An Embodied Computational Model of Social Referencing

Andrea Lockerd Thomaz, Matt Berlin, Cynthia BreazealMIT Media Lab, 20 Ames Street

Cambridge, MA USA

Abstract

Social referencing is the tendency to use the emotionalreaction of another to help form one's own affective appraisalof a novel situation, which is then used to guide subsequentbehavior. It is an important form of emotional communicationand is a developmental milestone for human infants in theirability to learn about their environment through social means.In this paper, we present a biologically-inspiredcomputational model of social referencing for our expressive,anthropomorphic robot that consists of three interactingsystems: emotional empathy through facial imitation, a sharedattention mechanism, and an affective memory system. Thismodel presents opportunities for understanding how thesemechanisms might interact to enable social referencingbehavior in humans.

IntroductionWe have implemented an embodied computational

model of social referencing to allow our robot to learn howto form its own affective appraisals of novel objects in real-time from natural interaction with a human partner. Socialreferencing represents a new channel of emotionalcommunication between humans and robots, one in whichthe human plays a central role in shaping and guiding therobot's understanding of the objects in its environment. Inaddition, we believe that social referencing will be a criticalskill for robots that work with and learn from people in thehuman environment. Specifically, such robots must be ableto leverage their interactions with humans to safely andefficiently learn about their environment and people, muchof which will be novel to them.

Our implementation is heavily guided by recent scientificfindings related to this social referencing phenomenon ininfants. Embedding our computational model in anembodied, socially interactive robot provides a uniqueopportunity to explore within a controlled behavioralcontext how scientific theories and mechanisms mightinteract to give rise to social referencing behavior in humaninfants.

Inspiration from Human InfantsFor humans of all ages, social referencing is an

important form of socially guided learning where oneperson utilizes another’s affective interpretation of a novelsituation in order to formulate one’s own interpretation andto guide subsequent behavior (Feinman, 1982). Thisbehavior arises under conditions of uncertainty andambiguity when one’s own intrinsic appraisal processescannot be used (Campos & Stenberg, 1981).

Given the complexity of the real world, infants areconstantly confronted with new situations, objects, andpeople. Social referencing is an important skill that allowsinfants to efficiently and safely learn how to handle novelsituations from others. Social referencing emerges withinthe first year of life, whereby infants learn through a processof emotional communication how to feel about a givensituation. They then respond to the situation based on thisemotional state (Feinman et al., 1992). For instance, theinfant might approach a toy and explore it upon receiving apositive message from the adult, or avoid the toy uponreceiving a fearful message (Hornik & Gunnar, 1988).

To perform social referencing, an infant must be able toaccomplish several distinct social-cognitive prerequisites(Feinman, 1982). Each is a critical milestone for the infant’scognitive and social development. First, the infant must beable to understand the emotional message of another,namely what is the caregiver's affective state? At 2 to 3months, infants begin to discriminate the facial expressionsof others and respond to them with smiles and frowns oftheir own (Trevarthen, 1979). By 6 months of age, infantsare able to respond appropriately to the expressed emotionsof others. This is also called emotion contagion, a processby which the caregiver's emotional expression influencesthe infants own emotional state and subsequent behavior(Feinman, 1982).

Second, the infant must be able to identify the referentof the communication, namely what is the caregiver'saffective state about? Infants first demonstrate the ability toshare attention with others at 9 to 12 months of age, such asfollowing the adult's gaze or pointing gestures to the objectthat they refer to (Baron-Cohen, 1991; Butterworth, 1991).

Third, the infant must be able to remember affectiveappraisals and incorporate these appraisals into its behavior,namely what is the emotional content of the object? By 9months, infants exhibit the ability to evaluate theconsequences of predicted outcomes before responding(Feinman, 1982). Further, these appraisals persist to regulatehow the infant interacts with the stimulus in the future andin different contexts.

Finally, the information of these first three systems isintegrated within a behavioral context. The infant mustextract the intentional nature of the affective informationfrom the adult's expression and associate this appraisal tothe specific referent. Thus, the infant begins to understandthat the expressed emotion is about something in particularand can use this to form his or her own appraisal of thenovel stimuli (Baldwin & Moses, 1994).

