Prediction of sensorimotor feedback -1-

Accepted manuscript
Imamizu, H. (2010). Prediction of sensorimotor feedback from efference copy of motor
commands: A review of behavioral and functional neuroimaging studies. Japanese
Psychological Research, 52(2), 107-120.
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Request for a reprint PDF to imamizu@gmail.com

Prediction of sensorimotor feedback from efference copy of motor
commands: A review of behavioral and functional neuroimaging studies
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Hirorshi IMAMIZU
National Institute of Information and Communications Technology (NICT) and
Advanced Telecommunications Research (ATR) Institute International

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Address for correspondence:
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Hiroshi Imamizu
Biological ICT Group
National Institute of Information and Communications Technology
2-2-2 Hikaridai, Seika-cyo, Soraku-gun, Kyoto 619-0288
Tel: +81 774 95 1220

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Fax: +81 774 95 1236
e-mail: imamizu@gmail.com

Prediction of sensorimotor feedback -2-

Prediction of sensorimotor feedback from efference copy of motor
commands: A review of behavioral and functional neuroimaging studies

Abstract5

Humans can rapidly and smoothly control their own bodies and external objects such as
tools. Because sensorimotor feedback of movements is inevitably delayed by manyfactors, including delay for transmission of motor commands from the brain to muscles
and time for processing of sensory information, many studies have suggested theexistence of mechanisms in the brain that can predict the precise motor commands

10

necessary for realizing intended motions and sensorimotor feedback from the motor
commands before movement. This article focuses on mechanisms for prediction of
feedback and summarizes how the predicted feedback can contribute to rapid, efficientand flexible control of movements and processing of sensory information. Possible
neural correlates for the prediction mechanisms and their contribution to higher-order

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cognitive functions are discussed.

Key words: movement control, sensorimotor feedback, forward model, functional
neuroimaging

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Running head: Prediction of sensorimotor feedbackPrediction of sensorimotor feedback -3-

Introduction
Skilled manipulation of objects, including our own bodies, relies on the brain learning
to control the object and predict the consequences of this control. The ability to learn
the relationships between actions and the resultant changes in states of external objects
5

is particularly important for rapid and smooth control of movements. It has beensuggested that such abilities are largely dependent on neural mechanisms that can modelor simulate the relationships between an action and its consequences before the action's
execution. Neural mechanisms that can mimic the input-output properties of controlled
objects are called internal models (Kawato, 1999; Kawato, Furukawa, & Suzuki, 1987;

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Wolpert, Ghahramani, & Jordan, 1995).
The central nervous system (CNS) is thought to use two forms of internal models:an inverse internal model and a forward internal model. In the context of arm reaching
movements (Fig. 1a), forward models transform efference copies of motor commands
into the resultant trajectory or sensorimotor feedback (Kawato, et al., 1987; Miall, Weir,

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Wolpert, & Stein, 1993; Wolpert, et al., 1995). Here, an efference copy is an internal
copy created with a motor command and sent to other brain regions. Inverse models
transform an intended motion of the arm into the motor commands used to realize themotion (Kawato, et al., 1987). Inverse and forward models can also contribute topredictive control of external objects such as tools. For example, skilled manipulation of20

a computer mouse requires the ability to predict how the cursor will move on the screen
if the mouse is moved in a particular direction (forward model: Fig. 1b) and how thePrediction of sensorimotor feedback -4-

mouse should be moved in order to move the cursor to a particular position on thescreen (inverse model).
In this article, I will focus on forward models for prediction of feedback andreview behavioral and functional imaging studies that clarify the contribution of
5

forward models to human behaviors. These studies demonstrate how predictedsensorimotor feedback helps flexible, rapid and precise control of movements as well asprocessing of sensory information.

