Significance
Accurate
motor control depends on maps of the body in the brain, called the body
schema. Disorders of the body schema cause motor deficits. Although we
often execute actions with different motor systems such as the eye and
hand, how the body schema operates during such actions is unknown. In
this study, participants simultaneously directed eye and hand movements
to the same body part. These two movements were found to be guided by
different body maps. This finding demonstrates multiple motor
system–specific representations of the body schema, suggesting that the
choice of motor system toward one’s body can determine which of the
brain’s body maps is observed. This may offer a new way to visualize
patients’ body schema.
Abstract
Purposeful
motor actions depend on the brain’s representation of the body, called
the body schema, and disorders of the body schema have been reported to
show motor deficits. The body schema has been assumed for almost a
century to be a common body representation supporting all types of motor
actions, and previous studies have considered only a single motor
action. Although we often execute multiple motor actions, how the body
schema operates during such actions is unknown. To address this issue, I
developed a technique to measure the body schema during multiple motor
actions. Participants made simultaneous eye and reach movements to the
same location of 10 landmarks on their hand. By analyzing the internal
configuration of the locations of these points for each of the eye and
reach movements, I produced maps of the mental representation of hand
shape. Despite these two movements being simultaneously directed to the
same bodily location, the resulting hand map (i.e., a part of the body
schema) was much more distorted for reach movements than for eye
movements. Furthermore, the weighting of visual and proprioceptive
bodily cues to build up this part of the body schema differed for each
effector. These results demonstrate that the body schema is organized as
multiple effector-specific body representations. I propose that the
choice of effector toward one’s body can determine which body
representation in the brain is observed and that this visualization
approach may offer a new way to understand patients’ body schema.
Since a classic work of Head and Holmes in the early 1900s (1),
it has been widely accepted that purposeful motor actions rely on a
spatial representation of the body in the brain, called the body schema (2⇓⇓⇓–6).
Without the body schema, we would be unable to accurately and safely
control our body parts. Indeed, impairment of the body schema leads to a
variety of disorders ranging from motor dysfunction to delusions that
the affected body part belongs to another person (7, 8).
The classic notion of the body schema had long been used to describe a
representation of the location of body parts in external space derived
from information about body posture specified by afferent signals (i.e.,
proprioceptive signals) and from information about efferent copies of
motor commands (9⇓–11).
However, current views on the body schema suggest that the ability to
localize body parts in external space requires not only afferent and
efferent information but also stored information about the body’s metric
properties, such as body part size and shape, because no afferent and
efferent signals directly inform the brain about the metric properties
of body parts (12, 13). This stored body metric information is provided by an implicit body representation in the brain (12). Such a metric representation serves not only perception but also action (13).
Based on the current views, the body schema can be defined as a
representation of the location of body parts in space that is
constructed by combining afferent and efferent information with stored
information about the body metrics. Moreover, the body schema has been
suggested to be involved in a global adjustment through afferent signals
from various body parts (14).
Indeed, afferent signals coming from all body parts, such as the eye
and foot, function together in modulating motor control (15, 16).
Taken together, the body schema contains the spatial configuration of
the body used for the guidance of action and functions in a global way.
The
accumulated research literature indicates that the body schema is
constructed based on bodily signals from multiple sensory sources such
as vision and proprioception (5, 6, 17⇓⇓–20).
The spatial and temporal congruence of multisensory bodily signals from
one’s body parts generates a single estimate of the body’s location (21⇓⇓⇓⇓–26). This is referred to as multisensory integration (27⇓–29).
The process underlying multisensory integration determines how much a
given sensory modality contributes to the final estimate relative to a
different sensory modality in a statistically optimal way (30⇓–32).
Supporting this multisensory integration model, many demonstrations
show that the weighting of each sensory source of bodily signals varies
with its reliability (33⇓–35).
The importance of multisensory integration for the body schema is
illustrated in a cross-modal effect in which the position sense of one’s
hand is influenced by vision of an artificial hand (36).