Robot PlatformOur computational model has been implemented and

tested on our research platform, Leonardo. “Leo” is ananthropomorphic robot with 65 degrees of freedom that hasbeen specifically designed for expressive social interactionwith humans (Fig. 1). The robot is able to interact andcommunicate with people through speech, vocal tone,gestures, facial expressions, and simple objectmanipulations.

Figure 1: The Leonardo robot cosmetically finished,with robotic underpinnings exposed, and in simulation.The same cognitive-affective architecture drives both thephysical and virtual robot.

Perceptual InputsThe robot has both speech and visual inputs. The vision

system has multiple cameras (on-board and environmental)and segments humans and the robot's toys (e.g., coloredbuttons and balls, stuffed animal toys, etc.) from the visualscene. The vision system recognizes pointing gestures anduses spatial reasoning to associate these gestures with theirobject referent (Breazeal et al., 2004). A head pose trackerbased on the WATSON adaptive tracking system (Morencyet al., 2002) is also used to assess the human's object ofattention. The system uses adaptive view-based appearancemodels to track the position and orientation (six degrees offreedom) of a person’s head, and has a reported angularerror of less than three degrees. A facial feature trackingsystem developed by NevenVision Corporation is used totrack 22 nodes of affectively salient facial features. This isimportant for recognizing the facial expression of a personreacting to objects in the environment.

Leonardo also responds to vocal stimuli. Our speechunderstanding system uses Sphinx-4, an open-source, Java-based speech recognition system (Lamere et al., 2003).Speech is used to support instrumental communicationbetween the human and the robot (i.e., telling the robot whatto do), as well as an affective channel (i.e., conveying thegoodness or badness of things). Using a simple wordspotting mechanism, we match the human’s spokenutterances containing emotive words with specific affectiveappraisals (e.g., “Leo, Elmo is your friend" maps to positivevalence, "The bucket is bad" maps to negative valence,“Leo, this is the fish” maps to neutral, etc.). Positiveappraisal utterances are assigned a high valence value, andnegative utterances a low value.

Additionally, Leonardo tracks vocal intonation usingthe Praat phonetic analysis toolkit (Boersma, 1993). Ourearlier work has shown that certain prosodic contours are

indicative of different affective contents (Breazeal, 2002),confirming the findings of (Fernald, 1989). Following thisapproach, we use pitch mean and energy variance to classifythe affective prosody of an utterance along valence andarousal dimensions (Fig. 2). Our previous systems haveused these features to attain affective classificationaccuracies of 70-80%.

Cognitive-Affective ArchitectureOur computational architecture is designed to explore

and exploit the ways in which affective factors influenceand interact with the cognitive elements of the system.Emotion mechanisms serve a regulatory role --- biasingcognition, perception, decision-making, memory, and actionin useful ways. This type of dual architecture, with mutuallyinfluencing systems of cognition and affect, is gainingscientific support for its role in enabling living creatures tolearn and behave intelligently within complex, unpredictableenvironments given limited resources (Damasio, 1994;Ortony et al., 1988).

Cognitive The cognitive system extends the C5 agentarchitecture, originally designed for use with animatedsynthetic characters (Blumberg et al., 2002). The frameworkis inspired by ethological and psychological models ofbehavior. The cognitive system has various modulesresponsible for the robot's perception, object tracking,memory, attention, behavior arbitration, and motorcoordination (Fig. 3). The perceptual system extracts visualand auditory features from the robot’s sensory systems andbinds them into discrete object representations, called objectbeliefs that are tracked through time. For example, visualinformation about a particular toy such as its location, color,shape, size, and label are merged to form one coherentbelief about the existence and state of that toy. Objectbeliefs are used in conjunction with other internal stateinformation (such as motives) to bias action selectiondecisions based on a behavior-based competitive actionselection mechanism. Ultimately, behavior is reduced to

Figure 2: Arousal and Valence extracted fromvocal prosody.

motor commands to control the physical body (or to animatea simulated graphical model of the robot).