Grip force - load force coupling using forward models

10When an object is held with the tips of the thumb and index finger at the sides, grip
force allows the development of frictional force to counteract the load force (Fig. 2a). In
a series of studies, Johansson and colleagues (Johansson & Cole, 1992; Johansson &Westling, 1984) have shown that when lifting objects or pulling on fixed loads grip
force is adjusted in parallel with changes in load force, in such a way that it is always

15

slightly greater than the minimum required to prevent slip. If grip force is measured
during rapid arm movements with hand-held objects, it is modulated in parallel with, oreven prior to, fluctuations in the acceleration-dependent inertial load (grip force - load
force coupling: Fig. 2b). This finding indicates that the CNS is able to precisely predictthe movement-induced load, and it has been suggested that this prediction is based on

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an internal model of the motor apparatus and external load (Flanagan & Wing, 1997).

Prediction of sensorimotor feedback -5-

This grip force - load force coupling is explained by a framework that contains aforward model of the arm (Kawato, 1999) (Fig. 2c). Take, for example, a point-to-point
arm-reaching movement that takes place while the hand is grasping an object. A
controller for the arm calculates the motor commands needed to realize the desired
5

motion of the arm. These commands are sent to the arm muscles as well as to theforward dynamics model as the efference copy. Then, the forward model can predict an
arm trajectory that is slightly in the future. Given the predicted arm trajectory, the load
force is calculated; then, by multiplying a friction coefficient and a safety factor, thenecessary minimum level of grip force can be calculated in a grip force controller. To

10realize this grip force modulation, motor commands are sent to hand muscles.
This framework has already been supported by theoretical and empirical studies
as follows. Many studies have indicated that forward models can be acquired more
quickly than the appropriate controllers (Chen-Harris, Joiner, Ethier, Zee, & Shadmehr,
2008; Flanagan & Wing, 1997). Because grip force modulation utilizes output signals

15from the forward model, this framework predicts that the learning rate of the grip force load force coupling is quicker than that of arm control. This prediction has been
supported by behavioral studies (Flanagan & Wing, 1997; Ugata, Kuroda, Imamizu,
Yoshioka, Wada, & Kawato, 2003), indicating quicker learning of grip force - load
force coupling than that of the appropriate control of arm movement in a novel

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environment. These studies suggest that forward models are used in grip force - load
force coupling.

Prediction of sensorimotor feedback -6-

Based on the above framework, we have explored the possible sites of forward
models through an experiment using a functional magnetic resonance imaging (fMRI)(Kawato, Kuroda, Imamizu, Nakano, Miyauchi, & Yoshioka, 2003). In this experiment,
subjects held an object in a precision grip and made cyclic up-and-down movements of5

their right arm according to sounds generated by a computer (Fig. 3a; test condition).
Brain activity during the test condition is expected to include not only activity related togrip force - load force coupling but also activity related to arm movement and activity
related to modulation of grip force. Theoretically, if we remove the last two types ofactivity from activity in the test condition, we can extract the brain activity exclusively

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related to grip force - load force coupling or to the coordination of arm movement andgrip force modulation. Therefore, we added two baseline conditions to the experiment.
One was an "arm-movement" condition (Fig. 3c) where subjects moved their arms inthe same fashion as the test periods but the object was attached at the tips of the fingers
by tape. In this condition, the arm movement was the same as that in the test condition

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but the grip force - load force coupling was not needed because the object would neverslip from the subjects' hands. By measuring the grip force during the experiment, we
confirmed that a change in the grip force in the arm-movement condition (thick curve in
Fig. 3d) was very small compared to that in the test condition (Fig. 3b). The other was a"grip-force-modulation" condition (Fig. 3e) where subjects held the object in the same20

grip as in the other conditions and periodically changed the grip force without arm
movements. Corresponding to the above framework, the test condition activates allcomponents, the arm-movement condition mainly activates the arm controller, and thePrediction of sensorimotor feedback -7-

