Brain Research Bulletin 75 (2008) 775–784

Research report

From self-observation to imitation: Visuomotor association on a robotic handThierry Chaminade b,∗ , Erhan Oztop a,b , Gordon Cheng a,b , Mitsuo Kawato a,ba JST-ICORP Computational Brain Project, 2-2-2 Keihanna Science City, Soraku-gun, Kyoto 619-0288, Japanb ATR Computational Neuroscience Laboratory, 2-2-2 Keihanna Science City, Soraku-gun, Kyoto 619-0288, Japan

Available online 14 February 2008

Abstract
Being at the crux of human cognition and behaviour, imitation has become the target of investigations ranging from experimental psychology
and neurophysiology to computational sciences and robotics. It is often assumed that the imitation is innate, but it has more recently been argued,
both theoretically and experimentally, that basic forms of imitation could emerge as a result of self-observation. Here, we tested this proposal ona realistic experimental platform, comprising an associative network linking a 16 degrees of freedom robotic hand and a simple visual system.
We report that this minimal visuomotor association is sufficient to bootstrap basic imitation. Our results indicate that crucial features of humanimitation, such as generalization to new actions, may emerge from a connectionist associative network. Therefore, we suggest that a behaviour ascomplex as imitation could be, at the neuronal level, founded on basic mechanisms of associative learning, a notion supported by a recent proposalon the developmental origin of mirror neurons. Our approach can be applied to the development of realistic cognitive architectures for humanoid
robots as well as to shed new light on the cognitive processes at play in early human cognitive development.
© 2008 Elsevier Inc. All rights reserved.
Keywords: Imitation; Associative learning

1. Introduction
In the course of human development, imitation entails two
key abilities: social interaction and learning of motor skills [29].
Facing an imitator triggers a positive emotional response from
infants around 1 year of age, who later engage in reciprocal
imitation [24]. Motor learning can be achieved with a “lookat-me and do-like-me” procedure more efficiently than through“trial-and-error”, even though these procedures are not mutually
exclusive and are probably used in alternation. Cultural learning,another essential aspect of human cognition, also uses imitation
to spread codes shared by a group within and between generations, a process Tomasello called the ratchet effect [39]. The factthat autism, characterized by abnormalities of social behaviours,
has been associated with impairment in infants’ imitation capacities highlights the putative fundamental role of this behaviour
in normal social development.
Imitation covers a set of behaviours sharing a common factor, the transformation of an observed action into an executed∗ Corresponding author. Present address: Institut de Neurosciences Cognitives
de la Méditerranée, 31, Chemin Joseph Aiguier, 13402 Marseille Cedex 20,
France.
E-mail address: tchamina@gmail.com (T. Chaminade).

0361-9230/$ – see front matter © 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.brainresbull.2008.01.016

action, widely differing in terms of what type of action andwhat part of the action is imitated, or whether the imitator hasaccess to the internal representation of the goal of the model.
It covers a continuum of behaviours ranging from simple, automatic and involuntary action contagion to intentional imitationand emulation [7]. Jacobs and Jeannerod recently emphasizedthat “[imitation] is a folk psychology concept whose boundaries are presently too ill-defined for scientific purposes” [18].
It is difficult to realize the number of complex mechanisms
involved in imitation, from body correspondence to extractionof task-relevant features [4]. Because of this complexity, theunderstanding of imitation benefited from the multi-disciplinary
approach inherent to cognitive neuroscience [37], built on avariety of scientific fields such as experimental psychology andneuropsychology, neurophysiology or computational sciences.
Our aim here is not to investigate or review the whole scope of
imitative behaviours. Instead, we used the opportunity offeredby robotics and computational sciences to test a specific hypothesis. We did not take an engineering stance but a cognitive
science perspective in order to test the hypothesis that automatic and non-intentional imitation of a simple action, or action
contagion, can emerge from the intrinsic properties of a neural associative network fed by spontaneous actions and visualfeedback of these actions available during neonates’ motor babbling.