The
body schema is normally assumed to be a common body representation
supporting all types of motor actions, and numerous studies have
typically used a single motor task to explore aspects of the body schema
(17, 19, 37⇓⇓–40).
However, we often take multiple simultaneous actions with different
effectors, such as the eye and hand, in our daily lives. In recent
years, growing evidence has indicated that, when multiple actions are
performed with different types of effectors, the action for each
effector is guided by a different spatial representation of the outside
world in the brain, such as motor responses to visual motion (41), localization of moving objects (42), and allocation of spatial attention (43, 44).
These results suggest that there are multiple spatial maps of the
outside world in the brain and that each of these spatial maps guides a
different effector. This is surprising because it is thought that the
spatial maps are represented in a common reference frame (i.e.,
eye-centered coordinates) regardless of whether the action is an eye
movement or a reach movement (45⇓⇓⇓⇓–50).
However, how the body schema operates during multiple actions with
different effectors is unknown. Here, I systematically investigate the
body schema mediating the spatial configuration of the hand when
simultaneous eye and reach movements are made to that hand. The present
results demonstrate that the body schema is organized as multiple
effector-specific representations of the body.
The body
schema contributes to the planning of motor actions toward one’s body
parts. It seems natural that the planning of motor actions toward one’s
body parts would use the same bodily information that allows us to
perceive those body parts. However, bodily information has been
suggested to undergo independent processing when used for ballistic
motor responses as opposed to perceptual judgments (19, 51).
Based on this finding, I developed a technique to measure the body
schema during multiple motor actions by having participants make
ballistic motor responses toward landmarks on a hand. The distance
between the judged locations of two adjacent landmarks on the hand
(e.g., the tip and knuckle of a single finger) depends only on the
represented length of the body segment connecting them. Other sources of
error, such as misjudgments of the knuckle angle, affect localization
error for a single landmark (e.g., the distance between actual and
judged locations at the tip of the finger) but preserve the relative
positions of the landmarks. Thus, the body schema was isolated and
measured by having participants make simultaneous eye and reach
movements toward the location of 10 landmarks on their hand. By
comparing the landing positions of eye and reach at these landmarks
regardless of their true positions (Fig. 1C),
I analyzed the internal spatial configuration of the hand
representations for the eye and reach landing positions. The distances
between these motor judgments for each effector are different from
either constant or variable error of localization and allow me to
estimate the internal structural representation of the body schema of
the hand. This behavioral measurement of the body schema was combined
with the cross-modal effect of a computer-generated hand on
proprioceptive judgments (36).
By using this effect, I was able to investigate whether the weighting
of visual and proprioceptive cues to the location of the landmarks
differed between eye and reach landing positions.
Fig. 1. Experimental setups. (A)
Participants binocularly viewed the visual stimuli presented through an
HMD with an eye tracker and placed their right hand on a table. A
transparent board was placed over the participant’s right hand. A
position sensor was attached to the tip of the participant’s left index
finger. (B) Sample result of eye and reach movements for one participant. (C)
The represented length of a finger was determined by comparing the
distance between the judged positions of the fingertip and knuckle
without respect to their true positions. This distance was then compared
with the true finger length. Thus, the represented hand shape was
assessed by examining the internal configuration of the localizations of
multiple landmarks. (D–G) Visual stimuli. (D) A CG
hand was presented on a gray surface in the virtual environment, and the
CG hand was placed so that it overlapped the participant’s unseen right
hand (experiment 1). (E) The CG hand and the participant’s
unseen right hand were rotated 90° so that their fingers pointed to the
left (experiment 2). (F) The CG hand was not presented in the
virtual environment, but the participant’s unseen right hand was placed
on the gray surface as in experiment 1 (experiment 3). (G) A
wood-like rectangle was presented on the gray surface instead of the CG
hand, and the rectangle was placed so that it overlapped the
participant’s unseen right hand, as in experiment 1 (experiment 4).