grip-force-modulation condition activates the grip force controller. Therefore,
subtraction of activity in the latter two conditions from activity in the test condition
would extract activity related to the forward model from the other activity.
Brain activity related to the arm-movement condition was mainly found in the5large portion of the sensorimotor region (Fig. 4a), while activity related to thegrip-force-modulation condition was found in a smaller portion in nearly the same
region (Fig. 4b). These results are consistent with the notion that the sensorimotor
region contributes to control of arms and fingers. Importantly, when activities in the twobaseline conditions were subtracted from the activity in the test condition, we could find10

the residual activity only in the cerebellum (Fig. 4c). This means that parts of thecerebellum acquire forward internal models and contribute to the grip force - load force
coupling. This activity was found in the contra-lateral (left) part to the moving hand andarm (right), although it is known that the hand and arm movements on one side activate
the ipsi-lateral parts of the cerebellum (e.g., Grodd, Hulsmann, Lotze, Wildgruber, &15Erb, 2001). However, activity in the left or bi-lateral cerebellum is often reported whensubjects manipulate an external object using the right hand (e.g., Imamizu, Miyauchi,
Tamada, Sasaki, Takino, Putz, Yoshioka, & Kawato, 2000; Schmitz, Jenmalm, Ehrsson,& Forssberg, 2005; Tamada, Miyauchi, Imamizu, Yoshioka, & Kawato, 1999). Thissuggests that the activity found in the above experiment reflects forward models for not

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only arm movement but also object manipulation.

Prediction of sensorimotor feedback -8-

Coordination between effectors based on prediction of forward models
The above studies show that a forward model contributes to coordination between
movements of different effectors, i.e., the arm and fingers, to accomplish grip force load force coupling. There are several studies indicating that forward models play5

important roles in coordination between effectors in the other context.
Miall and colleagues investigated brain activity related to eye-hand coordination
using a tracking task (Miall, Reckess, & Imamizu, 2001). In their experiment, subjects
followed a moving target with their eyes while simultaneously moving a joystick tocontrol the cursor. The temporal offset between targets for eye and hand motions caused

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parametric variation in the degree of eye-hand coordination (∆t in Fig. 5). The
behavioral data indicated that manual tracking performance was optimal when the target
for eye motion was anticipated before the target for hand motion by 38 ms.
Synchronous movements of two effectors with such a small offset cannot be achieved
simply by reaction to proprioceptive or visual feedbacks. This suggests that a forward

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model predicts the movement outcome based on motor commands for the eye muscles
and that the predicted outcome is sent to the hand-control system for programming or
modifying the manual movement. They conducted fMRI experiments and found that
activity in the lateral cerebellum and the oculomotor vermis (medial part of thecerebellum) parametrically increased as performance of the eye-hand coordination20

improved. This result suggests a contribution of the cerebellum to eye - handcoordination by predicting the movement outcome.

Prediction of sensorimotor feedback -9-

Another fMRI study investigated mechanisms for coordination between a finger(thumb) and arm movements (Diedrichsen, Criscimagna-Hemminger, & Shadmehr,
2007) in a different context from grip force - load force coupling. Using thumbs,
subjects pressed a button attached to the handle of a device that they grasped during arm
5reaching movements. Subjects were trained to press the button depending on the state of
the arm movements without relying on sensorimotor feedback. Theoretically, it wasnecessary for the CNS to predict the future state of the arm from motor commands toaccomplish this task. Brain activity during the state-dependent motor control wascompared to activity in a baseline condition where the subject pressed the button at a

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particular timing independent of the arm state (timing control). Activity related to thestate-dependent control was found in the arm regions of the anterior cerebellum,suggesting that a forward model of the arm is acquired in the CNS and predicts the
future state of the arm.