Affective The robot's affective system is based oncomputational models of basic emotions as described in(Breazeal, 2003) (inspired by Kismet, the first socio-emotively interactive robot). In humans, emotions seem tobe centrally involved in appraising environmental andinternal events that are significant to the needs and goals ofa creature (Plutchik, 1991; Izard, 1977). Several emotiontheorists posit an appraisal system that assesses currentconditions with respect to the organism's well-being, itsplans, and its goals (Frijda, 1994).

Our model of emotions includes a simple appraisalprocess based on Damasio's theory of somatic markers(1994) that tags the robot's incoming perceptual and internalstates with affective information, such as valence (positiveor negative), arousal (high or low), and whether or notsomething is novel. In a sense, the affective system providesthe common currency with which everything can bereasoned about.

The robot's affective system as currently implementedis a two-dimensional system (valence and arousal).Describing emotions along at least these two independentdimensions is widely accepted in psychological literature(Thayer, 1989; Watson & Tellgen, 1985). Many factorsinfluence Leo's emotional state, but in the context of social

referencing these include emotional communication with thehuman and remembered affective appraisals of objects. Therobot attends to two channels of human affect: facialexpression and vocal intonation.

In order for affect to serve as a useful communicationdevice, the system needs also to express its internal state ina way that is understandable to the human partner. Thistransparency of internal state helps the human help therobot. The robot constantly needs to regulate its stimuli andbehavior to maintain a desirable internal state (a moderatelevel for both arousal and valence). Given an active emotiveresponse and corresponding affective state, thecorresponding response tendencies are recruited withinmultiple systems (e.g., eliciting specific kinds of expressiveand behavioral responses) for coping with the situation.Plutchik calls this stabilizing feedback process behavioralhomeostasis (Plutchik, 1984). When the robot expresses itsinternal state, the human is able to intuitively assist therobot in this regulation process (as demonstrated in ourearlier work with Kismet). Leonardo conveys emotionalstate primarily through facial expressions, blendingcontinuously between seven facial poses that characterize itsemotional expression space. Additionally, the emotionalstate influences behavior. For example, in a socialreferencing scenario, if a novel object is associated withpositive affect, the robot enters into a positive emotive state,and tends to explore or interact with the toy. If a toy is

Figure 3: Overview of the cognitive-affective architecture.

associated with negative affect, the robot enters into anegative emotive state and tends to avoid the toy (a fearresponse) or reject it (a disgust response).

ImplementationOur computational model of social referencing implementsthree systems - an imitation-based emotion-empathy system,a shared attention system, and an object-based affectivememory system - each addresses the following issues inturn:

• What is the caregiver's affective state?• What is the caregiver's affective state about?• What is the emotional content of the object?

Understanding the Emotional MessageThe first challenge in social referencing is to understand

the emotional message from the human. Specifically, whatis the caregiver's affective state?

To address this, the robot uses a simulation-theoreticapproach to understanding the affective content of facialexpressions (Davies & Stone, 1995). A simulation theoryaccount posits that infants learn to decode emotionalmessages conveyed through facial expressions byleveraging their early facial imitation capability to bootstrapemotional empathy. For instance, Andrew Meltzoff’sexperiments support the finding that very young infantshave the ability to imitate facial expressions (Meltzoff,1996) (this is an innate ability that becomes moresophisticated over time). Other experiments have shown adual affect-body connection whereby posing one’s face intoa specific emotive facial expression actually elicits thefeeling associated with that emotion (Strack et al., 1988).Hence, imitating the facial expressions of others could cause

the infant to feel what the other is feeling, thereby allowingthe infant to learn the association of observed emotiveexpressions of others with the infant’s own internal affectivestates. In this way, infants learn the affective meaning ofemotive expressions signaled through another person’sfacial expressions and body language by a process ofempathy.

We argue that robots can use a similar mechanism tounderstand people at an affective level. In our model,Leonardo has an innate propensity to imitate the facialexpression of the person it is interacting with.

