger number of movement parameters that covary with amplitude (Messier and Kalaska, 2000). Recently, Fabbri and collaborators investigated the sensitivity to movement amplitude in those parietal and frontal regions already known to be tuned to movement direction (Fabbri et al., 2012). This study demonstrated that all PPC areas directionally tuned for direction (IPL, aIPS, pIPS and SPOC) are also sensitive to movement amplitude, suggesting they contain cell populations tuned to specific combinations of direction and amplitude. In addition these authors reported that the frontal areas, including PMd, PMv and SMA, show a partial transfer of adaptation to movement direction from the large to the small movement amplitude, but not the opposite, suggesting that, although this result remains puzzling, the amplitude is somehow processed by frontal areas, but in a different way than in the PPC areas (Fabbri et al., 2012).

The present study fails to find any evidence that mIPS is causally involved in amplitude coding, although of course we cannot exclude that another parietal area belonging to the ''reaching'' cortical circuit (Vesia and Crawford, 2012) codes movement amplitude. This finding corroborates the conclusion of our earlier TMS study showing no evidence for amplitude coding in mIPS (Davare et al., 2012). However, because this earlier conclusion was based on indirect evidence gathered by analyzing velocity and acceleration peaks, it remained questionable. To address this issue, in the current study, we tested an additional open-loop condition in which the target was flashed only for 200 ms, allowing us to prove that the distribution of endpoint errors after mIPS TMS was compatible with errors in coding direction, and not compatible with an error in movement amplitude, in contrast to what we found for PMd TMS.

From a more theoretical perspective, the current study reopens the long-lasting debate about a separate coding for amplitude and direction, a view mainly supported by behavioral studies (Desmurget et al., 1998), but for which neural evidence was still lacking, at least at the cortical level. At a behavioral level, a series of findings concur in suggesting independence of treatment for amplitude and direction: RT is decreased by prior information about either the direction or the distance of the target with respect to the hand (Rosenbaum, 1980; Bock and Arnold, 1992; Desmurget et al., 2004); rotation and gain learning occur at different paces and have different patterns of generalization (Pine et al., 1996; Krakauer et al., 2000; Vindras and Viviani, 2002); specification of movement amplitude and direction follow different time courses (Favilla et al., 1989; Ghez et al., 1997); variability and systematic biases of direction and amplitude errors are independent (Gordon et al., 1994; Messier and Kalaska, 1999; Vindras et al., 2005). The current TMS study demonstrates for the first time that such an independent coding for direction and amplitude exists at the cortical level. This finding echoes the results of previous studies that have linked amplitude coding with the basal ganglia (BG) network (Desmurget et al., 2003, 2004; Desmurget and Turner, 2008). Indeed, BG activity is known to modulate neural response in PMd (Grafton et al., 2006). It is thus tempting to speculate that BG inputs mediate the influence of PMd on movement amplitude (or any covariate of this parameter; e.g., velocity, acceleration, force). This view is compatible with recent evidence that the BG modulates movement performance according to non-motor motivational factors (Mazzoni et al., 2007; Turner and Desmurget, 2010; Baraduc et al., 2013). It is also interesting to note that the BG network has been involved in coding force amplitude during grasping movements, likely via BG connections to the dorsolateral grasping circuit including AIP and PMv (Wasson et al., 2010). This highlights that the BG are a key node for coding the amplitude not only for reaching movements via interactions with the dorsomedial circuit but also for force scaling via interactions with the dorsolateral grasping circuit (see Prodoehl et al., 2009 for review). In addition, to this point, and in agreement with electrophysiological experiments performed in monkeys (Fu et al., 1995; Messier and Kalaska, 2000; Hadjidimitrakis et al., 2014), our results also confirm that the amplitude is processed later than the direction information during reach movement preparation, suggesting that these two parameters are processed, or at least implemented in a serial order in the motor-related cortical areas. We can speculate that a serial encoding of direction and amplitude is likely to reflect a motor control strategy leading to smoother movement generation. First selecting a specific agonist muscle group (to reach a particular direction in space) before determining the exact amount of muscle activity required to reach a given distance seems a more cost-saving strategy. Interestingly, the current study also suggests that the processing of motor intention signals evolves along the parieto-frontal circuit: whereas the left or right mIPS only encodes preparatory signals for movements directed towards targets located in the contralateral hemifield,

# References

the coding in PMd appears more closely linked to the effector i.e., the contralateral hand, irrespective of the target location. Nevertheless, an additional study in which the left and right hands are systematically tested will be necessary to substantiate this conclusion.