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Cancellation of sensory inputs using prediction of sensory feedback
It has been suggested that another important function of a forward model is to predict
sensory inputs caused by our own movements to cancel the inputs. A behavioral study
was conducted to investigate why we cannot tickle ourselves (Blakemore, Wolpert, &Frith, 2000). They examined two hypotheses on the reasons for this. One is that there is20

a general gating of all incoming sensory stimulation when we tickle ourselves. The
other is that the CNS precisely predicts, probably using forward models, the sensory

Prediction of sensorimotor feedback -10-

consequences of motor commands for tickling behavior and then cancels these sensory
inputs using the predicted sensory consequences.
In their experiments, they asked subjects to rate the sensation of a tactile stimulus
on the palm of the hand and then examined the perceptual effects of altering the5correspondence between self-generated movement and its sensory (tactile)consequences. The alteration was achieved by a mechanical device, which was designed
to introduce parametrically varied degrees of delay or angular discrepancy between thesubject's movement and the resultant tactile stimulation. If the CNS simply shuts down
sensory input, then the tickled sensation would remain at the same level under all delays

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and angular discrepancies, since movement occurs regardless of delays anddiscrepancies. By contrast, if the CNS utilizes an accurate prediction of the sensation tocancel sensory inputs, the tickled sensation increases as the delay or angulardiscrepancy increases because the difference between the predicted and actual sensory
inputs would increase. Results of their experiments showed that subjects rated the15

sensation increasingly tickly as the delay or discrepancy increased, thus supporting the
hypothesis that the CNS precisely predicts the sensory consequences of motor
commands for tickling behavior.
Furthermore, these researchers conducted functional neuroimaging studies using
positron emission tomography and fMRI to investigate corresponding neural

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mechanisms (Blakemore, Frith, & Wolpert, 2001; Blakemore, Wolpert, & Frith, 1998).
They subtracted brain activity in a condition where subjects tickled themselves fromPrediction of sensorimotor feedback -11-

activity in the other condition, where subjects were tickled by another person. They
found residual activity mainly in the secondary somatosensory cortex (SII) and the
cerebellum. Because the SII receives sensory inputs, activity in the SII probably reflects
tickled sensation itself. Furthermore, they also found a negative correlation between
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activity in the SII and activity in the cerebellum, suggesting that the more thecerebellum was activated the less the SII was activated. This can be interpreted as theoutput signals from the cerebellum being used to cancel input signals to the SII.

Switching of internal models based on prediction of sensorimotor feedback
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Humans interact with myriad objects and environments that often change in a discretemanner. If the CNS maintains only a small number of global internal models, relearningis needed whenever objects and environments change. However, if the CNS maintains alarge number of internal models or modules for different objects and environments, less15

relearning is needed, and thus learning interference is avoided. Moreover, initial
learning of objects and environments may be facilitated by a combination of stored
modules. Many lines of behavioral studies have shown the multiplicity and modularity
of internal models: dual adaptation to prismatic displacements of visual feedback in
opposite directions (Welch, Bridgeman, Anand, & Browman, 1993), independent

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learning of internal models for kinematic and dynamic control of reaching (Krakauer,
Ghilardi, & Ghez, 1999), and composition of output signals from stored modules(Flanagan, Nakano, Imamizu, Osu, Yoshioka, & Kawato, 1999; Ghahramani & Wolpert,

Prediction of sensorimotor feedback -12-1997). Our previous imaging studies first found cerebellar activity related to an internal
model of a novel tool (Imamizu, et al., 2000) and, second, indicated the modularity ofinternal models by showing activation in different sets of cerebellar regions, reflecting
different input-output properties of tools (Imamizu, Kuroda, Miyauchi, Yoshioka, &5Kawato, 2003).
If multiple internal models exist in the CNS, a question arises about how models
are selected or switched appropriately from the current context, i.e., how the CNS
solves the problem of modular selection. Behavioral studies have reported that humans
can flexibly switch internal models based on contextual information. For example, an

10auditory-tone cue can induce context-dependent adaptation to prismatic displacement(Kravitz & Yaffe, 1972). Cognitive contextual cues can help simultaneous learning andswitching of internal models for different dynamic environments (Osu, Hirai, Yoshioka,& Kawato, 2004; Wada, Kawabata, Kotosaka, Yamamoto, Kitazawa, & Kawato, 2003;
Yamamoto, Kawato, Kotosaka, & Kitazawa, 2007). However, little is known about