To do so, we have implemented Meltzoff and Moore’sActive Intermodal Mapping model for early infant facialimitation (1997). In an imitative interaction where thehuman initially imitates the robot’s expressions, Leonardolearns an intermodal representation (in the robot’s facialmotor coordinates) of the observed facial expression (invisual coordinates) using a neural network (Breazeal et al.,2005). Once this mapping is learned, the robot can imitatethe human’s expressions by executing a search over aweighed blend space of its basis facial poses to bestapproximate the intermodal representation of the human’sexpression (see Figure 4).

Once the robot can imitate the facial expressions ofothers, it can use its own motor representation of facialexpressions to learn the affective meaning of emotiveexpressions generated by the human. To do so, we haveimplemented the dual body-affect pathway by which thefacial expression of the robot elicits the correspondingaffective state (i.e., arousal and valence) associated with thatexpression. The robot learns to associate its internalaffective state with the corresponding observed expressionto learn the affective meaning of the human’s facialexpression.

Thus, through this “empathic” or direct experientialapproach to social understanding, the robot uses its owncognitive and affective mechanisms as a simulator forinferring the human’s affective state as conveyed throughfacial expression.

A Shared Attention MechanismThe second challenge in social referencing is for the

robot to determine what the caregiver's affective reaction isabout. This is resolved by Leonardo’s shared attentionmechanism.

Previous computational models for joint attention havefocused on deictic gaze or referential looking - defined byButterworth as “looking where someone else is looking”(1991). For instance, Scassellati explored socialunderstanding on robots with joint visual attention and arobot that imitates only the movement of animate entities(2001). In contrast, our approach follows that of Baron-Cohen where shared attention is explicitly represented as amental state of appreciating what the other person’s interestis about (Baron-Cohen, 1991), which is critical if affectiveappraisals are to be attached to specific referent objects.Hence, in our model, referential focus is distinct from gazedirection and the robot’s current attentional focus.

To implement shared attention rather than referentiallooking, the robot’s attentional state must be modeled with

Figure 4: Tracked facial features of the human (top-left)are represented in the terms of the robot’s own motorsystem (the intermodal representation, top-right). Themotor system does a search over weighted blend space ofits basis facial poses (lower-left) to match the intermodalrepresentation (lower-right - is what the observer sees).

two related but distinct foci: the current attentional focus(what is being looked at right now) and the referential focus(the current topic of shared focus, i.e., what communication,activities, etc. are about). Furthermore, the robot must notonly have a model for its own attentional state, but it mustalso have a model for the attentional state of the human.Thus there are three foci of interest: the robot's attentionalfocus, the human's attentional focus, and the referentialfocus shared by the two.

To compute the robot's attentional focus, Leonardo's

attentional system computes the level of saliency (a measureof “interest”) for objects and events in the robot'sperceivable space (Fig. 5). The contributing factors to anobject’s overall saliency fall into three categories: itsperceptual properties (its proximity to the robot, its color,whether it is moving, etc.), the internal state of the robot(i.e., whether this is a familiar object, what the robot iscurrently searching for, and other goals), and socialreference (if something is pointed to, looked at, talkedabout, or is the referential focus). For each item in theperceivable space, the overall saliency at each time step isthe result of the weighted sum for each of these factors(Breazeal & Scassellati, 1999). The item with the highestsaliency becomes the current attentional focus of the robot,and also determines where the robot's gaze is directed(Breazeal et al., 2001). The gaze direction of the robot is animportant communication device to the human, verifying forthe human partner what the robot is attending to andthinking about.

The human's attentional focus is determined by what heor she is currently looking at. Leo calculates this using thehead pose tracking data, assuming that the person's headorientation is a good estimate of his/her gaze direction. Byextrapolating from the person’s gaze, the shared attentionsystem determines which (if any) object is the attentionalfocus of the human's gaze.

The mechanism by which infants track the referentialfocus of communication is still an open question, but anumber of sources, such as word learning studies, indicatethat looking time is a key factor (Baldwin, 1994; Bloom,2002). For example, when a child is playing with one objectand hears an adult say “It’s a modi”, the child does notattach the label to the object the child happens to be lookingat (which is often the adults face!). Instead the childredirects his or her attention to look at what the adult islooking at, and attaches the label to that object.