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Most developmental theories emphasize that social interactions, in particular understanding of other individual’s
intentions, could be first achieved through imitation, yet the discussion on the origin of low-level imitative abilities is often
neglected, referring instead to the possibility of its innateness.
The question of the origin of imitation has indeed been highly
controversial following the seminal paper reporting neonatal
imitation [25]. The finding that ‘neonates between 12 and 21days of age can imitate both facial and manual gestures’ is often
cited as evidence of innate abstract representational systems ofactions. Yet despite its elegance, this finding has been criticizedon two separate fronts, its results and their interpretation [2]. The
poor reproducibility of the neonatal imitation has shed doubtson their validity so that altogether, it has been claimed that only
tongue protrusion has been repeatedly shown to be imitated byneonates [2].
Other lines of evidence suggest that newborn infants comeinto the world with innately specified, though crude, visual
representation of faces [38]. Using 2-dimensional stimuli, it
was found that neonates preferably track a schematic facelike pattern than other patterns consisting of the same facial
features in different, not face-like, arrangements. This led tothe proposal that neonates were provided with innate modules
such as a face-detecting device consisting of a perceptual system sensitive to specific arrangements of 2-dimensional shapes[reviewed in 38]. Recent finding of neonates imitation of oral
gestures in chimpanzees [28] suggests a release mechanism
ability that is likely to have evolved in relation to feeding
behaviours and to be restricted to oral gestures. In an evolutionary psychology perspective, these mechanisms could havebeen positively selected for the advantages they provide toneonates not only in feeding but also in initiating social interactions. It is noteworthy that innate recognition and imitation of
facial and oral gestures in humans facilitate early social interaction with caregivers and are important for social cognitive
development.
Keeping in mind that imitation is not a unitary behaviour butcovers a range of different behaviours, this attractive scenario on
the innate origin of oral and facial gestures imitation cannot be
generalized to other, in particular visible, body parts. Interestingly, another visuomotor ability has been clearly demonstrated
in human neonates less than a month old despite the poor resolution of their visual system: they can control their actions
purposefully in order to bring their hand back in the field ofview, even when it is being pulled by an external force [41].
This shows that babies perceive their hands as objects of particular interest, and can make use of spontaneous arm waving
to build an embodied frame of reference for their actions [41].
The central aspect of our hypothesis is derived from this result.
We propose that in the case of hand movements the temporalsynchrony between the motor command and the corresponding visual (and somatosensory) feedback is sufficient to acquirevisuomotor associations which can sustain early forms of imitation. The observation of another agent would automatically
retrieve the visuomotor association which execution results in asensory input corresponding, in a loose sense, to the observed
one. Such a visuomotor association would thus uphold action

contagion, an early form of involuntary imitation. This developmental path could be particularly important when compelling
evidence about innate mechanisms are absent, as in the case ofhand and finger gestures.
Recent neurophysiological findings on the cerebral bases ofperception, imitation and understanding of actions provide us
with a rich set of results [9,13,17]. Of particular interest are
mirror neurons, activated both when monkeys perform a goaldirected action and when they see the same action performedby an experimenter. These neurons were found in the reciprocally connected ventral premotor region F5 and inferior parietal
region PF [36], and it has been proposed that a similar mirror
system exists in humans. These neurons are the best example
of a motor resonance system, in which not only brain activity
related to both observation and execution of action, but also
behavioural markers of this activity, have a reciprocal influenceon each other. In the most classical behavioural example, observing an action hinders the execution of a different one [22,32].
Key regions for the human imitation are the left inferior parietal lobule [9], possible homolog to the monkey’s PF, and theventral premotor cortex [17], putative homolog to F5 [34]. Bothvisual and motor properties have been reported for these tworeciprocally connected regions. One study showed that parietal
and premotor cortices involved in producing a specific action
are recruited when understanding the precise goal of anotherindividual performing a similar action [8].
A classical view related to synaptic plasticity and learning in
the brain is Hebbian learning (‘when an axon of cell A is nearenough to excite a cell B and repeatedly or persistently takes
part in firing it, some growth process or metabolic change takes
place in one or both cells such that A’s efficiency, as one of thecells firing B, is increased’). In its original form this learning is
not very useful from a computational point of view. However,slight relaxations to the original statement allow one to construct so-called associative memories [30]. The crucial propertyof an associative memory is its ability to retrieve a stored patternbased on a partial representation of it. So these architectures arealso called content addressable memories. Assuming that similar mechanisms are at work in the cerebral cortex, we can thinkof how early automatic forms of imitation ability may emerge.
When the system (a learning robot or an infant) generates motor
commands, the representations of this command and the sensed
effects of the command can be associated through Hebbian-likelearning.
These associations between motor command and their sensory effects are reminiscent of internal models, defined as central
representations of movement which mimic the input–output
relationships of a controlled system, the human body, formovement generation. The model that predicts the sensory consequences of a motor command is referred to as a forward model[26,43]. Conversely, the model that outputs the required motor
command to reach a desired sensory state is called an inverse
model. A computational structure storing input–output relationsof a control system and capable of retrieving the input–output
pair given either the input or the output, as in an auto-associative
memory, is effectively a combined inverse and forward model of
the whole system (e.g. the MOSAIC control architecture [44],T. Chaminade et al. / Brain Research Bulletin 75 (2008) 775–784