Results
Experiment 1: With Vision of a Hand.
Participants
placed their right hand palm down under a transparent board and wore a
head-mounted display (HMD) that displayed visual stimuli in stereoscopic
three dimensions (3D) (Fig. 1A). The HMD showed a realistic life-sized computer graphics (CG) hand (Fig. 1D).
The CG hand overlapped the participant’s unseen right hand in the
virtual environment and was configured similarly to the participant’s
actual hand. While participants viewed the CG hand, they made
simultaneous eye and reach (left forefinger) movements toward the
location of 10 landmarks on their hidden right hand (the knuckles and
tips of each finger). In such coordinated eye and reach movements, eye
movement onset preceded hand movement onset (Fig. 1B; see SI Appendix, Fig. S1 for more details), as confirmed by previous studies (52⇓–54). Based on a previous study (12),
comparing the eye or reach landing position of different landmarks
allowed me to build a spatial map of the mental representation of hand
shape (i.e., hand map), which could be then compared with the actual
hand shape. Fig. 1C
shows an example: The targeted locations of the index fingertip and
knuckle were used to calculate the represented index finger length
(dotted red line) for comparison with its actual length (dotted black
line). Before and after each block, a picture was taken to record the
actual hand shape and to ensure that the hand had not moved.
To
assess finger length, the distance between the average landing
positions of each knuckle and fingertip was calculated from the thumb to
the little finger. These distances were then averaged to estimate
overall finger length. The estimated overall finger length
underestimated the actual length for both eye and reach landing
positions [t (11) = −5.75, P < 0.001, and t (11) = −7.49, P < 0.0001, respectively; Fig. 2A; see SI Appendix, Fig. S2 for more details], which is consistent with the results of the previous study (12).
Intriguingly, the overall underestimation of finger length
significantly differed between the eye and reach landing positions
[paired t test, t (11) = 4.39, P < 0.01; Fig. 2A].
To
assess hand width, the distance between pairs of adjacent knuckles was
calculated as for finger length. These distances were then averaged to
estimate overall knuckle spacing. In contrast to the overall
underestimation of finger length, strong overall overestimation of
knuckle spacing was observed [t (11) = 4.59, P < 0.001, for eye landing positions and t (11) = 6.62, P < 0.0001, for reach landing positions; Fig. 2B; see SI Appendix, Fig. S2 for more details], which is consistent with the results of previous work (12). Overall overestimation of knuckle spacing significantly differed between the eye and reach landing positions [paired t test, t (11) = −3.14, P < 0.01; Fig. 2B].
To assess the shape of the hand map in detail, generalized Procrustes superimposition (GPS) (55)
was used to compare the actual configuration of landmarks from each
participant’s right hand with the internal representation based on eye
and reach landing positions (Fig. 3 A and B). GPS removes differences in location, rotation, and scale and thereby highlights differences in shape (55, 56).
Analysis of these data indicated significant differences in mean shape
between the actual hand and the resulting hand map for eye landing
positions [Bonferroni-corrected Goodall’s F test: Goodall’s F(16, 352) = 5.50, P < 0.0001; Fig. 3A; see SI Appendix, SI Materials and Methods for further details] and reach landing positions [Goodall’s F(16, 352) = 17.75, P < 0.0001; Fig. 3B].
Although the shape of the hand map was distorted for both eye and reach
landing positions, the shape was more similar to the actual shape of
the hand in eye landing positions than in reach landing positions. The
mean shape of the hand map significantly differed between eye and reach
landing positions [Goodall’s F(16, 352) = 5.56, P < 0.0001].
One
might argue that the difference in measured positions between eye and
reach movements might reflect differential times of motor actions,
rather than the difference in body representations, because eye and
reach movements do not have the same speed or the same landing time (see
SI Appendix, SI Results for details; SI Appendix, Fig. S3).