To robustly track the referential focus, we use a simplevoting mechanism to track a relative-looking-time for eachof the objects in the robot’s and human’s sharedenvironment. An object receives x votes for each time stepthat it is the attentional focus of either the human or therobot; it loses y votes for each time step that it is not thecurrent focus; and, it loses z votes when another object is theattentional focus of either the human or robot (x, y, and z aredetermined empirically). The object with the highestaccumulated relative-looking-time is identified as thereferent of the communication between the human and therobot (see Fig. 6).

As a concrete example, Fig. 7 shows the robot andhuman sharing joint visual attention. The robot has trackedthe human's head pose and pointing gesture to determinethat this object is the human’s attentional focus. This in turnmade this object more salient to the robot and therefore therobot's own attentional focus. Both of which thereby castthat object as the referential focus as well.

Affect and MemoryThe final challenge in social referencing is

remembering affective appraisals for familiar objects,learning appraisals for new objects, and incorporating theseFigure 7: Leonardo sharing attention with the

human about the Elmo doll.

Figure 6: Shared attention mechanism schematic.

Figure 5: Saliency of objects and people arecomputed from several environmental and socialfactors.

appraisals into the robot's behavior. Specifically, the robotmust determine the emotional content of the object.

Recent embodied theories of cognition have identifiedpervasive links between physical embodiment, affect, andmemory. Barsalou discusses a number of social embodimentstudies revealing the interdependencies among these factors(Barsalou et al., 2003). For instance, experiments show thatmanipulating people’s face or body posture into a positiveor negative pose affects their memory performance. Peoplecan more accurately recall events that are congruent withtheir body posture (e.g., happy/angry posture facilitatesrecall of happy/angry events). Our memory model isdesigned to capture this relationship between the body,affective state, and memory.

Leonardo’s object memory system allows Leo to formand maintain long-term object memories, and to integratethese memories tightly with his behavior. As discussedbriefly above, Leonardo's cognitive system manages currentobject beliefs, as well as a set of long-term object memories,called object templates. Each object template encodes a setof expectations about perceptual evaluations for a particularobject. Object beliefs are short-term structures inspired bymodels of working memory, while object templates arelong-term structures inspired by concepts from prototypetheory. Thus, object beliefs encode what a particular objectis like right now, and object templates encode what thisobject is usually like.

Object templates can be created and revised from objectbeliefs, and conversely, exemplar object beliefs can becreated from object templates. This latter process of“imagining” objects from their templates is particularlyuseful in searching behaviors for a desired target, and inreasoning and inference activities. Object templates can beused to fill in information about absent objects such asvisualizing a face from a name, or to attach additionalinformation to objects in the perceptual environment such asremembering a name associated with a particular face.Object templates are revised intermittently, and the revisionprocess is triggered by various events of interest to therobot. For example, one of these “remembering episodes”could be triggered by the human naming a particular objector by the successful completion of an object-directed action.

In the context of social referencing, a triggering eventfor a “remembering episode” happens when the robot’saffective state experiences a significant change. Forinstance, when Leo’s affective state becomes significantlypositive or negative, he is biased to associate that affect withthe object that is his referential focus (described in theprevious section). This in turn revises his persistent memoryof the object to reflect his current emotional state.

Initially the robot does not have a general model forappraising new objects. After a number of object appraisalexperiences acquired over social referencing interactions,the affective memory system trains a Radial Basis Function(RBF) model mapping the continuous input features ofobjects (size, brightness, hardness) to the affective appraisal(arousal and valence). Once this model is built, the affectivememory system is then able to appraise novel objects basedon past experience (which can be revised throughsubsequent interaction with that object). The training

examples that are collected to learn this model come fromthe robot’s social referencing interactions with a human.