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neural mechanisms for internal-model switching.
Empirically, two types of information are crucial for the switching of internal
models: contextual information, such as color or shape of the objects that can beperceived before movement execution, and information on the difference between
actual and predicted sensorimotor feedbacks calculated during or after execution. For

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example, when we lift a transparent bottle, the CNS can switch between internal models
for light and heavy objects in a predictive fashion, since we know whether the bottle isPrediction of sensorimotor feedback -13-

empty or full beforehand (upper panel of Fig. 6a). However, when lifting up a paper
milk carton (upper panel of Fig. 6b), we cannot estimate the weight, and the CNS relieson the error between actual and predicted sensorimotor feedbacks (prediction error) as
shown in the diagram in Fig. 6c. In the latter case, switching during or after the5

movement is needed. We call this a "postdictive" switch in contrast to the predictive
switch. The above example illustrates switching during control of forces (dynamics),
but we often encounter similar situations during kinematic control in which forces do
not play an important role. That is, for example, when you want to reduce or increase
the brightness of a room light by adjusting a slide volume control (lower panel of Fig.

106a), you can decide the direction and amount of movement of the controller in a
predictive fashion if you often use the same controller (e.g., if the room is yours) or if alabel illustrating the relationship between brightness and lever position is attached to thecontroller. However, we generally have to rely on the difference (error) between
predicted sensory feedback (expected brightness of the light) and actual feedback, i.e.,15

we typically use a controller for the first time without the benefit of such a label (lower
panel of Fig. 6b). As we have already mentioned, forward models play crucial roles in
prediction of sensorimotor feedback and thus contribute to the postdictive switch ofinternal models based on prediction error.
We conducted a behavioral experiment to investigate whether the predictive

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mechanism is functionally independent from the postdictive mechanism (Imamizu,
Sugimoto, Osu, Tsutsui, Sugiyama, Wada, & Kawato, 2007). Subjects learned to move

Prediction of sensorimotor feedback -14-

their index fingers to targets while visual feedback of the finger movements was rotatedclockwise (CW) or counterclockwise (CCW) by 40º around the initial position (i.e.,kinematic alteration of the relationship between the finger movement and the cursor
movements). When subjects adapted to alternating blocks of opposing rotations, we5

investigated the effects on the subjects' performances due to contextual information (averbal instruction) on the forthcoming direction of rotation. We measured the effect ofsuch contextual information on the predictive mechanism by measuring theperformance error at the beginning of each block and that on the postdictive mechanism
by measuring the speed of gradual decrease of the error within blocks. Consequently,

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the contextual information selectively improved predictive switching performance but
did not affect postdictive switching performance based on prediction errors, suggesting
the existence of functionally independent mechanisms.
Based on the results of our behavioral study, we planned an fMRI experiment toexamine whether these two mechanisms are based on separate neural substrates. In the

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fMRI experiment, discrete pointing movements and event-related fMRI were used toseparate activity related to the presentation of the cognitive cue from that related tosensorimotor feedback (Imamizu & Kawato, 2008). The task for subjects followed thatin our behavioral study, and subjects sufficiently learned the 40º CW and 40º CCW
visuomotor rotations before scanning of brain activity. During the fMRI experiment, the20

direction of rotation changed in a block-random fashion. A cue was presented at the
beginning of each trial and before movement initiation. The color of the cue

Prediction of sensorimotor feedback -15-

corresponded to the direction of rotation of the feedback in an instructed condition, andthus predictive switching was possible. However, the color did not correspond to thedirection in the non-instructed condition, and thus subjects relied on prediction errors
calculated from sensorimotor feedback for switching in a non-instructed condition.
5