However, this is unlikely. The initial movement end point was used to
calculate the landing positions of the movement for each of the eye and
reach movements. Moreover, I confirmed that there were significant
differences in the overestimation of finger lengths and knuckle spacings
between eye and reach, even though reach movements had latencies
comparable to those of eye movements (see SI Appendix, SI Results for details; SI Appendix, Fig. S4).
Thus, the difference in measured positions between eye and reach
reflects a difference in the spatial representations of the body rather
than the differential starting times of motor actions.
Experiment 2: Rotated Posture.
The
results of experiment 1 could potentially reflect either a
foreshortening of perspective in the near–far axis or motor biases in
trunk-based coordinates for motor responses. To address these issues, a
second experiment was conducted in which participants’ hands and the CG
hand were rotated 90° counterclockwise relative to their trunk so that
the fingers were pointing toward the left (Fig. 1E).
If any effects independent of the hand map reproduce the results of
experiment 1, these effects should be reversed in the rotated posture
relative to the original posture used in experiment 1 (i.e., the fingers
pointing away from the trunk): extended finger lengths and narrow hand
widths. However, experiment 2 showed almost identical estimated finger
lengths and hand widths in the rotated posture to those in the original
posture. Finger length was underestimated overall even in the rotated
posture [t (11) = −7.08, P < 0.0001, for eye landing positions and t (11) = −7.57, P < 0.0001, for reach landing positions; Fig. 2A; see SI Appendix, Fig. S2 for more details] and significantly differed between eye and reach landing positions [paired t test, t (11) = 4.64, P < 0.001; Fig. 2A]. Knuckle spacing was overestimated overall [t (11) = 3.92, P < 0.01, for eye landing positions and t (11) = 5.82, P < 0.001, for reach landing positions; Fig. 2B; see SI Appendix, Fig. S2 for more details] and significantly differed between eye and reach landing positions [paired t test, t (11) = −2.41, P < 0.05; Fig. 2B].
Analysis of GPS data revealed significant differences in the mean shape
between the actual hand and the resulting hand map for eye landing
positions [Goodall’s F(16, 352) = 4.57, P < 0.0001; Fig. 3C] and reach landing positions [Goodall’s F(16, 352) = 16.88, P < 0.0001; Fig. 3D].
As in experiment 1, the shape of the hand map was more similar to the
actual shape of the hand in eye landing positions than in reach landing
positions. The mean shape of the hand map significantly differed between
eye and reach landing positions [Goodall’s F(16, 352) = 5.97, P
< 0.0001]. Thus, these results demonstrate that the effects observed
in the present study reflect the hand map in the brain rather than
biases in head- or trunk-based coordinates for motor responses or in a
foreshortening of perspective in the near–far axis.
Experiment 3: Without Vision of a Hand.
To
investigate whether viewing a hand affected the shape of the hand map
in experiments 1 and 2, the CG hand was not presented to participants
and they were asked to make concurrent eye and reach movements to the
location of 10 landmarks on their hidden right hand (Fig. 1F). Under these conditions, finger length was underestimated overall [t (17) = −17.36, P < 0.0001, for eye landing positions and t (17) = −16.06, P < 0.0001, for reach landing positions; Fig. 2A; see SI Appendix, Fig. S2 for more details] but was not significantly different between eye and reach landing positions [paired t test, t (17) = −0.30, P = 0.76; Fig. 2A]. Knuckle spacing was overestimated overall [t (17) = 15.13, P < 0.0001, for eye landing positions and t (17) = 8.13, P < 0.0001, for reach landing positions; Fig. 2B; see SI Appendix, Fig. S2 for more details] but was not significantly different between eye and reach landing positions [paired t test, t (17) = 0.058, P = 0.95; Fig. 2B].
Analysis of GPS data revealed significant differences in the mean shape
between the actual hand and the hand map for eye landing positions
[Goodall’s F(16, 544) = 47.28, P < 0.0001; Fig. 3E] and reach landing positions [Goodall’s F(16, 544) = 25.21, P < 0.0001; Fig. 3F].