that employs explicit multiple paired inverse and forward models
and has been proposed to sustain imitation [42]).
Thus, in the computational framework our hypothesis canbe stated as follows: motor babbling could induce Hebbian-likeacquisition of sensory-motor associations able to sustain early
imitative abilities. The synchrony between motor commands andsensory feedback during motor babbling could allow Hebbianlearning of the associations between the two types of events,
i.e. motor and sensory, in infants. The activation of such multimodal representations when actions from another person areperceived could result in automatic and involuntary productionof the motor output, a behaviour that would be considered asaction contagion. This Hebbian acquisition of sensory-motor
associations and its relation with mirror neurons and imitation
have been proposed elsewhere [15,21]. The proposal that associative learning could provide the developmental link between
automatic imitation and the representation of goal-directed andmeaningful actions by mirror neurons is particularly interesting. Other studies have used Hebbian-like associative learning
during self-observation to bootstrap early forms of imitation in
embodied agents [23].
This bootstrapping of imitation could be particularly important for certain body parts, most notably the hands in the visual
domain and the vocal tract in the auditory domain, whose circuits appear to overlap on the human cortex [3,35]. To assess theextent of imitation features that can be bootstrapped we combined a robotic human-like hand with a minimal visual retina
via biologically inspired associative network to reproduce aninfant’s early visuomotor experiences. Results indicate that thissimple system depicts contagion of finger postures demonstrated
by an external agent, and, more importantly, that it can generalize to unseen hand postures, raising the possibility that thismechanism is at play during early infancy.
2. Methods
2.1. Associative memory and hardware
As detailed in Section 1, accumulating evidence suggests that visual andmotor processing overlap in most aspects of the sensory-motor transformation,
which at the neuronal level can be realized through a Hebbian-like learning.
The goal of the present experiment is to explore to what extent the features of

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imitation, such as the ability to produce unseen postures through generalization,
can be bootstrapped via self-observation and Hebbian-like association. In thenext section, we describe the visuomotor representations that are stored in thehigher order Hopfield (HHOP) network, the associative memory we used forthis study. The details of HHOP network are given in the following sections.
2.1.1. Visuomotor representation: the HHOP encoding
Fig. 1 shows the flow of visual and motor information converging onto HHOP
network forming a visuomotor representation over the array of units making up
the HHOP network. The visual input initiates from a video source. We havecircumvented the requirement for extensive computer vision algorithms (e.g.
skin colour tracking, body segmentation, etc.) by providing a background-freevisual input in an approximately fixed angle. The image processing we appliedemulates a very simple retina: the colour video is converted into greyscale andsmoothed. A bounding rectangle is determined based on the smoothed image,and the contents are scaled to a fixed size set by the HHOP network (in this study:
width 32, height 12). Thresholding and removal of isolated points complete thepreprocessing of the input video resulting in a reduced image, referred to as theretina from now on.
The retina is directly connected to the visual units of the HHOP network,
which receive binary pixels (+1 or −1) in a one-to-one manner, where +1 indicates the existence of a bit at the corresponding location on the retina. The motor
code that is used to drive the Gifu hand is represented by five bits for each finger,
also directly connected to the HHOP network. The units that receive motor input
are referred to as motor units. The representation in the motor units is redundant
as in the current setting, fingers can only be in two states (up or down) which
could be coded by one bit per finger, but will allow additional flexibility in future
developments, for example encoding of joints angles. The motor units of HHOP
receive these representations of motor patterns whose values are +1 or −1. The
visuomotor representation produced by the combination of the retina and motor
inputs form an input pattern (orange square in Fig. 1) which can be either stored
or used to retrieve a stored pattern by the HHOP network. We will often use ‘pattern’ to indicate a visuomotor memory trace, which has been stored or supplied
as a key for the retrieval of a visuomotor memory trace. In most sophisticatedarchitectures, one could revert to a feature space framework and extract a set of
powerful features (e.g. higher order moments) with desirable invariance properties and use those as visual input to the HHOP network. The result would be arobust imitation engine, thanks to the sophisticated feature encoding. However,in this study our concern is not so much to provide robust imitation but rather topresent a simple but yet biologically realistic connectionist framework that may
be thought of as a model for the origin of action contagion. For this purpose wehave avoided complex computer vision and pattern recognition techniques.
2.1.2. HHOP: higher order Hopﬁeld network
The standard Hopfield network [16] is a classical associative memory, whichis composed of units that are fully connected with symmetrical weights. The
computation of the connection strength between units in a Hopfield network
follows a Hebbian-like update rule, and is thus considered as a biologicallyplausible network. Although Hopfield network is suitable for small problems,Fig. 1. Flow of visual and motor information, which form the input pattern when combined together. The whole retina and all fingers are associated to form the input
pattern of the HHOP network.