Switching-related activity was identified as activity that transiently increased after the
direction of rotation was changed. The switching-related activity in cue periods in the
instructed condition, when a predictive switch is possible, was observed in the superiorparietal lobule (SPL) (Fig. 6d). However, the switching-related activity in feedback
periods in the non-instructed condition, when prediction error is crucial for the10

postdictive switch, was observed in the inferior parietal lobule (IPL) and prefrontal
cortex (PFC) (Fig. 6e). These results clearly demonstrate regional differences in neuralsubstrates between the predictive and postdictive mechanisms.
Our results are consistent with previous studies indicating that the SPL is involved
in modulation of behaviors based on visual information as follows. For example, fMRI

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studies of the SPL identified activity related to discrimination of visual surface
orientation (Shikata, Hamzei, Glauche, Knab, Dettmers, Weiller, & Buchel, 2001) andcontrol of steering according to visual future path information (Field, Wilkie, & Wann,
2007). Many studies indicated a contribution of the SPL to planning of action based on
visual information (Culham, Cavina-Pratesi, & Singhal, 2006; Medendorp, Goltz,

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Crawford, & Vilis, 2005). Kimberg et al. (Kimberg, Aguirre, & D'Esposito, 2000)
investigated activity when subjects switched between different stimulus-responsePrediction of sensorimotor feedback -16-

mappings during a task in which subjects pressed a key according to a letter presented
on a screen, and they reported that the SPL is related to switching based on contextual
information. Our study suggests that the SPL associates contextual information with thedegree of contribution of each individual internal model.

5

In contrast to the SPL, our study suggested that the IPL contributes to thepostdictive switch by integrating sensorimotor feedbacks obtained by vision andproprioception and then comparing this with predictions by forward models to calculate
prediction error. Many studies have suggested the IPL contributes to integration ofmultisensory information. It is known that the lateral bank of the intraparietal sulcus in

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the IPL is related to control of grasping movements guided by vision (Castiello, 2005).
Grefkes et al. (Grefkes, Weiss, Zilles, & Fink, 2002) reported the contribution of the
IPL to cross-modal integration between visual and somatosensory information.
Furthermore, a study on anatomical pathways in the monkey brain indicated that a
region in the IPL (area 7b in monkeys) is the target of output from the cerebellum

15(Clower, West, Lynch, & Strick, 2001). This suggests that prediction of sensorimotor
feedback made by cerebellar forward models is sent to the IPL and compared with theactual sensory feedback and that the results of the comparison (prediction error)
contribute to switching of internal models.

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Discussion

In this article, I reviewed behavioral and imaging studies related to neural mechanisms

Prediction of sensorimotor feedback -17-

for prediction of sensorimotor feedback from the efference copy of motor commands.
These mechanisms are called (internal) forward models. Actual sensorimotor feedbacks
that can be obtained during or after movement are inevitably delayed by many factors:
transmission of motor commands from the brain to muscles, mechanical delay of5muscles and external objects, and processing of visual and proprioceptive feedback
information. Therefore, internal prediction of sensorimotor feedback without relying on
an external feedback loop is useful for rapid, efficient, flexible, and precise control of
our own bodies and objects. Studies reviewed in this article have shown examples ofsuch control: a grip force - load force coupling, coordination of effectors, cancelation of

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sensory inputs caused by one’s own movement, and switching of sensorimotor tasks.
So far, I have mainly reviewed functional neuroimaging studies indicating thatforward models are acquired in the cerebellum. In the following, I refer to studies that
directly measured electrophysiological activity of neurons. These studies found neuralactivity reflecting forward models not only in the cerebellum but also in the other15

cortical regions. Miall and colleagues proposed that simple spike activity of Purkinjecells (neurons in the cerebellar cortex) represent prediction of sensory feedback and iscorrected by complex spike activity representing a discrepancy between the prediction
and actual feedback (Miall, et al., 1993). They found, as supporting evidence, that theinterval between an increase in simple spike activity and the resulting complex spike

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activity is about 150 ms, which is equivalent to visuomotor feedback delay andnecessary for synchronizing the prediction and feedback (Miall, Keating, Malkmus, &Prediction of sensorimotor feedback -18-

Thach, 1998). Recently, it has been suggested that simple spike discharge of Purkinjecells has several characteristics of a forward internal model of the arm (Ebner & Pasalar,2008).
Another stream of studies has suggested that neural activity in parietal or frontal
5

regions reflects forward-model computation or signals related to forward models. A
study recorded activity in the posterior parietal cortex when monkeys moved a cursor
toward a target by controlling a joystick, and it found neurons encoding a forward
estimate of the state of the cursor, that is, the direction of cursor movements caused by
joystick movement (Mulliken, Musallam, & Andersen, 2008). Their encoding

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properties were available from neither sensory feedback nor outgoing motor commands
but from sensory signals predicted by a forward model. Studies on neuronal activity
during eye-movement tasks have also found data related to forward models. For
example, eye movement causes a shift in the visual image on the retina, but a study has
shown that the location of receptive fields of neurons in the lateral intra-parietal area

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shifts before the initiation of the eye-movement based on prediction of a shift of a target
on the retinal image (Duhamel, Colby, & Goldberg, 1992). This suggests that parietal
neurons use information about intended eye movements and update the representationof visual space. Such update cannot be done without prediction of the movement
outcome from an efference copy of motor commands sent to the eye muscles. Another20

study investigated a pathway that conveys an efference copy of motor commands forsaccadic eye movements, and it identified an ascending pathway from the superiorPrediction of sensorimotor feedback -19-colliculus to the frontal eye field via the mediodorsal thalamus (Sommer & Wurtz,
2002). According to this result, the frontal eye field is a target location of the efference
copy and a possible neural correlate of a forward model.
Many studies have indicated that the cerebellum is an important locus of
5

forward-model computation, but parietal and frontal regions have also been suggested
as contributing to the computation or having relationships with the input signals to andoutput signals from forward models. Probably, the locus of forward models is notunique, and several regions contribute to the computation according to different
contexts and requirements of tasks. Future studies that can extract neural information

10

simultaneously from multiple regions in the brain will enables us to elucidate theoutlines of the brain network used for the forward-model computation as well as thefunctional differences among the regions.
Although I mainly focused on the contribution of forward models to control of
movements, many researchers are now interested in the contribution of forward models

15

to higher-order cognitive functions. By using forward models, the CNS can constructinternal simulation loops without relying on external feedback. Hurley proposed a"Shared Circuit Model" in which cognitive functions such as mirror systems, imitation,
mental simulation of social interactions, and mind reading (theory of minds) use suchinternal simulation loops for sensorimotor control and additional systems that inhibit

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motor outputs and generate virtual sensory inputs during the simulation (Hurley, 2008).
More specifically, Oztop and colleagues conducted computer simulations of tasks that

Prediction of sensorimotor feedback -20-

are closely related to mirror systems (Oztop, Kawato, & Arbib, 2006; Oztop, Wolpert,& Kawato, 2005). In their simulations, an observer estimated the goal of reaching
movements or the intention of an agent performing grasping movements. The results
indicated that, in theory, forward models for sensorimotor control contribute to effective
5

inferring the goals or mental states of others, suggesting that the models contribute to
mirror systems and theory of minds. These internal circuits support "mental
simulations" of interactions between one's own actions and the resultant changes in theenvironment, and they can increase accuracy based on recursive computation insophisticated planning and selection of behaviors necessary for social interactions and10achievement of long-term goals. It is well known that immediate feedback of error isimportant for motor learning that depends on the cerebellum (Ito, 1984; Kitazawa,
Kohno, & Uka, 1995). However, feedback in social interaction is often delayed, and this
delay can be on the order of hours or days. Therefore, the internal circuits include not
only the cerebellum but also other brain regions, such as prefrontal regions that can

15compensate for these variable and unpredictable delays.
Because neural mechanisms for such cognitive functions are more complex thanthose for simple reaching and grasping movements, future theoretical and empiricalstudies are needed to fully understand how internal simulation loops support
higher-order functions. However, the current studies will give us important clues to20

understanding how human intelligence has emerged from fundamental brain functions,such as control of movements, which are common with other species including

Prediction of sensorimotor feedback -21-

non-human primates.

Prediction of sensorimotor feedback -22-

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Prediction of sensorimotor feedback -26-

Figure Legends

Figure 1
Examples of internal forward models and inverse models. (a) Internal models for arm
5reaching movement. (b) Internal models for an external object and a tool (e.g., acomputer mouse).

Figure 2
Illustrations of grip force - load force coupling. (a) An object held with the tips of the10index finger and thumb. When lifting the object, the normal grip force on the contact
surface allows for the development of a frictional force, which prevents the object fromslipping. The figure illustrates the load force in the deceleration phase of the upward
movement. (b) Examples of time courses of grip force (solid curve) and load force(broken line) when lifting up and down an object. A dash-dotted line indicates a peak

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latency of the grip force. (c) A framework explaining mechanisms for grip force - load
force coupling based on inverse and forward models of the arm. Reproduced withpermission from reference (Kawato, 1999) © Elsevier.

Figure 3
20

Three conditions in an fMRI experiment investigating brain activity related to grip force- load force coupling (a, c and e) and typical change in grip force and load force
measured during each condition (b, d and f).

Prediction of sensorimotor feedback -27-

Figure 4
Brain activity related to arm movements (a), grip force modulation (b) and coordination
between arm movement and grip force modulation (c). White regions enclosed by thick5

curves indicate activated regions. In panels a and b, activated regions are indicated on
the brain surface. In the top image of panel c, activity is rendered on the coronal slice as
indicated by the bottom panel.

Figure 5
10

Examples of target trajectories used in an experiment investigating eye - handcoordination (Miall, et al., 2001). Solid curves indicate target trajectory for hand(joystick) tracking as time courses of the horizontal (x) position (a) and the vertical (y)position (b). Dashed curves indicate target trajectory for eye tracking, which isadvanced or delayed from the target for the hand tracking by a parameter ∆t. (c) A

15target trajectory for the hand tracking is represented in x - y coordinates. Each position
is represented in pixel coordinates of a computer screen.

Figure 6
Illustrations of two types (a), (b) of switching in sensorimotor tasks. (a) Examples of20

predictive switch. When we lift a transparent bottle, the CNS can switch between
internal models for light and heavy objects in a predictive fashion, since we knowwhether the bottle is empty or full beforehand (an example for control of dynamics:

Prediction of sensorimotor feedback -28-

upper panel). When we control the brightness of a room light, we can decide thedirection and amount of movement of the control lever before moving the controller if alabel is attached to the controller indicating the relationship between the brightness andposition of the lever or, otherwise, if we are familiar with the controller (an example for5control of kinematic control: lower panel). (b) Examples of postdictive switch. Whenlifting up a milk carton, we cannot estimate the weight, and the CNS relies on the error
between actual and predicted sensorimotor feedbacks. In such a case, switching during
or after the movement is needed (upper panel). In the case of light control, we have torely on sensory feedback (brightness of the light) if no label is attached to the controller

10(lower panel). (c) A schema for calculation of prediction error of sensorimotor feedback.
Brain activity related to predictive switch (d) or postdictive switch (e) as found by an
fMRI study (Imamizu & Kawato, 2008). Activated regions (white regions enclosed bythick curves) are indicated on the brain surface viewed from the top. SPL: Superiorparietal lobe, IPL: Inferior parietal lobe, PFC: prefrontal cortex.

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Figure 1

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5Figure 2

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Figure 35Prediction of sensorimotor feedback -32-

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Figure 4Prediction of sensorimotor feedback -33-

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Figure 5Prediction of sensorimotor feedback -34-

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Figure 6