However, there was no significant difference in the mean shape of the
hand map between eye and reach landing positions [Goodall’s F(16, 544) = 1.22, P
= 0.25]. These results indicate that hand viewing is required to
generate the difference in the shape of the hand map between eye and
reach landing positions.
Experiment 4: With a Wood-Like Rectangle.
To
investigate whether the difference in the shape of the hand map between
eye and reach movements is specific to vision of a hand, the CG hand
was visually replaced with a computer-generated wood-like rectangle that
spatially overlapped the participant’s unseen right hand, as in
experiment 1 (Fig. 1G). Finger length was underestimated overall [t (11) = −7.74, P < 0.0001, for eye landing positions and t (11) = −8.70, P < 0.0001, for reach landing positions; Fig. 2A; see SI Appendix, Fig. S2 for more details] but was not significantly different between eye and reach landing positions [paired t test, t (11) = 1.37, P = 0.20; Fig. 2A]. Knuckle spacing was overestimated overall [t (11) = 7.29, P < 0.0001, for eye landing positions and t (11) = 5.26, P < 0.001, for reach landing positions; Fig. 2B; see SI Appendix, Fig. S2 for more details] but was not significantly different between eye and reach landing positions [paired t test, t (11) = −0.48, P = 0.64; Fig. 2B].
Analysis of GPS data identified significant differences in the mean
shape between the actual hand and the hand map for eye landing positions
[Goodall’s F(16, 352) = 11.96, P < 0.0001; Fig. 3G] and reach landing positions [Goodall’s F(16, 352) = 18.59, P < 0.0001; Fig. 3H].
However, there was no significant difference in the mean shape of the
hand map between eye and reach landing positions [Goodall’s F(16, 352) = 1.29, P
= 0.20]. These results indicate that the difference in the shape of the
hand map between eye and reach movements is specific to vision of a
hand.
Finger Lengths and Knuckle Spacings for Eye and Reach Are Different in Relation to Perception.
Since separate somatosensory processes have been proposed for action and perception (51),
I investigated whether the finger lengths and knuckle spacings for eye
and reach are different from those for perception. Participants were
instructed to judge the location of landmarks on their invisible right
hand by moving a visual pointer in the virtual environment with a
trackball controlled by their left hand (perceptual localization task; SI Appendix, SI Materials and Methods).
In the “with CG hand” condition, the participants saw the CG hand, as
in experiment 1. In the “without CG hand” condition, they saw a gray
surface alone without the CG hand, as in experiment 3. I found that the
finger lengths and knuckle spacings for reach differed from those for
perception [Bonferroni-corrected paired t tests, t (11) = 5.23, P < 0.01, for finger lengths and t (11) = 2.71, P < 0.05, for knuckle spacings; Fig. 4 A and B; see SI Appendix, SI Materials and Methods and SI Results for details; SI Appendix, Fig. S5). In contrast, the finger lengths and knuckle spacings for eye were almost the same as those for perception [t (11) = 2.13, P = 0.11, for finger lengths and t (11) = 0.65, P = 0.99, for knuckle spacings; Fig. 4 A and B; see SI Appendix, SI Results for details; SI Appendix, Fig. S5]. Furthermore, participants answered questionnaire items to rate perceptual aspects of the CG hand (ownership rating task; see SI Appendix, SI Materials and Methods, Table S1, and SI Results for details; SI Appendix, Fig. S6) (25, 26, 35, 57).
I found that the overestimation of the finger lengths and knuckle
spacings was significantly correlated with the strength of sense of body
ownership over the CG hand for eye and percept but not for reach (Fig. 4 C–H; see SI Appendix, SI Materials and Methods and SI Results
for details). Note that it is suggested later that a different hand
representation is used for eye movements and perception (see Correlations across Individuals between Hand Maps).
Thus, these results point to a difference in the body map used to guide
reach movements as opposed to that used to guide saccadic eye
movements.
Hand Maps for Eye and Reach Are Different from the Conscious Body Image.
To
investigate whether the hand maps for eye and reach are dissociated
from a conscious body image, I used Napier’s shape index, which
quantifies the ratio of hand width to length (58) (template-matching task; SI Appendix, SI Materials and Methods).
I found differences in shape indices between the hand map for eye and
the conscious body image and between the hand map for reach and the
conscious body image [Bonferroni-corrected paired t tests, t (11) = 4.19, P < 0.01, for eye and t (11) = 5.55, P < 0.005, for reach; Fig. 5A; see SI Appendix, SI Results for details; SI Appendix, Fig. S7]. There was a significant difference in shape indices between eye and reach [t (11) = 3.38, P < 0.05; Fig. 5A]. Given that the hand maps for eye and reach were also different in relation to sense of body ownership as shown in Fig. 4 C–H, this sense of body ownership might involve the conscious body image (19, 59). However, I found no correlation between the shape and ownership indexes (SI Appendix, Fig. S8), implying that measures based on sense of body ownership may not be measures of body image.
Correlations across Individuals between Hand Maps.
To
determine whether a different hand representation is used between eye
and reach, I analyzed the correlation across individuals between the
shape indices, rather than the overestimation of finger lengths and
knuckle spacings, for different effectors. The shape index is a better
measure of the hand representation, because the overestimation of finger
lengths and knuckle spacings shows the extent of distortion but does
not represent the shape of the hand map itself. If the same hand
representation is used between different effectors, there should be a
correlation across individuals between the shape indices for these
effectors (60).
However, I found that there were no significant correlations between
eye and reach under both the “with CG hand” and “without CG hand”
conditions (rs = 0.16, n = 24, and P = 0.46 for the “with CG hand” condition and rs = 0.20, n = 30, and P = 0.30 for the “without CG hand” condition; see Fig. 5B and SI Appendix, Fig. S9 A–D and SI Results
for details). In particular, I found no significant correlation between
the shape indices for eye and reach movements without the CG hand (Fig. 5B), even though the average distortions of the hand map were similar between eye and reach without the CG hand (SI Appendix, Fig. S9C).
Thus, these results suggest that different hand representations are
used for eye and reach movements regardless of whether visual
information about the hand is available. On the other hand, there was a
significant correlation between the normal (experiment 1) and rotated
(experiment 2) postures for each of eye and reach (rs = 0.68, n = 12, and P < 0.05 for eye and rs = 0.81, n = 12, and P < 0.001 for reach; see SI Appendix, Fig. S9 E–H and SI Results
for details), suggesting that the same hand representation is used for
the normal and rotated postures in the same effector. Furthermore, I
found that there was no significant correlation between eye movements
and perception (rs = −0.28, n = 12, and P = 0.38; Fig. 5C; see SI Appendix, Fig. S9 I and J and SI Results
for details), suggesting that different hand representations are used
even for eye movements and perception. This implies that the distortions
of hand shape in eye movements are not indicative of distortions in the
perceptual body image.
Discussion
The
present study reveals that there are multiple representations of the
body schema for the same body part. The body schema has been assumed to
be a common body representation used to control all types of motor
actions for more than a century (17, 19, 37⇓⇓–40). Although many studies have suggested that the body schema is based on the process of multisensory integration (4⇓–6, 17⇓⇓–20),
I believe that the current systematic investigation of this process
during multiple motor actions with different effectors is unique. The
present results show that the body schema is organized as multiple
effector-specific body representations. These representations were
measured through proprioceptive judgments of body parts. These
proprioceptive judgments show remarkably large distortions of the
represented body shape, which are thought to reflect the characteristics
of the body schema (12, 13, 61).
I found that the pattern of the distortions differs between saccadic
eye movements and reach movements even when these two types of movements
are simultaneously directed to the same body part location. This
difference was observed when an artificial body part spatially
overlapped the participant’s invisible body part but not when the
artificial body part was replaced with a nonbody object. Moreover, the
distortions of the represented body shape were not correlated between
saccade and reach across individuals regardless of whether visual
information about the artificial body part was available. These results
provide clear evidence of a dissociation in body representations between
different types of effectors and that these body representations differ
in the weighting of visual and proprioceptive bodily cues.
What
might be the mechanisms underlying such a dissociation in body
representations between different types of effectors? Body
representations are the spatial model of the body that the brain
constructs based on the integration of information from multiple sensory
modalities such as vision and proprioception (20, 62). Such body models can provide an estimate of where one’s body parts are in space (12).
This estimate is generated by combining bodily cues in a way that the
weights change flexibly according to the uncertainty surrounding the
body’s location (31).
For example, when vision is heavily weighted, an estimate of where
one’s body part is in space relies more on vision than on
proprioception. Recent studies examining the integration of visual and
proprioceptive bodily information suggest that the weighting of each
bodily cue depends on its reliability (33, 35).
However, the present results demonstrate that, even though the same
visual and proprioceptive bodily cues are theoretically available for
saccade and reach, these cues are incorporated differently for the two
movements. Thus, this finding indicates that the weighting of vision and
proprioception differs for each effector even though the reliability of
each bodily cue is the same across the effectors being used.
The
present results suggest that there are two distinct body
representations for action. One body representation is used to guide
saccadic eye movements, whereas the other is used to guide reach
movements. Are these body representations different from a body
representation for perception? Separate somatosensory processes have
been proposed for perception and action (51).
For example, illusory displacement of the perceived location of a
participant’s hidden hand toward a rubber hand presented in front of the
participant, called proprioceptive drift, is reduced when tested with
reach movements rather than perception (19).
Consistent with this result, I found that the body representation for
reach differed from that for perception. In contrast, the body
representation for saccade was almost the same as that for perception.
Furthermore, I found that the magnitude of the distortion of the body
representation was significantly correlated with the strength of sense
of body ownership over the CG hand for saccade and percept but not for
reach. Although these results highlight the difference between the
saccade and reach systems, the results indicate that both saccade and
percept depend on a cross-modal effect of vision and proprioception on
body ownership. However, I found that the distortions of the represented
body shape were not correlated between saccade and percept across
individuals. These findings suggest that dissociated processing of
perception and action is a characteristic of both the saccade and reach
systems.
The present results indicate that dissociable
body representations are identified on motor tasks. Dissociation between
body schema (a body representation used to control bodily movements)
and body image (a body representation used to judge bodily properties)
is well established (1, 4, 9).
In the present study, participants controlled their saccade and reach
movements toward their body parts. To carry out such motor tasks, the
brain needs to have access to the body schema (1).
For that reason, the body representations for saccade and reach should
reflect the body schema. Indeed, I found that the body representations
for saccade and reach are different from a conscious body image. These
results suggest that the body representations for saccade and reach
reflect the body schema and that the body schema can be further
subdivided into at least two representations depending on the effector.
Which
brain areas are responsible for the body representations for saccade
and reach? Neuroimaging studies have suggested that the posterior
parietal cortex (PPC), the ventral premotor cortex (PMV), and the
extrastriate body area (EBA) are involved in the performance of motor
actions (63⇓–65).
The PPC contains distinct cortical areas, the intraparietal sulcus
(IPS) and the superior parietal lobe (SPL). The IPS and SPL are
selectively recruited during saccade and reach movements, respectively (63).
Like the PPC, the PMV also contains distinct cortical areas, the
saccade-evoking area (PMVe) and the reach-evoking area (PMVr) (64). Like the SPL and PMVr, the EBA also responds during reach movements (65). Interestingly, the IPS, SPL, EBA, and PMV have also been suggested to be involved in the body representations (66, 67). The IPS and PMV have been reported to encode the hand position by integrating visual and proprioceptive signals (66, 68). Moreover, activity in the PMV reflects individual differences in experienced artificial hand ownership (67, 69).
Unlike the IPS and PMV, the SPL and EBA have been found to encode
changes in proprioceptive hand position in the dark, although these
regions also responded to the position of a visible computer-generated
hand (65, 67).
These findings suggest that the contribution of vision and
proprioception to the hand representation is different between the
saccade-related IPS and PMVe and the reach-related SPL and EBA, although
this contribution seems to be similar between the PMVe and the PMVr.
This view is supported by the present results. Indeed, the present
results indicate that the estimate of the landmark positions on the hand
relies more heavily on proprioception for reach movements than for
saccade movements. Taken together, these findings suggest that the IPS
and PMVe may be responsible for the body representation accessed by the
saccade system and that the SPL and EBA may be responsible for the body
representation accessed by the reach system.
Although a
common spatial representation in the brain is often believed to guide
different types of effectors toward the same goal (49),
some evidence suggests that different effectors aiming for the same
goal can show different spatial representations. Visual motion
processing is carried out independently for manual and ocular following
responses to visual motion (41). The spatial localization of moving objects differs between eye and hand movements (42).
During the preparation of coordinated eye–hand movements, spatial
attention is allocated independently to the targets of both movements (43, 44).
These previous studies propose the view that spatial maps of the
outside world in the brain dissociate between different types of
effectors. The present findings support this view and demonstrate that
such dissociations are present not only with spatial maps of the outside
world but also with spatial maps of one’s body.
What
are the different roles of the body representations accessed by the
reach system and the saccade system? Distortions in hand representations
have been reported to extend to objects (70).
Interestingly, similar distortions to the hand representation were
found for the representation of manipulable objects such as a mobile
phone, but the pattern of distortions for nonmanipulable objects, such
as a cactus with spines, differed from that for manipulable objects.
This suggests that the pattern of distortions depends on the
availability of motor functions. The present study indicates that this
pattern of distortions also depends on the types of motor functions.
Motor functions differ between reach and saccade. The reach system would
allow us to interact with objects around us, whereas the saccade system
would allow us to take advantage of foveal vision to judge whether our
hands can safely interact with objects. I suggest that the body
representations accessed by the reach system might be involved in
facilitating body−object interactions, whereas those accessed by the
saccade system might be less involved in manipulability of objects.
A visual distortion in HMDs is well established (71).
However, this visual distortion in HMDs cannot explain the present
results. The current study showed that there is a highly distorted
representation of hand shape, shortened finger lengths, and wider hand
widths using an HMD. If these results are due to the effects of the HMD,
the results should be extended finger lengths and narrower hand widths
(i.e., reversed) when the participant’s hand and the CG hand are rotated
90° counterclockwise relative to their trunk (experiment 2). However,
experiment 2 showed that the estimated finger lengths and hand widths in
the rotated posture are almost identical to those in the original
posture shown in experiment 1. This suggests that the present results
are not due to the effects of the HMD itself. Nevertheless, because the
CG hand leads to a misestimation of depth perception in a virtual 3D
environment, it may be better to run a similar study using mixed reality
in which participants see their real hand. Future research is needed to
examine this topic.
The present study has implications
for methods designed to visualize the body representations of patients
with motor paralysis. The rapid population aging of many developed
countries, such as Japan, will sharply increase the number of patients
with motor paralysis resulting from motor dysfunction and stroke. To
overcome this issue, effective rehabilitation techniques need to be
developed for patients with motor paralysis. Visualization of the
patient’s body representations for action will help to build such an
effective rehabilitation technique. The present results suggest that the
choice of effectors toward one’s body can determine which body
representation in the brain is observed. This approach to the
visualization of multiple body representations might be useful for
understanding abnormal body schemas in patients with motor paralysis or
limb amputation.
More at link.