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Fig. 2. Illustration of the higher order Hopfield network (HHOP) with threeunits i, j and k. The weight wijk represents the higher order effect of units j andk on unit i.

tive memory. The Gifu hand consists of a thumb and four fingers (Fig. 1, left).
The thumb possesses four joints with four degrees of freedom (DOF) while thefingers possess four joints with 3-DOF. One hand contains a total number of
20 joints, encompassing 16-DOF. These DOF closely approximate those of ahuman hand.
A network control framework was developed for maximal flexibility andwork load distribution. It contains three computers as shown in Fig. 3. The
Video Capture computer (C) is connected to a video camera and a video captureboard. The task of C is to capture frames and transfer them to the target machine
H. The colour video is sent at 30 frames/s with a resolution of 320 × 240. The
High Level Coordinator (H) has three main tasks. The first one is to preprocess
the incoming video as described in the previous section (see also Fig. 1). The
second task is to run the HHOP network based on the video processing result.
Finally the last task is to send commands to the Low-Level Hand Control Server(S) that is directly connected to the Gifu hand. Computer S in return implementsa PD servo driving the Gifu hand to the desired postures. In this sense the HighLevel Coordinator is the main imitation system while Capture and Controller
servers (C and S) serve as the input and output channels, similar to the visualand motor pathways in humans.

2.2. Experimental procedure
in its standard form, the performance of the network degrades when the patterns
to be stored are too closely correlated (e.g. overlapping patterns). Preliminary
experiments showed that Hopfield network was not suitable for storing visuomotor patterns since the patterns are highly correlated. Motivated with the factthat the use of higher order units increases the computational power of neuralnetworks [14], we implemented an extension of the Hopfield network using
higher order units (named higher order Hopfield network or HHOP [31]). Here
we present the basic equations that are sufficient to implement the HHOP network on a computer. The HHOP network representation is bipolar (i.e. −1, +1).
Each unit in a HHOP network receives input from all possible products of theother units as illustrated in Fig. 2. Output of a unit (Si ) is given by
Si = sgn

wijk Sj Sk

(1)

jk

where sgn(·) is defined assgn(x) =

−1,

x<0

+1,

x≥0

Denoting the pth pattern to be stored with ξ p , and representing the kth bit ofppattern p with ξk , the connection weight of the product to unit k is calculatedusing
wijk =

1 p p p
ξi ζj ζkN

(2)

p

The running of the network is asynchronous. After initial loading (assignment of Si s), the network is run by choosing a random unit and applying the
update rule (1) until convergence is reached. In practice it is possible to stop
updating at a fixed iteration since usually, several passes for each unit suffices toreach equilibrium with HHOP. The network was iterated four times in all simulations reported here. If the initial loaded pattern is close to one of the stored
patterns then the network state converges to that pattern, called the attractor
pattern. The main advantage gained by using higher order units is the increased
ability to deal with correlated patterns compared with the standard Hopfieldmodel. In addition less iterations are required to reach stable equilibrium states[20].
2.1.3. Experimental platform
We used “The Gifu Hand III” (Dainichi Co. Ltd., Japan; referred to as “GifuHand”1 in this report) as the test-bed for action contagion using HHOP associa-

1 This article includes a word that is a proprietary term. Its inclusion does not
imply it has acquired for legal purposes a non-proprietary or general significance.

2.2.1. Network simulations
A first series of simulations were run off-line on synthetic data to ensure
correct function of the associative network and assess its properties. The aim ofthese simulations was to confirm that HHOP can reliably be used in real-time as
an associative memory bridging video input and controller software that actuates
the Gifu hand. For these simulations the artificial input patterns consisted of
all possible hand postures with 4 fingers (all but thumb) up or down, and the
expected retinal images for the posture coded by the motor patterns. The motor
bits of the input patterns were set to ones if the corresponding finger was up
and to zero otherwise (zeros are treated as −1 when loaded to the HHOP). To
obtain the retinal image, the 32 columns of the retina were divided into four
equal parts. Each part was used to represent a finger, and its six central columns
were filled with one if the motor code of the corresponding finger was one andzero otherwise. The two-pixel gap between fingers was always kept null. Twolines at the bottom were filled with ones in all postures to represent the palm of
the hand. Thus, a total of 24 = 16 synthetic visuomotor patterns were generated
for testing. Two properties of the HHOP network were tested: its robustness withregard to noise and its ability to generalize across patterns.
2.2.1.1. Noise robustness. To assess noise robustness, random noise was uniformly added to the input patterns by flipping the value of n percent of the bits,with n varying from 0 to 100% in 10% increments. Note that the noise contamination is applied to the motor and visual units without distinction. Before
testing, HHOP was trained with the full data set of 16 patterns. Then, for eachnoise level, the recall capability of the HHOP network was tested by loading eachof the 16 patterns and contaminating them with noise at the appropriate noiselevel. The network was then iterated four times (each unit received 4 updates ina random order) after which the overlap with the original (‘clean’) pattern wasrecorded. This process was repeated 100 times for each pattern. The averageover repetitions and patterns gives the noise robustness curve of HHOP, given
in Fig. 5A.
2.2.1.2. Generalization across patterns. To assess the ability to generalize
across postures, a random training set was generated which could have n = 1–15
training patterns. The testing was conducted on the 16 possible hand postures,
16-n of which were not in the training set. For each level of n, 500 randomized
training and testing session were run. If the network can ‘infer’ the motor patterns
corresponding to patterns which are not in the training set, then m, the numberof correctly recalled motor patterns, must be greater than n (m > n). Notice thatafter a pattern not belonging to the training set is loaded into the network, themotor part is randomized, and the network is iterated. A correct functioning
would recover the randomly initialized motor bits such that they reflect the posture represented by the visual bit patterns. In other words, this generalization
tests the ability of the network to reconstruct the motor information on the basisof the visual information of a posture the network has never experienced before.

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Fig. 3. The network framework built around Gifu hand comprises three computers, to capture video (C), to control the Gifu hand (S), and to run the simulation (H),respectively. Arrows indicate the flow of information illustrated in Fig. 2.
Fig. 5B shows the generalization ability of the HHOP network averaged overthe 500 repetitions.
2.2.2. Robotic validation
To assess whether this network depicts action contagion in a real-life situation, we used real hands, either the Gifu hand or other hands (human hands,
wood hand) to provide visual input to the system in the testing phase. Since ourhypothesis is that a network trained by self-observation displays action contagion when observing another individual, we performed training with the robotic
hand: the robot watched its own hand postures while associating the motor commands with the perceived hand postures through the simple retina. The testingis carried on with other hands as well as the robot hand itself. Two propertiesof the HHOP network were tested: its ability to generalize between different
agents and to generalize to new postures.
2.2.2.1. Generalization between agents. Generalization between agents entails
the ability of a network trained by self-observation to respond to other agents,
i.e. other hands (human hands, wood hand) used to form the retinal bit pattern(preprocessed real-time video). Upon presentation of a posture, the motor part ofthe input was set randomly as in the off-line simulations. Then, the network wasiterated, yielding a motor code that was send to the robot for visual inspectionof the action contagion behaviour. In addition, the motor code was recorded

Fig. 4. First (top) and second (bottom) sets of 4 finger postures used in the real-life experiment. For each set, the top row shows the image of the posture shown by
the robotic hand which is recorded by the camera, and the bottom row one example of the visual part of the retina after preprocessing of the video signal.

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Table 1
Percentage of finger configuration correctly reproduced by the HHOP networks
during testing
Set of postures

Gifu hand

Wooden hand

EO

TC

Average

First
Second

100
100

82
99

82
95

98
94

90.7
97.8

The first column indicates which set of postures were used to train the HHOP
networks (simple or complex sets of Gifu hand’s patterns), the second, which set
was used for testing the networks (simple or complex sets of hand patterns), thefollowing columns whose hand was presented when testing the network (Gifuhand, wooden hand, or one of the experimenters’, EO and TC, hand). The lastcolumn gives the average correct recollection rate.
for estimating an average performance value for the generalization. In the realexperiments with the robot, we used two reduced sets of hand postures as humanhands could not perform all of the sixteen postures that were used in the off-line
simulations. Both sets contain four postures as illustrated by Fig. 4.
The first set consists of 4 postures: all fingers flexed, all extended, index
finger extended, and little finger extended. The second set consists of two or
three-finger postures. We created four different versions of each set by producing
the respective postures with the respective agent (Gifu hand, wood hand, eachof the two human hands) four times independently. The training, emulating theself-observation learning, is carried on by letting the Gifu hand generate all
postures from one set, and associating the motor code and the retinal feedbackof its posture perceived in one HHOP associative network. The ability of thisnetwork to generalize to different agents is tested by recording its responses whenpresented with another set of retinal images of hand postures. The other set could
be from the same (Gifu) or another (Wood, Human) hand, and the four versions ofeach set were used for testing in order to take into account the existence of noise
during posture presentation. For each posture, the number of fingers in the correct
position on the robotic hand output was counted after iterating the network fourtimes. For example, if the output of the robotic hand has two fingers in the correct
position and two in the incorrect position, the ratio of correct response is 1/2,
which corresponds to chance level for finger configurations. Finally, for each
set and agent, four networks are formed from each of the four versions of theset, and each are tested across the four versions of set, so that 4 (postures) × 4(trained network) × 4 (tested sets), i.e. 64, observations are converted into thepercentage of correct reproduction given in Table 1.
2.2.2.2. Generalization between postures. A follow-up was conducted to
specifically test generalization to new finger postures. Off-line simulation indicates that networks trained with a small number of gestures could not generalize
to the whole set of remaining patterns. The Gifu hand was used for both training
and testing, allowing the use of all sixteen postures available with four fingers.
Five simple postures were used for training (all fingers up, each of the four finger
up individually), and the trained HHOP networks were tested against a set con-taining all possible configurations except the ones used for training. The fully
closed hand was not used in this experiment. We considered that the posture wascorrectly reproduced when all fingers were flexed or extended correctly as thisshows that the motor code that would yield the observed posture was correctly
inferred. Therefore, the chance level for correct imitation was 6.25% in thisexperiment.

3. Results
3.1. Network simulations
3.1.1. Noise robustness
Hopfield networks construct associative memories by creating attractor dynamics around the stored patterns. Given properconditions, when loaded with a pattern near a stored one, the network will settle to an attractor that will coincide with the stored
pattern. The ability to return a stored pattern given a noisy version of it is thus intrinsic to Hopfield networks including ourhigher order Hopfield network.
The effect of noise on the recall performance of HHOP is
illustrated in Fig. 5A. Statistical analysis shows that the effectof noise on recall performances is highly significant. For levelsof noise between 0 and 30%, recall performance is perfect andstill superior to 90% when the noise amounts to 40%. In otherwords, when 40 out of 100 pixels of canonical hand postures
used for training are randomly flipped, the HHOP still recognizes
more than 9 out of 10 postures presented. Recall performance isreduced at 50% noise (32%) and 60% noise (16%), but bounces
back for increased levels of noise. When the input is at 100%
noise, the input pattern is the inverted version of the original.
The increase in recall performance can be intuitively explained
when we look closer at the workings of HHOP. Assume we haveflipped some number of bits in the input pattern so that the ratioof flipped bits to all bits is p (0 ≤ p ≤ 1). The effective input toa unit is all the double products of the input bit patterns (seeEq. (1)), which are −1 or +1, therefore p2 + (1 − p)2 of the input
channels to each unit remains the same as if no noise was applied.
Thus, for the first iteration, the ratio of number of disturbed
input channels to a unit has a maximum at p = 0.5, where we
may expect to see the worst recall performance. Although, thissimple argument does not consider the subsequent iterations, it

Fig. 5. (A) Percentage of bits correctly recalled as a function of the noise added. Dotted lines correspond to individual postures and straight line with squares to theaverage across the 16 postures. (B) Percentage of generalization (number of new postures imitated divided by the total number of postures not experienced by the
network during training) as a function of the number of postures used for training. Other details are available in Section 2 of the main text.

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gives a reasonable approximation of the noise level where therecall performance is minimum (p ≈ 0.6; see Fig. 5A).
3.1.2. Generalization across patterns
In contrast to their ability to return a stored pattern to thepresentation of a noisy pattern, Hopfield networks are not easy
to craft to generate ‘plausible’ new memories out of the stored
patterns, an essential feature for generalization. It is known thatspurious or ‘ghost’ memories will be created when a given set ofpatterns is stored, but the possibility that these spurious memories would coincide with what we understand as generalization is
not clear. Although, it is possible to understand the ghost memories of the standard Hopfield network as linear combinations of
the stored patterns, it is harder when higher order or more complex memories are concerned as in HHOP network. Therefore,
we tested the ability to generalize in an off-line simulation before
using the network in a real-life environment. Results show that
HHOP network is capable of correctly inferring motor codes
other than those used for training (Fig. 5B). On average, when
8–12 postures are used for training, the network can correctly
reproduce 3.5 postures that were not part of the training set, andit extrapolates to the 16 postures when more than 12 postures areused for training. Between 5 and 7 postures the network shows
limited ability to generalize, reproducing 1–3 new postures.
3.2. Robotic validation
3.2.1. Generalization between agents
The two sets of pattern inputs from the Gifu hand shownin Fig. 4 were repeated four times to investigate generalization
over agents. HHOP network were trained with one of this set,
and tested with similar sets, one identical and three different
versions recorded using the Gifu hand or one of the other hands
available, two humans hands and a wood hand. Results, given in
the first lines of Table 1, reveal an average percentage of 90.7%
of correct finger imitation for the first set and of 97.8% for thesecond set.
It appears that the networks trained by self-observation
demonstrate perfect reproduction when tested with the Gifu
hand, despite being tested with the exact same set as well as
three other versions of the same set. The system also depicts
high level of generalization to other agents. Though the numberof patterns used in the two datasets was similar, generalization toother agents is improved in the second dataset. A likely explanation is the similarity of the retinal image of the closed and open
hand patterns of the first set due to scaling.
3.2.2. Generalization between gestures
Because of the small number of postures available in the previous simulation, it is not fit to test the generalization ability ofthe system. Indeed, the off-line simulation demonstrated positive generalization results when 5 gestures or more are used.
Yet technical and practical limitations forbid the use of extensive tests similar to those used in the off-line simulation. To test
generalization more specifically, a similar experiment was thus
conducted using five simple postures to train the network and all
remaining postures to test it with the Gifu hand. The assumption

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here was that complex postures could be described as combinations of the simple ones. The mean ability to generalize to thepostures presented in the new data set is about 30%, significantly
higher than the chance level of 6.25% for whole hand configuration. There was a huge variability depending on the posturestested. In particular, we found that the network was able to imitate perfectly (100%) the two-finger posture involving the index
and the middle fingers even though it had never produced twofinger postures during training. This indicates that the system isable to reproduce postures it had never experienced before. On
the other hand, there were some postures it was never able toreproduce.
4. Discussion
The aim of the simulations reported here was to test whether
the ability to imitate could emerge from Hebbian-like learning of
sensory-motor associations resulting from self-observation. Perception of the visual consequences of our hands actions would
suffice to acquire internal models of these actions, which could
then be triggered by the observation of other individuals’ hand.
This automatic and involuntary action in response to the perception of another individual’s action is referred to as action
contagion [6], as in contagious yawning. This Hebbian learning of sensory-motor associations and its relation with imitation
have been proposed elsewhere [15,21], but not tested empiricallyon a robotic hand.
We investigated this hypothesis using a robotic hand and asimple associative network. First we will describe the propertiesof the system in relation to the biological inspiration, and argue
that associative network was more suitable for the current simulation than another possible approach. Then we will discuss theresults in relation to the hypothesis, and finally describe possible
extensions of the system.
4.1. Properties of the computational network and robotic
implementation
In addition to testing a hypothesis derived from cognitive
sciences, one long-term goal of this research is to implement
biologically inspired robotic systems. It was thus not possible tolimit this investigation to the off-line simulation aspects, and thesystem had to be implemented in a biologically inspired robotic
system. The hand was chosen for two reasons: in a biologicalperspective, it is one of the effectors clearly visible to infants
from birth and it is possible to relate our results to developmental psychology studies [41]; in a roboticist perspective, it offersa large number of degrees of freedom in a limited space withlimited security or control issues. Nevertheless, the same principles could be applied to most parts of the human body, the mostnotable exceptions being the head and face.
The associative memory we employed is a connectionist
architecture relying on Hebbian-like learning mechanisms withunits resembling neurons, and is thus a credible biological simulation. The experiment setup was voluntarily kept minimal, as away to limit the hypotheses required to describe the homologies
between our artificial and biological systems. For instance, no782

T. Chaminade et al. / Brain Research Bulletin 75 (2008) 775–784

claims are made here on the biochemical mechanisms that may
underlie association in the cortex. Two fundamental properties
were necessary for the associative network (higher order Hopfield net, see Section 2) to be used in this experiment. First, inorder to be usable in real-life environments when implemented
on a robot it needs to be resistant to noise. The ability to return astored pattern given a noisy version of it is intrinsic to Hopfield
networks, and results from the off-line simulations indicate thatwhen 40% or less of the pixels of the input pattern used for training are randomly flipped, the HHOP still retrieves on more than
9 out of 10 postures presented (see Fig. 5A). This robustness tonoise is fit for correctly reproducing gestures. It was also foundthat the network is capable of generalizing, on average to morethan three new gestures when 8 or more are used for training(Fig. 5B). This series of off-line tests of the associative network
ensures that it is robust to noise and supports generalization.
It can thus be used to test our hypothesis that some imitative
abilities can emerge from self-observation.
4.2. Emergence of imitative abilities in the associative
network
We investigated this system’s ability to imitate not in an engineering point of view, but from a cognitive science perspectivein order to acquire knowledge on the possibility for simple imitation abilities – comparable to those described by Piaget inits early developmental stages – to be bootstrapped by experience given the simple (innate) capacities of the system (thenewborn). As explained in the previous parts, efforts were madefor the network to be biologically realistic so that as few a priori
hypotheses as possible are needed.
The main result from the robotic implementation is that thisassociative network trained by self-observation of hand postures
is capable of action contagion, depicting two features of imitation: reproducing actions regardless of the actor, and more
importantly exhibiting one-shot imitation, that is without training the motor code corresponding to a new posture presented
can be inferred, and hence executed. In the first experiment,
we tested the network response when it was tested with either
visual input from itself or from another hand (Table 1). As
expected, the network was 100% correct when it was tested
with its own visual input, and largely above chance (superior to80%) when tested with another hand. This was irrespective of
which set of finger postures and which hand was used, though
there may be individual differences that are beyond the scopeof the present report. In accordance with the theoretical framework which inspired this experiment, our interpretation for thisability is that observation of actions from the self can be used
to associate (near) synchronous visual and motor aspects of anaction by application of Hebbian type of learning rule. The storedvisuomotor patterns can be seen as internal models of actions.
Hands used in the experiment differ in relative size and shapeof fingers, but their general aspect is similar to the Gifu hand.
After preprocessing, the content of the retina can be regarded asa noisy hand input similar to a stored pattern. In other words,
the differences among the hands are treated like noise by the
associative network. Because of robustness to noise, the observa-

tion of another individual’s action can retrieve the visual aspectof the corresponding stored pattern. The ability of the network
to return a stored pattern to the presentation of a noisy patternleads to the retrieval of the original motor code stored along withthe visual code produced during self-observation. If this motor
code is used to drive the hand, we obtain the action contagion
reported in Table 1. This automatic and involuntary behaviour isdefined as action contagion in psychology, and the present result
suggests self-observation could bootstrap this initial step in thedevelopment of imitative abilities.
The experiment testing the capacity of the associative network to generalize to unseen postures in a real-life setting used
only the robotic hand, with the intention to stress the capability togeneralize by checking all possible postures which could not be
performed satisfactorily with a human hand. The results showedthat the overall ability to generalize (∼30%) is significantly
higher than chance (6.25%) but highly variable. Unexpectedly,
some new postures are always reproduced while others neverare. This indicates that the system is able to reproduce postures
it had never experienced before, but did not generalize to thewhole data set with the simple and limited number of postures
provided for training. This result demonstrates that the ability toimitate postures that are not in its existing repertoire of actions
emerged from the associative memory network without any tuning. In accordance with our hypotheses, action resonance could
eventually be used to learn new gestures after some simple visuomotor primitives, in this case individual fingers’ patterns, havebeen used to form an associative memory. It would be interesting
to compare the present results with the development of generalization capability, or more generally of action contagion, in
infants.
4.3. Possible extensions of the imitation system
4.3.1. Acquisition of visuomotor representation of the body
The neonatal ability to recognize faces or to move the hands
so that they remain in the visual field, even when the visual feedback is given through a camera and screen display [41], strongly
suggests innate features detectors for faces and maybe hands too.
In addition, these feature detectors could correspond to brain
areas for which specific activity for faces [Fusiform Face Area
or FFA; see 19 for example] or for body parts [Extrastriate Body
Area or EBA; 10] has been reported. In addition the FFA wasactivated during face perception in 2-month-old infants using
Positron Emission Tomography (PET [40]), and a recent study
reported different electrophysiological response to normal andscrambled bodies in 3-month-old infants, suggesting that very
young infants already recognize faces and bodies [12].
The visual processing of information and in particular thedifficult computational problem of the rotation of point of view
when watching hands from self and from others, which constitutes one of the main difficulties for modelling imitation in an
associationist framework, was largely simplified by the segmentation of the visual input. On the basis of very young infants’abilities, we postulated that their visual system could segment
the visual field so as to isolate a region of interest on the basisof its intrinsic features, in our case a hand. Alternatively, someT. Chaminade et al. / Brain Research Bulletin 75 (2008) 775–784

motor control schemes, such as proposed for reaching [33], could
be extended for viewpoint independent hand posture control.
Thus, the issue of the origin of mechanisms used to segment
visual input and isolate visible body parts is still opened. It
is theoretically possible to use a similar visuomotor associative network approach using motor babbling and first-person
perspective visual feedback to investigate whether it can learn
to recognize its own body parts [23]. As for imitation, selfobservation could bootstrap recognition of visible body parts.
4.3.2. From posture to complex action
Considering the attraction of neonates to dynamic stimuli[11], we believe the extension of the current network to dynamic
actions is crucial to further explore the current hypothesis ina biologically valid framework. Dynamic features may also
facilitate the acquisition of a body schema through visuomotor
associative learning, an hypothesis that could be investigatedwith our model. Dynamic actions require the addition of time
as a variable to the network as the current system does notconsider the timing of events, and uses static sensory stimuli. More widely explored, in particular in robotics, is the useof motion, the time variation of visual stimuli, as input forimitation [1,23]. A more general system would need to learn
spatio-temporal relations over the sensory stimuli, for example
by utilizing spatio-temporal associative memories [5,27]. However, the use of these networks for complex and highly correlated
set of movement patterns is a challenging task that constitutes alogical extension of the current approach.
Here we assumed that the network would be in the learning phase first, and then in the testing phase. In a biologicalsetting learning and testing ensue in an interleaved manner.
Additional iterations, similar to reciprocal imitation games during development, could improve the efficiency of the system.
We can speculate on the recursive learning possibility provided
by the generalization ability. For example, learning visuomotor patterns A and B by self-observation provides the ability
to imitate a novel action C. Then by self-observation associative learning of C, the repertoire of visuomotor patterns can be
expanded to store A, B and C. This expanded network could thenallow more new actions D, E to be imitated and learnt. It would
be interesting to expand the one-iteration of generalization
explored in this article by using such a recursive learning mechanism, which would illustrate a “ratchet effect” at the level ofindividuals.
5. Conclusion
This report emphasizes that robotic systems are suited fortesting hypotheses about human visuomotor development byshowing that a biologically realistic neural network coupledwith a simple visual system and a motor apparatus is capableof producing imitative skills. The current study is the first step
towards an imitative agent that ‘lives’ in social learning loop,
where the recursive nature of imitation can be expressed by theagent, better emulating the learning cycle of a growing infant
in a socially active environment. The works building on thisstudy then could help answer questions about imitation includ-783

ing the self-observation versus innate interpretations of infants’imitative abilities.
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