To summarize, the present study provides, for the first time, evidence for a double-dissociation between direction and amplitude coding of reaching movements within the dorsomedial reaching circuit in humans. It is noteworthy that another candidate area for playing a causal role in encoding amplitude within the human dorsomedial circuit could be the human homolog of V6A (Pitzalis et al., 2013). Since Ciavarro et al. (2013) have found endpoint amplitude errors in reaching movements when TMS was applied over that area. Interestingly this effect seemed related more to a disruption of the visuospatial target representation rather than to the motor representation of target amplitude such as following PMd TMS. Further studies are required to substantiate the existence of a visuomotor gradient of amplitude encoding in parieto-frontal networks. Additional TMS experiments are also needed to investigate the possible interactions between the dorsomedial reaching and dorsolateral grasping circuits because, so far, these two components of prehension movements remain frequently investigated by using separate experimental paradigms.

# Acknowledgments

This work was performed at the Institute of Neuroscience (IoNS) of the Université catholique de Louvain (Brussels, Belgium); it was supported by grants to EO from the ARC (Actions de Recherche Concertées, Communauté Française de Belgique), from the Fondation Médicale Reine Elisabeth (FMRE) and from the Fonds de la Recherche Scientifique (FNRS–FDP). MD is funded by a BBSRC David Phillips fellowship (UK), the Royal Society (UK) and a FWO Odysseus project (Fonds Wetenschappelijk Onderzoek, Belgium). AZ is a Senior Research Associate supported by INNOVIRIS.

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Davare, Zénon, Desmurget and Olivier. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Corrigendum: The development of trunk control and its relation to reaching in infancy: a longitudinal study

Jaya Rachwani 1 \*, Victor Santamaria1 , Sandra L. Saavedra2 and Marjorie H. Woollacott 1

*1 Department of Human Physiology and Institute of Neuroscience, University of Oregon, Eugene, OR, USA, 2 Department of Rehabilitation Sciences, University of Hartford, West Hartford, CT, USA*

Keywords: infant development, independent sitting, posture, trunk control, reaching, EMG

# **A corrigendum on**

**The development of trunk control and its relation to reaching in infancy: a longitudinal study** by Rachwani, J., Santamaria, V., Saavedra, S. L., and Woollacott, M. H. (2015). Front. Hum. Neurosci. 9:94. doi: 10.3389/fnhum.2015.00094

**Figure 4** of the article by Rachwani et al. (2015) contained a minor error, which we hereby rectify. In the original figure, the graph displaying the number of movement units across sitting development is incorrect (bottom graph on the left column). We therefore re-submit **Figure 4** with the correct graph. We sincerely apologize for the inconvenience.

Edited and reviewed by: *Gavin Buckingham, Heriot-Watt University, UK*

> \*Correspondence: *Jaya Rachwani<EMAIL_ADDRESS>
Received: *27 June 2015* Accepted: *01 July 2015* Published: *21 July 2015*

#### Citation:

*Rachwani J, Santamaria V, Saavedra SL and Woollacott MH (2015) Corrigendum: The development of trunk control and its relation to reaching in infancy: a longitudinal study. Front. Hum. Neurosci. 9:406. doi: 10.3389/fnhum.2015.00406* Acknowledgments

This work was supported by the National Institutes of Health Grant 1R01HD062745-01, MW, principal investigator, and by the Eugene Evonuk Memorial Graduate Fellowship for JR.

# References

Rachwani, J., Santamaria, V., Saavedra, S. L., and Woollacott, M. H. (2015). The development of trunk control and its relation to reaching in infancy: a longitudinal study. Front. Hum. Neurosci. 9:94. doi: 10.3389/fnhum.2015.00094

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Rachwani, Santamaria, Saavedra and Woollacott. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Muscle co-activity tuning in Parkinsonian hand movement: disease-specific changes at behavioral and cerebral level

A. M. M. van der Stouwe1 \*, C. M. Toxopeus 1 , B. M. de Jong1 , P. Yavuz 1 , G. Valsan2 , B. A. Conway 2 , K. L. Leenders 1 and N. M. Maurits 1

*1 Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands, 2 Biomedical Engineering, University of Strathclyde, Glasgow, UK*

#### Edited by:

*Simona Monaco, University of Trento, Italy*

#### Reviewed by:

*Krystal Lynn Parker, University of Iowa, USA Elisabeth Huis in 't Veld, Maastricht University, Netherlands Ahmed Mostafa, York University, Canada*

#### \*Correspondence:

*A. M. M. van der Stouwe, Department of Neurology, University Medical Center Groningen, Hanzeplein 1, 9700 RB, Groningen, Netherlands<EMAIL_ADDRESS>
> Received: *06 October 2014* Accepted: *17 July 2015* Published: *05 August 2015*

#### Citation: