Social Cognitive Neuroscience

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Social cognitive neuroscience and humanoid robotics

Thierry Chaminade a,*, Gordon Cheng b

a Mediterranean Institute for Cognitive Neuroscience (INCM), Aix-Marseille University CNRS, 31 Chemin Joseph Aiguier, 13402 Marseille Cedex, Franceb Department of Electrical Engineering and Information Technology, Cluster of Excellence Cognition for Technical Systems CoTeSys, Barer Str. 21, Technical

University Munich, 80290 Munich, Germany

a r t i c l e i n f o

Keywords:

Robotic

Humanoid

Human

Cognition

Neuroscience

Social interactions

a b s t r a c t

We believe that humanoid robots provide new tools to investigate human social cognition, the processesunderlying everyday interactions between individuals. Resonance is an emerging framework to under-

stand social interactions that is based on the finding that cognitive processes involved when experiencing

a mental state and when perceiving another individual experiencing the same mental state overlap, both

at the behavioral and neural levels. We will first review important aspects of his framework. In a second

part, we will discuss how this framework is used to address questions pertaining to artificial agents

social competence. We will focus on two types of paradigm, one derived from experimental psychology

and the other using neuroimaging, that have been used to investigate humans responses to humanoid

robots. Finally, we will speculate on the consequences of resonance in natural social interactions if

humanoid robots are to become integral part of our societies.

2009 Elsevier Ltd. All rights reserved.

1. Introduction

Humanoid robots are robots whose appearance resembles that

of a human body, in our case a robot with two legs, two arms

and a head attached to a trunk. Because of this anthropomorphism,

they provide relevant testbeds for hypotheses pertaining to human

cognition. The phrase understanding the brain by creating the brain

was coined to synthesize how humanoid robots and computational

neuroscience could contribute to progresses in naturalizing human

psychology and the underlying neurophysiology (Asada et al.,

2001; Brooks, 1997; Cheng et al., 2007; Kawato, 2008). Here, we

will discuss the application of this adage to the investigation of so-

cial interactions, on the premise that robots provide testbeds for

hypotheses pertaining to natural social interactions.

The distinction we wish to make here is with past approaches

that placed focuses on behavior syntheses as the core of cogni-

tion (Arkin, 1998; Atkeson et al., 2000; Brooks, 1997) but,although said to be biologically-inspired, had little direct input

from biological sciences. In contrast we wish to bring forward a

direct connection between humanoid robotics and social

cognitive neurosciences, in an endeavor to gain:

1. a better understanding of social interactions of humanhuman

and humanmachines (Chaminade, 2006; Chaminade and Dec-

ety, 2001);

2. deeper understanding of brain functions involved in these inter-

actions (Chaminade et al., 2007);

3. better engineering guidelines in building machines (as sug-

gested byCheng et al., 2007) suitable for human interactions.

In this review, we will provide examples of how robots can be

used to test hypotheses pertaining to human social neuroscience,

both in behavioral (Section 3.1) and neuroimaging (Section 3.2)

experiments, but also how social cognitive neurosciences can pro-

vide insights for developing socially competent humanoid robots

(Section 4.1). First, we will present a brief history of humanoids

development.

The last decade has seen the emergence of increasingly autono-

mous humanoids, and eventually of androids. Hondas humanoids

P2, in 1996, followed by P3 in 1997 and ASIMO in 2000 (Hirai et al.,

1998; Sakagami et al., 2002), were among the first humanoids

walking on their legs and feet (Fig. 1) and eventually climbingstairs and navigating autonomously, that stunned the world by

going public: human-like robots were on their way from fiction

to reality. SONY produced QRIO (Fig. 1) for entertainment purposes

(Nagasaka et al., 2004), and the Humanoid Robotics Project inves-

tigate practical applications of humanoid robots (HRP series) coop-

erating with humans (Hirukawa et al., 2004). Fundamental

developments in humanoid research also started their investiga-

tions with bipedal walk, as early as the mid-1960s (Waseda

Lower-Limb series), then started to use humanoids as the embod-

ied platform necessary for certain application, with actuators and

sensors approximating human motor and sensory processes in

order to simulate human intelligence (Brooks, 1997). The use of

0928-4257/$ - see front matter 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.jphysparis.2009.08.011

* Corresponding author.

E-mail address: [email protected](T. Chaminade).

Journal of Physiology - Paris 103 (2009) 286295

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humanoids to understand the brain is now at the core of many

projects, such as RoboCub, a European project investigating humancognition, and in particular developmental psychology, through

the realization of a humanoid robot the size of a 3.5 year old child,

iCub (Sandini et al., 2004). The humanoid robots DB and CB, pro-

duced in two projects headed by Mitsuo Kawato, were used in

some studies reported here. In the ERATO project, the robotic

group, led by Dr. Stefan Schaal and in collaboration with the re-

search company SARCOS (Hollerbach and Jacobsen, 1996), devel-

oped a humanoid robot called DB (Dynamic Brain) replicating a

human body given the robotics technology of the mid 1990s

(Fig. 1). It was followed by the ICORP Computational Brain Project

in which Dr. Gordon Cheng, again in collaboration with SARCOS,

developed a new humanoid robot called CB (Computational Brain,

Cheng et al., 2007), more accurate in reproducing the human body

than DB (Fig. 1).Because they reproduce part of the human appearance,

humanoids provide testbeds for hypotheses pertaining to natural

social interactions. They are used for researching how global hu-

man-like appearance influences our perception of other agents, in

comparison to real humans or, at the other end of the spectrum,

industrial robotic arms. This is even more so of androids, a spe-

cific type of humanoids that attempt to reproduce the human

appearance not only in their global shape, but also their fine-

grained details. Interestingly, the acceptability of androids in

everyday application has been described by the Uncanny Valley

of Eeriness hypothesized by Japanese roboticist Masahiro Mori

(Mori, 1970). While one would expect that social acceptance of

robots would increase with anthropomorphism, the uncanny

valley hypothesis postulates that artificial agents attempting,but imperfectly, to impersonate humans, the case of androids, in-

duce a negative emotional response (MacDorman and Ishiguro,

2006; Mori, 1970). While this hypothesis has proved itself

impractical, as neither anthropomorphism nor emotional re-

sponse easily lend themselves to being described by one-dimen-

sional variables, understanding the cognitive mechanisms

underlying the feeling of uncanniness that one experiences when

facing an android will be invaluable to understanding human so-

cial cognition; this is one of the objectives of the emerging field ofandroid science (MacDorman and Ishiguro, 2006). Androids indis-

tinguishable from humans in terms of form, motion and behav-

iors, a goal not unlike the Total Turing Test Stevan Harnad

proposed (Harnad, 1989), would be invaluable for research by

providing fully controlled partners in experimental social interac-

tions. While artificial conversational abilities at the core of the

original Turing Test (Turing, 1950), including language, semantics

and symbolism, are beyond the scope of the present article, the

concept of a robot passing a Total Turing Test highlights the

possible outcomes of bidirectional exchanges between robotic

developments and research in human cognition.

The goal of this review is not to provide definitive answers

about optimized robot design in the form of a series of guidelines

for roboticists, but to present an overview, based on our works,

on how robotics and cognitive sciences can work together towards

the goal of developing social humanoids. We will rely on one the-

oretical framework that fueled our work, the hypothesis of motor

resonance, that pertains to embodied social interactions with a fo-

cus on actions. After a section describing this framework, a second

part will present pertinent experimental results obtained using ro-

botic devices, and a last part will attempt to derive guidelines for

improving the social competence of interacting humanoids based

on this framework.

2. Motor resonance in social cognition

Theories of social behaviors using concepts of resonance have

flourished in the scientific literature following the finding thatthe same neural structures show an increase of activity both when

executing a given action and when observing another individual

executing the same action (Blakemore and Decety, 2001; Gallese

et al., 2004; Rizzolatti et al., 2001). Neuropsychological findings,

that used action production, perception, naming and imitation,

hinted, in the early 1990s, that limb praxis and gesture perception

share some parts of their cortical circuits (Rothi et al., 1991). Sim-

ilarly in language, the motor theory of speech perception claimed,

on the basis of experimental data, that the object of speech percep-

tion are not sounds, but the phonetic gestures of the speaker,

whose neural underpinnings are motor commands (Liberman

and Mattingly, 1985). We will refer to these processes under the

header of motor resonance, which is defined, at the behavioral

and neurallevels, as the automatic activation of motor control sys-

tems during perception of actions.

2.1. Neurophysiology of resonance

Mirror neurons offered the first physiological demonstration

that motor resonance had validity at the cellular level. Mirror neu-

rons are a type of neuron found in the macaque monkey brain and

defined by their response, as recorded by single cell electrophysio-

logical recordings. First reported in 1992 by Giacomo Rizzolattis

group in Parma (di Pellegrino et al., 1992), they were officially

named mirror neurons in a 1996 Cognitive Brain Research report

as a particular subset of F5 neurons [which] discharge[s] when the

monkey observes meaningful hand movements made by the

experimenter (Gallese et al., 1996). The importance of this discov-ery stems from the known function of area F5, a premotor area in

Fig. 1. Center: SONY humanoid robot QRIO (photo courtesy of SONY). Clockwise

from left, bottom: HONDA humanoid robots P3 and ASIMO (Advance) (photo

courtesy of HONDA); infanoid (photo courtesy of Hideki Kozima); ATR humanoid

robot, DB (co-developed withSARCOS during the JST Kawato dynamic brainproject.

Photo courtesy of Stefan Schaal); CB (co-developed with SARCOS during the

computational brain project. Photo by Jan Moren, courtesy of Gordon Cheng).

T. Chaminade, G. Cheng/ Journal of Physiology - Paris 103 (2009) 286295 287


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which neurons discharge when monkeys execute distal goal-direc-

ted motor acts such as grasping, holding or tearing an object. Com-

paring the various reports, it is reasonable to assume that around

20% of recordable neurons in this area have mirror properties in

a loose sense, with a lower percentage, around 5%, shows action

specificity, i.e. the same action is the most efficient in causing

the neuron to fire when the monkey observes and when he exe-

cutes it. These neurons are activated both during the execution

of a given goal-directed action and during the observation of the

same action made in front of the monkey.

The human physiological data, using the brain imaging tech-

niques which emerged in the last decades such as positron emis-

sion tomography (PET), functional magnetic resonance imagery

(fMRI), electroencephalography (EEG), magnetoencephalography

(MEG) and transcranial magnetic stimulation (TMS), entails an ex-

pected conclusion on the basis of the mirror neuron literature in

macaque monkey: premotor cortices, originally considered to be

exclusively concerned with motor control, are also active during

observation of actions in the absence of any action execution

(Chaminade and Decety, 2001). What remains unknown is whether

the same brain region, and a fortiori the same neurons, would be

activated by the observation and the execution of the same action

in the whole of the premotor system, or whether this specificity is

limited to a small percentage of ventral premotor neurons. In other

words, are all premotor regions activated in response to the obser-

vation of action populated with mirror neurons? But irrespective of

the answer to this question, accumulating human neuroimaging

data does confirm in humans what mirror neurons demonstrated

beyond doubt in macaque monkeys at the cellular level: neuro-

physiological bases for the perception of other individuals behav-

iors makes use of the neurophysiological bases for the control of

the selfs behavior.

An intriguing trend in human cognitive research is that this

resonance is not limited to observation of object-directed hand

actions, as mirror neurons are, but generalizes to a number of

other domains of cognition. For example, an fMRI study investi-

gated touch perception by looking for overlap between beingtouched and observing someone being touched (Keysers and Per-

rett, 2004). An overlap of activity was found in the secondary

somatosensory cortex, a brain region involved in integrating

somatosensory information with other sensory modalities such

as touch. Another study reported activity in the primary sensory

cortex during the observation of touch (Blakemore et al., 2005).

Thus, there is a resonance for touch, by which observation of

someone else being touched recruits neural underpinnings of

the feeling of touch. In the same vein, observation of the expres-

sion of disgust activates a region of the insula also activated dur-

ing the feeling of disgust caused by a nauseating smell (Wicker

et al., 2003). Empathy for pain also makes use of resonance in

the anterior cingulate cortex (Singer et al., 2004). Taken together,

these findings led to the hypothesis that a generalized resonancebetween oneself and other selves, or social resonance, underlies a

number of social behaviors including action, such as action

understanding (Chaminade et al., 2001) and imitation (Rizzolatti

et al., 2001), but also more generally in the social domain, such

as empathy and social bonding.

In summary, the mirror neurons studied in macaque monkey

provided a very specific example of a more general mechanism

of human cognition, namely the fact that neuronal structures used

when we experience a mental state, including but not limited to

internal representation of an action, are also used when we per-

ceive other individuals experiencing the same mental state. Recent

examples support a generalization of motor resonance to other do-

mains of cognition such as emotions and pain that can be trans-

ferred between interacting agents, hence the term of socialresonance.

2.2. Resonance in social interactions

Motor resonance is evident in behaviors like action contagion

(contagion of yawning for example), motor priming [the facilita-

tion of the execution of an action by seeing it done (Edwards

et al., 2003)] and motor interference [the hindering effect of

observing incompatible actions during execution of actions (Kilner

et al., 2003)]. But, does the motor resonance described in a labora-tory environment have a significant impact in everyday life? The

chameleon effect was introduced to describe the unconscious

reproduction of postures, mannerisms, facial expressions and

other behaviors of ones interacting partner (Chartrand and Bargh,

1999). Subjects unaware of the purpose of the experiment inter-

acted with an experimenter performing one of two target postures,

rubbing the face or shaking the foot. Analysis of the behavior

showed a significant increase of the tendency to engage in the

same action. This effect can easily be experienced in face-to-face

interactions, when one crosses his arms or legs to see his partner

swiftly adopt the same posture. In addition this imitation makes

the interacting partner more likable even though you are not

aware of this imitation (Chartrand and Bargh, 1999). This mimicry

has been described as a source of empathy (Decety and Chaminade,

2003), so that motor resonance offers a parsimonious system to

automatically bond with conspecifics.

The main function classically attributed to resonance is action

understanding. The most convincing argument to date comes from

neuropsychology, the study of cognitive impairments consecutive

to brain lesions. It was recently reported that premotor lesions

impair the perception of biological motion presented using point-

light displays (Saygin, 2007). Therefore, not only are premotor cor-

tices activated during the perception of action, but also their lesion

impairs the perception of biological motion, demonstrating that

they are functionally involved in the perception of action.

Another function frequently associated with resonance is imi-

tation. Imitation covers a continuum of behaviors ranging from

simple, automatic and involuntary action contagion to intentional

imitation and emulation (Byrne et al., 2004). It is extensively usedas a diagnostic tool in the neuropsychology of apraxia. Research

on the neural bases of imitation supports the intervention of mo-

tor resonance in several types of imitative behaviors. At the auto-

matic level, observing an action that shares features with an

action present in the observers repertoire primes the production

of the same action (Brass et al., 2000). Using fMRI to investigate

the neural substrate of this phenomenon, Iacoboni et al. (Iacoboni

et al., 1999) showed increased activity in the inferior frontal

gyrus when subjects actions were primed by action observation

compared to the other conditions of action execution. This region

involved in human motor priming is putatively the homologue of

the macaque monkey area F5, where mirror neurons were first

reported. A study of voluntary imitation aimed at disentangling

brain representation for the goal of an action and the means toachieve this goal demonstrated an involvement of the inferior

parietal lobule bilaterally in imitation irrespective of the feature

of the action being imitated (Chaminade et al., 2002), this brain

region being in humans the possible homologue of the macaque

monkey area PF where mirror neurons were also reported (Rizzol-

atti and Craighero, 2004). The same regions were also active

when subjects naive in playing the guitar learned to do so by

observing an expert in another fMRI experiment (Vogt et al.,

2007). Regions in the inferior parietal lobule and ventral premo-

tor cortex were more active when subjects observed actions to

reproduce them later than during action observation without

instruction to imitate. These results suggest that observed actions

were internally simulated in order to parse them into elementary

components to be able to reproduce them later. Altogether theseresults support the engagement of structures involved in motor

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resonance in increasingly complex form of imitation, from motor

priming to action imitation to imitative learning.

3. Resonance applied to humanoid robotics

Motor resonance is a well-studied phenomenon central to the

understanding of social behaviors (Decety and Chaminade, 2003).

The methods that have been developed to investigate it have beenextended to investigate how humans react to anthropomorphic

artificial agents such as humanoid robots. The underlying assump-

tion is that the measure of resonance indicates the extent to which

an artificial agent is considered as a social inter-actor.

3.1. Behavioral experiments

In an experimental paradigm developed to investigate motor

interference, volunteers were asked to raise their fingers in re-

sponse either to a symbolic cue appearing on a nail or to a move-

ment of the finger of a hand presented visually (Brass et al., 2000).

The two cues could be present on the same finger (congruent cues)

or on different fingers (incongruent cues). In the later case, there

were two conflicting cues and only one was relevant for the volun-teers. It was found that the observation of an incongruent finger

movement hindered the response to the symbolic cue i.e. in-

creased the time needed to respond but that the reverse effect

i.e. the symbolic cue hindering the response to the finger move-

ment- was very small. In other word, when responding to a sym-

bolic cue, the response is hindered by the observation of an

incompatible action and facilitated by a compatible one. In this

paradigm, producing an action similar to an observed action is a

prepotent response that requires to be inhibited to execute the cor-

rect response. To summarize, as a consequence of motor resonance,

perception of another individuals actions influences the execution

of actions by the self: observing an action facilitates the execution

of the same action (motor priming), and hinders the execution of a

different action (motor interference). These behavioral effects canbe investigated experimentally to provide objective measures of

the magnitude of motor resonance depending on the nature of

the agents.

3.1.1. Motor priming with a robotic hand

Motor priming can be conceptually conceived as a form of

automatic imitation consequential of motor resonance. In other

words, observing an action facilitates (primes) the execution of

the same action. In experimental terms, responses that are primed

by observation are faster and more accurate. This effect was inves-

tigated with two actions, hand opening and hand closing, in re-

sponse to the observation of a hand opening and closing, with

the hand being either a realistic human hand or a simple robotic

hand having the appearance of an articulated claw with two oppo-site fingers (Press et al., 2005). Volunteers in the experiment were

required to make a prespecified response (to open or to close their

right hand) as soon as a stimulus appears on the screen. Response

time was recorded and analyzed as a function of the content of the

stimulus, either a human or a robotic hand, in a posture congruent

or incongruent with the prespecified movement (e.g. open or

closed hand when the prespecified action is opening the hand).

Results showed an increased response time in incongruent

compared to congruent conditions, in response to both human

and robotic hand, suggesting that the motor priming effect was

not restricted to human stimuli but generalized to robotic stimuli

(Press et al., 2005). As with the motor interference measure, the

size of the effect, taking the form of the time difference between

response to incongruent and congruent stimuli, was larger for hu-man stimuli (30 ms) that for robotic stimuli (15 ms).

A follow-up experiment tested whether the effect is better ex-

plained by a bottom-up process due to the overall shape or a

top-down process caused by the knowledge of the intentionality

of humans compared to robotic devices (Press et al., 2006). Human

hands were modified by the additionof a metal and wire wrist, and

were perceived as less intentional than the original hands. Never-

theless in the priming experiment, no significant differences were

found between the priming effect of the original and of the robot-ized human hand, in favor of the bottom-up hypothesis that the

overall hand shape, and not its description as a human or robotic

hand, affects the priming effect.

3.1.2. Motor interference with humanoid robot DB

We investigated motor resonance elicited by the humanoid ro-

bot DB. DB is a 30 degrees-of-freedom (hydraulic actuators) hu-

man-size (1.85 m) anthropomorphic robot with legs, arms,

fingerless hands, a head and a jointed torso. These human-like fea-

tures were central to the experiment described in details now.

This series of experiments (Chaminade et al., 2005; Oztop et al.,

2005b), was initiated by Kilner et al.s (Kilner et al., 2003) study of

motor interference when facinga real human being or an industrial

robotic arm. Volunteers in this study produced a vertical or hori-

zontal arm movement while watching another agent in front of

them producing a spatially congruent (i.e. vertical when vertical,

horizontal when horizontal) or a spatially incongruent (horizontal

when vertical and vertical when horizontal) movement. The inter-

ference effect, measured by the increase of the variance in the

movement, was found when volunteerswatched an arm movement

spatially incompatible with the one they were producing e.g.

vertical versus horizontal,Fig. 2(Kilner et al., 2003). Interestingly,

Kilner et al.s study did not find any interference effect using an

industrial robotic arm moving at a constant velocity, suggesting

at first that motor interference was specific to interactions between

human agents.

The original experimental paradigm was adapted to investigate

how humanoid robots interfere with humans (Fig. 2). In these

experiments, subjects performed rhythmic arm movements whileobserving either a human agent or the humanoid robot DB stand-

ing approximately 2 m away from them performing either congru-

ent or incongruent 0.5 Hz rhythmic arm movements. The robot

was programmed to track the end point Cartesian trajectories of

rhythmic top-left to bottom-right and top-right to bottom-left

reaching movements involving elbow, shoulder and some torso

movements by commanding the right arm and the torso joints of

the robot. The experimenter listened to a 1 Hz beep on headphones

to keep its beat constant. Subjects were instructed to be in phase

with the other agents movements. During each 30-s trial, the kine-

matics of the endpoint of the subjects right index finger was re-

corded with a motion capture device. The variance of the

executed movements was used as a measure of motor interference

caused by the observed action. Briefly, each individual movementwas segmented from the surrounding movements by the identifi-

cation of endpoints using 3D curvature. Trajectories were projected

onto a vertical and a horizontal planes. The signed area of each

movement is defined as the deviation from the straight-line joining

the start and end of each segmented movement. The variance of

this signed area within a trial provides an estimate of the amount

by which this curvature changes between individual movements.

The variance was divided by the mean absolute signed area during

this trial to normalize the data.

In a first experiment (Oztop et al., 2005b), trajectories were de-

rived from captured human motion of the same movements per-

formed by the human control for the experiment. We found

(Fig. 2) that in contrast to the industrial robotic arm, the humanoid

robot executing movements based on motion captured data causeda significant change of the variance of the movement depending on

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congruency (Oztop et al., 2005b). The ratio between the variance in

the incongruent and in the congruent conditions increases from

the industrial robotic arm (r= 1, no increase in incongruent condi-

tion, as reported inKilner et al. (2003)and the human (r 2), both

in ours and in Kilner et al.s study. The new result was that an

humanoid robot triggers an interference effect but weaker than a

human (r 1.5).

In a follow-up experiment, we investigated the effect of the

movement kinematics on the interference. The humanoid robot

moved either with a biological motion based, as previously, on re-

corded trajectories, or with an artificial motion implemented by a

1-DOF sinusoidal movement of the elbow. We found a significanteffect of the factors defining the experimental conditions. The in-

crease in incongruent conditions was only significant when the ro-

bot movements followed biological motion (Chaminade et al.,

2005). A similar trend for artificial motion was not significant.

The ratio that could be calculated on the basis of the results was,

in the case of biological motion, comparable to the ratio reported

in the previous experiment, 1.3. Note the importance of having

internal controls, in this case human agents, to compare the ratio

within groups.

A final experiment assessed whether seeing the full body or

only body parts of the other agent influences motor resonance

(Chaminade, Franklin, Oztop and Cheng, unpublished results).

The effect of interference could be due merely to the appearanceof the agent, which would predict a linear increase of the ratio

Fig. 2. Top: factorial plan showing the four canonical condition of motor interference experiment: horizontally, the spatial congruency between the volunteers and the tested

agent movement; vertically, the human control and the agent being tested, in this case the humanoid robot DB. Bottom: summary of the results from the three experiments

described in the text. Bars represent the ratio between the variance for incongruent and congruent movements (error: standard error of the mean). Effect of appearance:

results are given for three agents, an industrial robot on the left (Kilner et al., 2003), a humanoid robot with biological motion at the center (Oztop et al., 2005b) and a human

on the right (Kilner et al., 2003; Oztop et al., 2005b). Effect of the motion: the humanoid robot DB displays artificial (ART) or biological (BIO) motion (Chaminade et al., 2005).

Effect of visibility the humanoid robot displays artificial (ART) or biological (BIO) motion while its body is visible or hidden by a cloth (unpublished observations).



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between the variance for incongruent and congruent movements

with anthropomorphism. Alternatively it could be influenced by

the knowledge we have about the nature of the other agent. Cur-

rent knowledge on motor resonance, as well as the previous re-

sults, including reproducing the doubling of variance in ours and

Kilner et al.s (Kilner et al., 2003) experiment, favors the former

hypothesis of a purely bottom-up (i.e. perceptual and automatic)

process. To test whether appearance was the main factor, we cov-

ered the body and face of both agents, the human and the human-

oid robot, with a black cloth leaving just the moving arm visible,

and compared the results of the interference paradigm between

covered and uncovered agents. Preliminary results indicate that

the variance is only increased when the body is visible, implying

that motor interference cannot be measured in the absence of body

visibility. This suggests that arm movements, from either a human

or a humanoid robot, do not provide sufficient cues about the nat-

ure of the agent being interacted with to elicit motor resonance

(bottom-up effect of the stimulus). Also, knowledge about the as-

pect of the agent being interacted with is not sufficient to elicit

motor resonance (top-down effect of the knowledge). These results

confirm the conclusions of he motor priming experiment described

previously, in favor of a bottom-up effect due to the appearance of

the robotic device.

Overall, these accumulating results confirm the validity of using

motor interference and motor priming as metrics of motor reso-

nance, a possible proxy for social competence, with humanoid ro-

bots. First, motor resonance is an important aspect of social

cognition, particularly important in automatic and unconscious

perception of other agents. Second, the effects of motor resonance

on behavior can be measured objectively, as movement variance or

reaction time. Third, existing results strongly suggest the effect is

modulated by the appearance of the agent being tested. And final-

ly, these interference effects have been shown to increase with the

realism of the stimulus.

3.2. Neuroimaging experiments

Motor resonance has been extensively studied with neuroimag-

ing in humans, and it is possible to adapt similar approaches to the

perception of anthropomorphic robots.

3.2.1. Neuroimaging of grasping movements with a robotic hand

Neuroimaging experiments comparing the observation of hu-

mans versus robots have so far yielded mixed results. In a PET

study, subjects were presented with grasping action performed

by a human or by a robotic arm. The authors report that the left

ventral premotor activity found in previous experiments of ac-

tion observation responded to human, but not robot, actions

(Tai et al., 2004). However, results of a recent fMRI study indi-

cate that a robotic arm and hand elicits motor resonance, in

the form of increased activity in regions activated by the execu-tion of actions during the observation of object-directed actions

compared to simple movements (Gazzola et al., 2007). Further-

more, the trend is of an increased activity in response to robot

compared to human stimuli, though this increase is not reported

as significant. How can we reconcile these two sets of results?

One possibility, the difference techniques used in these experi-

ments, PET and fMRI, cannot explain the dramatic reversal of

the results. Another possibility derives from differences in

anthropomorphism of the robotic arm and hand used by the

two groups, but in the absence of figure representing the robotic

arm used in Tai et al., it is difficult to draw conclusions. It is en-

ough to acknowledge here that according to both reports, the ro-

botic arms and hands and their motions were not attempting to

be realistic. The interpretation proposed by Gazzola et al., aboutthe repetition of stimuli reducing activity in these areas also

seems questionable, as both robot and human stimuli underwent

the same procedure in Tai et al. procedures. Note that the ab-

sence of motor interference when the body hidden, reported in

the previous section, is not relevant to understand the present

data as stimuli consisted of object-directed actions in both

experiments, in contrast to the meaningless arm movements

used in the interference experiments.

Another source of discrepancy between the two studies comesfrom the experimental instructions. Indeed, instructions can have

significant effects on the brain structures involved in a given cog-

nitive task. This has been clearly shown in fMRI studies in which

subjects interacted with a similar random program but were pre-

sented their partner as varying in anthropomorphism (Krach

et al., 2008). Regions involved in mentalizing were more active

when subjects believed they were interacting with the human

compared to a unintentional, artificial agent. This highlights the

importance of the experimental setting, in particular when using

artificial agents. While it is the robot embodiment that is manip-

ulated in both Tai et al. and Gazzola et al. studies, their instruc-

tions do differ. In the first report, subjects were instructed to

carefully observe the human (experimenter) or the robot model,

while in the second, subjects were instructed to watch the mov-

ies carefully, paying particular attention to the relationship be-

tween the agents and the objects. Well propose in the next

part that differences between these instructions, in particular

the focus on the goal of the actions, can explain discrepancies

in the results.

3.2.2. Neuroimaging of a humanoid robots actions

The preliminary results partially presented here derive from an

international collaboration (Thierry Chaminade, Sarah-Jayne

Blakemore, Chris D. Frith from UCL, UK; Massimiliano Zecca, Silves-

tro Micera, Paolo Dario, Atsuo Takanishi from RoboCasa, Japan,

Giacomo Rizzolatti, Vittorio Gallese, Maria Alessandra Umilt from

Universit di Parma, Italy; manuscript in preparation) aimed at

investigating the involvement of motor resonance during the

observation of a humanoid robot. Using fMRI, local brain activitywas recorded when participants observed video clips of human

and humanoid robot facial expressions of emotions, while partici-

pants rated the emotion (how much emotion in the video, expli-

cit task) or the movement (how much motion in the video,

implicit task). The humanoid robot used for this experiment, WE-

4RII, has 59 degrees of freedom (DOFs), 26 of which were specifi-

cally used for controlling the facial expression executed in this

experiment plus 5 DOFs in the shoulders, important for squaring

or shrugging gestures used in the expression of emotions. A subset

of the facial Action Units (AU, described in Ekman and Friesen,

1978) was chosen for a simplified but realistic reproduction of

the facial expression of emotions used in this experiment (Itoh

et al., 2004).

We were particularly interested in activity in the left ventralpremotor cortex, a region involved in motor resonance that was

found in the main effect of action observation. There was a sig-

nificant interaction between the subjects task (implicit or expli-

cit) and the agent used to display the stimulus (human or robot).

Fig. 3illustrates the source of this effect. The signal increased be-

tween the implicit and explicit tasks was mainly driven by the

robot. This increased response to robot stimuli when subjects

rated the emotionality of the stimulus supports a modulation of

the motor resonance systems response to the humanoid robot

by the task. Our interpretation is grounded on the postulate that

one function of motor resonance processes taking place in infe-

rior frontal cortices is to extract automatically the goal from ob-

served human actions (Rizzolatti and Craighero, 2004). Bottom-

up processes would then be automatic when perceiving humanstimuli, and would show little to no modulation by the task, as



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is the case here in response to human stimuli. In contrast, robot

stimuli would not be processed automatically because the system

has no existing representation of robots actions as is the case

when subjects rated the movement (implicit task). The large in-

crease of activity in the left inferior frontal cortex during presen-

tation of robot stimuli when the task is to explicitly judge

emotion can be understood as forcing the perceptual system to

process robot stimuli as goal-directed, anthropomorphic, actions:

when the task is to explicitly rate the emotion, the subjects

attention is directed towards the goal of the action, the emotion.

The interaction between task and agent would thus derive from

an interaction between bottom-up processes, influenced by the

nature of the agent (automatic for human, not robot), and top-

down processes, depending on the object of attention. If this

interpretation is correct, motor resonance towards artificialagents would be enhanced when the agents actions are explicitly

processed as actions, and not mechanical movements, by the

perceiver.

This finding offers an interesting solution to the issue raised in

the previous section: when asking subjects to pay particular

attention to the relationship between the agents and the objects,

Gazzola et al. oriented their subjects attention to process the ro-

bots movement as transitive goal-directed actions, hence reinforc-

ing a top-down activation of motor resonance. In contrast, Tai

et al.s instructions to carefully observe the agent did not impose

focusing the attention on the goal of the action, hence relying

exclusively on bottom-up processes to activate motor resonance,

that is reduced towards humanoid robots. An important conclusion

with regards to the social competence of humanoid robots there-fore relates to the way they are perceived, either as a mechanical

devices or as goal-directed agents, that would be influenced by

the expectations of the observer.

4. Resonance and humanoid robots design

While robots appear to be pertinent to investigate motor reso-

nance, the last part of this review focuses on the complementary

question: can social cognitive neuroscience, and in the present fo-

cus, the concept of resonance, be used to enhance the social com-

petence of humanoid robots? While complete achievements are

scarce, two lines of investigation are described here: can we build

resonating robots, and could the uncanny valley hypothesis beexplained by the concept of resonance.

4.1. Robots resonating with humans

Artificial anthropomorphic agents such as humanoid and an-

droid robots are increasingly present in our societies, and every-

day use of robots is becoming accessible, as with the example of

Kokoros company simroid, a feeling and responsive android pa-

tient for use as a training tool for dentists, or robotic companions

being introduced for use with children (Tanaka et al., 2007) or el-

derly people. For these robots to interact optimally with humans,

it is important to understand humans reactions to these artificial

agents in order to optimize their design. Studies have addressed

the issue of the form (DiSalvo et al., 2002) and functionalities

(Breazeal and Scassellati, 2000; Kozima and Yano, 2001) a

humanoid robot should have in order to be socially accepted.

Both types of approaches have mostly relied on subjectiveassumptions, such as the need for human traits. It was thus pro-

posed that the design of consumer product humanoids should

balance human-ness, facilitating social interaction, with an

amount of robot-ness so that the observer does not develop false

expectations about the robots emotional capabilities, and prod-

uct-ness so that the user feels comfortable using the robot, and

provided guidelines on how to achieve this balance (DiSalvo

et al., 2002). For example, the face should be wider than tall to

look less anthropomorphic, but have a nose, a mouth, eyes and

eyelids.

But anthropomorphism is not limited to the robots appear-

ance and motion: interactive robots behaviors also matter for

interacting with humans. Robothuman interactions are mas-

sively unidirectional at present. As increasingly complex andautonomous humanoid platforms become available, we believe

that including human-like motor resonance in their behavior

would significantly improve the social competence of their inter-

actions. We recently demonstrated the feasibility of such an ap-

proach (Chaminade et al., 2008; Oztop et al., 2005a). Our

hypothesis was that synchronized sensory feed-back of executed

actions could drive Hebbian learning in associative brain net-

works, forming motor resonance networks from which contagion

of behaviors could emerge. This scenario was inspired by the the-

oretical proposal that motor resonance networks can result from

Hebbian learning of associations between visual and motor repre-

sentations of actions (Keysers and Perrett, 2004), as well as devel-

opmental psychology observations that synchronized action and

sensory feedback are available to neonate during motor babblingwith their hands (Heyes, 2001).

Fig. 3. Top: location of the left ventral premotor cluster in which brain activity was analyzed. Bottom: graphs presenting brain activity in response to human (white) and

robot (grey) agents presented on the right depending on the task (error bar represent standard error of the mean). Note the larger increase between implicit and explicit for

robot than for human stimuli.



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In our system, a simple associative network linked a robotic

hand and a simple visual system consisting of a camera. During a

training phase, the network was fed simultaneously by the motor

commands sent to the robotic hand to perform gestures and by

the visual feedback of the robotic hand. During a testing phase,

the system was presented with the same or new hand postures,

or with hand postures from a human agent. Our results indicated

that some features of human behaviors, such as the ability to per-

form new actions (i.e. not present in the repertoire formed by

training) by imitation, can emerge from this connectionist associa-

tive network (Chaminade et al., 2008). Similar results were ob-

tained with a non-anthropomorphic robotic arm (Nadel et al.,

2004). As is the case for behaviors derived from motor resonance,

this imitation is unconscious, in the sense that the system has

not been designed in order to imitate, but to reproduce the onto-

genic origin of resonance system for testing whether this reproduc-

tion is sufficient to bootstrap a key behavior making use of motor

resonance, imitation.

Building on this proof-of-concept, a similar associative learn-

ing could be used with humanoid robots to develop a realistic

architecture for full-body motor resonance abilities at the core of

the robotic platform, akin to providing the robot with a sensorimo-

tor body schema. This architecture could subtend realistic human

behaviors. For instance, studies of natural interaction between hu-

mans have demonstrated that as a consequence of motor reso-

nance, interacting agents align their behaviors (Schmidt and

Richardson, 2008): two persons walking together in the street syn-

chronize their step frequency unconsciously (Courtine and Schiep-

pati, 2003), and crowds applause synchronously when one starts

clapping at the end of a show (Neda et al., 2000). As bi-directional-

ity is a hallmark of social interactions, implementing bidirectional

coordination of behaviors in humanoid robots by incorporating a

motor resonance framework to the platform may lead to dramatic

improvements of their social abilities, though such a conclusion

awaits demonstration.

4.2. Motor resonance and the uncanny valley

The Uncanny Valley of eeriness hypothesis has served for

years as a guideline to avoid realistic anthropomorphism in robotic

designs for commercial usage. This hypothesis postulates that arti-

ficial agents imperfectly attempting to impersonate humans in-

duce a negative emotional response (MacDorman and Ishiguro,

2006; Mori, 1970). As Toshitada Doi, an official representative

commenting the design of Sonys humanoid robot QRIO, explained

We suggested the idea of an eight year-old space life form to the de-

signer we did not want to make it too similar to a human. In the

background, as well, lay an idea passed down from the man whose

work forms the foundation of the Japanese robot industry, Masahiro

Mori: the valley of eeriness. If your design is too close to human

form, at a certain point it becomes just too . . .

uncanny. So, whilewe created QRIO in a human image, we also wanted to give it little

bit of a spacemanfeel. Nowadays though, people like David Han-

son, founder of Hanson robotics, builds realistic anthropomorphic

robots under the assumption that the uncanny valley is an illusion

caused by the poor quality of aesthetic designs (Hanson, 2005), not

an insurmountable limit.

A speculative explanation for the uncanny valley hypothesis

could be derived from the motor resonance framework described

here. Results from the previous section support the hypothesis

that the neural network subtending resonance results from Heb-

bian learning of associations between visual and motor represen-

tations of actions (Chaminade et al., 2008; Keysers and Perrett,

2004). The simultaneous experience of doing an action and of

perceiving an action during human development is responsiblefor establishing resonance networks, that are in turn used to imi-

tate and understand others actions. The actual processes engaged

when we understand a perceived action can be described as a

competition between various representations of action. Selection

of one representation among many would rely on reducing iter-

atively an error term (called a prediction error) between accumu-

lating evidence about a perceived action and the predictions

derived from competing existing representations of actions in

the resonance network. Lets speculate about the balance be-tween the strength of the representation of an action and the

prediction error when perceiving the same action performed by

a human, a humanoid and an android robot. As the resonance

networkhas been trained by the observation of human actions,

the internal representation of the observed action will be se-

lected by the reduction of the prediction error in the case of hu-

man agent.

We proposed previously that bottom-up processes are re-

duced in the case of humanoids, so that the driving inputs to

the resonance system are less strong than in the case of humans.

Top-down control of resonance to interpret the robots move-

ments as action provides a larger error as a consequence of the

mechanical (i.e. not human) appearance, as suggested by fMRI:

when subjects have to process actions explicitly, in Gazzola

et al. as well as in the results described in Section 3.1.2, we ob-

serve a trend towards increased activity in response to robots

compared to human actions. In the case of contemporary an-

droids, the realistic human appearance triggers bottom-up pro-

cesses as for real humans. But while the stimulus does select

the representation of the correct action, the motor resonance sys-

tem is unable to match its predictions with the incoming infor-

mation because of androids imperfections in form and/or

motion. The ensuing large prediction error signal could give rise

to the feeling of eeriness: the realistic albeit imperfect imperson-

ation of a human acting does not fit existing representations of

human actions.

We have preliminary fMRI data supporting this interpretation.

In an international collaboration to investigate the perception of

androids (Ayse Saygin, Thierry Chaminade, Chris Frith and JonDriver from UCL, UK; Hiroshi Ishiguro and colleagues, Osaka Uni-

versity, Japan; manuscript in preparation), we recorded brain re-

sponses to the perception of actions performed by the android

Repliee Q2, by the human after whom the android was devel-

oped, and by a humanoid robot obtained by removing the an-

droid cover skin. Repetition priming was used to isolate

regions specifically responding to each agents actions. The main

results come from comparing repetition priming results for the

three agents: while the human and robot activate a limited

number of circumscribed regions, the effect for the android is

much larger and widespread across the cortex, as expected from

an error signal: the inability to minimize the error recruits

numerous cognitive processes in order to make sense of the

input.In this interpretation of the uncanny valley, as we get closer to

human appearance, the perceptual system, tuned by design for rec-

ognizing human actions, becomes particular to the tiniest flaws in

the android form and motion. Such a view comforts David Hanson

argument: the uncanny valley is mainly a result of poor aesthetic

design. This is a particularly important line of development in

the perspective of the design of companion robots. By extension,

it is likely that a similar uncanniness will result from to imper-

fect social behaviors of anthropomorphic robots. For example, the

addition of random micro-behaviors in Hiroshi Ishiguros latest an-

droid has proven beneficial to avoid it falling into the uncanny val-

ley (Minato et al., 2004). Thus, our proposal to design

anthropomorphic social behaviors based on motor resonance can

participate to making robots interacting with humans moreacceptable.



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5. Conclusions

The fields of humanoid robotics and of social cognition can both

benefit from mutual exchanges. Robots provide tools to investigate

parameters modulating both behavioral and neural markers of mo-

tor resonance. Using the humanoid robot DB, we have shown that

human-like appearance and motion is sufficient to elicit motor res-

onance. Investigating the brain response to the emotion-express-ing robotic upper torso WE-4RII, weve proposed that while

resonance is primarily a perceptual (i.e. automatic) process when

perceiving humans, it may be more susceptible to the attention

of the observer when perceiving robots. This result could be useful

to frame users expectations and increase robots acceptability. Fi-

nally, weve shown that resonance could inspire epigenetic robot-

ics, in particular the implementation of a body schema. These

reciprocal influences between social cognitive neuroscience and

humanoid robotics thus promise a better understanding of man

robot interactions that will ultimately lead to increasing the social

acceptance of future robotic companions.

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