This affective tagging mechanism is inspired byDamasio's theory of somatic markers (1994). Emotionalcontext is stored with the robot’s memories of the objects inits environment using “affective tags” (arousal and valencein our implementation). Each perceived object is thereforetagged either with remembered affective information, whichin turn influences the robot's mood and elicits mood-congruent behavior. In related work, the QRIO robotdemonstrates affective memory, attaching long termaffective appraisals to people based on past pleasant orpainful interactions with them (Sawada et al., 2004).

Social Referencing BehaviorGiven these three elements of the social referencing

model, we now present the interaction scenario where theimitative capability, the attentional system, and the affectivememory interact to bootstrap the robot’s ability to engage insocial referencing.

We have implemented and demonstrated ourcomputational model of social referencing in the followingscenario. Leonardo can attend to the human or to any of anumber of colored toys in the environment. The human canpick up the objects, move them around, teach Leonardo theirnames, and influence Leonardo's appraisals of the objects byemotionally reacting to them. The robot's ability to imitateand communicate emotionally, along with its sharedattention capabilities, long-term memory, and associativeobject appraisal mechanism, all interact such that socialreferencing behavior emerges from the interaction betweenrobot and human.

In a typical scenario, the robot’s attention system drawsLeonardo’s gaze to different salient objects in its localenvironment. As a result, Leonardo demonstrates visualawareness of the objects and people nearby, favoring to lookat those that are the most salient, such as brightly coloredtoys and people close enough to Leo that the robot can lookat their faces. As perceptual stimuli filter through the robot’sfocus of attention and their features become bound intoobject beliefs, they are matched against Leo’s memory offamiliar objects as represented by object templates. Familiarobjects are tagged with affective information, biasing theemotion system to activate a specific emotive responsetoward that object. Consequently, Leo favors toys withpositive associated affect, and tends to shy away from thosewith negative associated affect.

When confronted by a novel object, a new belief objectis generated that cannot be matched to an existing objecttemplate. The object appraisal tags the object with novelty,which biases the emotion system to evoke a state of mildanxiety (a mildly negatively aroused state) as a response tothe uncertainty. Leonardo’s face expresses a state ofheightened arousal as it looks upon the novel object. Abehavioral component of Leonardo’s “anxious” response isan increased tendency to look to the human’s face. Thehuman notices Leonardo’s initial reaction to the unknownobject and decides to familiarize Leonardo with the object.She picks up the object and shares her reaction to it withLeonardo.

The shared attention system determines the robot'sfocus of attention, monitors the attentional focus of thehuman, and uses both to keep track of the referential focus.The fact that the human is gazing and reacting toward thenovel toy draws Leonardo’s attentional focus to it as well.By computing relative-looking-time, the novel object isestablished as the referential focus. This allows the robot toshift its gaze and attentional focus to gather informationabout this object while maintaining the correct referentialfocus. For instance, Leonardo looks to the human's face(triggered by the “anxious” response) thereby allowing Leoto witness her emotional response, and also to look back tothe novel toy to share attention with her about the referent.

As Leonardo’s attentional focus shifts to the human(while maintaining the novel object as the referential focus),the robot extracts the affective signal from her voice byanalyzing her vocal prosody for arousal and valence levels,and processes her speech for certain emotive key words andphrases. The facial imitation system causes the robot tomimic the human's facial expression, which in turn elicits acorresponding emotional response within the robot.

The significant change in the robot’s internal affectivestate triggers a “remembering episode” within the appraisalsystem, thereby creating a new object template to beupdated. The robot’s emotive state is used as the affectivetag for the referential focus and therefore is bound to thenovel object. Thus, the novel object is appraised withsocially communicated affective information and committedto long-term memory with that object.

Once the robot forms an object template and knowshow to affectively appraise the toy, that appraisal gives riseto the corresponding emotive state and behavioral responsewhenever that toy is reencountered in the future. The robot'semotive response towards that toy will persist to futureinteractions when the toy is visually presented, or even justverbally mentioned (provided the robot has been taught theobject’s name), given the existence of an object template forit with appraisal and label attributes.

Future WorkWe have detailed our implementation of a

computational model of social referencing that follows asimilar developmental story to that of human infants. Therobot's facial imitation capabilities help it to recognize thehuman's emotive expressions and learn their affectivemeaning. The addition of a shared attention mechanism andaffective memory allows the robot to associate the affectivemessages of others with things in the world. This is animportant milestone towards building robots capable ofsocial understanding in the affective and referential realms.

Social referencing is a promising mechanism throughwhich a human can influence the robot's early interactionswith a novel object. In ongoing work, we are interested inexploring how the robot’s initial appraisal of the object canbe refined through further experience. As the robot gainsexperience through direct interaction with the object, it willsolidify an appraisal of the object that may differ from itsinitial evaluation (as communicated by the human). Thismay be an important process in the evolution of therelationship between the robot and the human, as the human

moves from being a teacher to being a collaborative partner.Instead of simply accepting the human's appraisals of novelobjects, the robot may begin to negotiate these appraisals oreven offer guidance to the human directly (as observed byinfants 18 months old).

The robot's direct experience with the objects in itsenvironment may also offer the opportunity for reflectionabout the quality of the human's guidance. People providingconsistently accurate appraisals may come to be seen astrustworthy teachers, whereas people who providemisleading appraisals may come to be trusted less. Theability to identify trusted sources of information in theenvironment may provide important leverage to the learningsystem.

One particularly interesting application of the robot’ssocial referencing machinery is in the development ofempathic behavior. For example, if the robot notices that theperson is upset, he might try to give the person a toy that therobot knows the person likes in an effort to improve theirmood. For the robot, keeping track of the human's appraisalsof the environment as distinct from its own may be animportant first step towards the understanding of socialidentity and a pre-cursor to false-belief capabilities.

ConclusionSocial referencing represents a new channel of

emotional communication between humans and robots,which allows the human to actively shape the robot'sunderstanding and exploration of its environment. We havepresented a computational model of social referencing forsociable robots. Our implementation, inspired by infantsocial development, addresses three key problems:understanding the caregiver's affective state, rememberingemotional appraisals of objects, and identifying the referentof the affective communication. The first of these problemsis handled by an emotional empathy system bootstrappedthrough facial imitation, the second by an affective memorysystem, and the third by a social attention mechanism thatexplicitly models the attentional focus of both the humanand the robot. These three systems interact to bring aboutsocial referencing behavior in an expressive, embodiedrobot.

AcknowledgmentsThe work presented in this paper is a result of the ongoingefforts of the graduate and undergraduate students of theMIT Media Lab Robotic Life Group and our collaborators.Particular thanks go out to Jesse Gray for his involvement inthis project. Stan Winston Studio provided the physicalLeonardo robot. Vision algorithms were provided by Louis-Phillipe Morency, Trevor Derrell, and the NevenVisionCorporation. Leonardo's architecture is built on top of theC5M code base of the Synthetic Characters Group at theMIT Media Lab, directed by Bruce Blumberg.

ReferencesBaldwin, D. and Moses, J. (1994), “Early understanding of

referential intent and attentional focus: Evidence fromlanguage and emotion,” in Children’s Early

Understanding of Mind, C. Lewis and P. Mitchell, Eds.New York: Lawrence Erlbaum Assoc.

Baron-Cohen, S. (1991), “Precursors to a theory of mind:Understanding attention in others,” in Natural Theories ofMind, A. Whiten, Ed. Oxford, UK: Blackwell Press.

Barsalou, L. W., Niedenthal, P.M., Barbey, A. and Ruppert,J. (2003), Social Embodiment, The Psychology ofLearning and Motivation.

Bloom, P. (2002), “Mindreading, communication and thelearning of names for things,” Mind and Language, vol.17, no. 1 & 2.

Blumberg, B., Downie, M., Ivanov, Y., Berlin, M., Johnson,M. and Tomlinson, B. (2002), “Integrated learning forinteractive synthetic characters,” in Proceedings of theACM SIGGRAPH.

Boersma, P. (1993), “Accurate short-term analysis of thefundamental frequency and the harmonics-to-noise ratioof a sampled sound,” in Proceedings of the Institute ofPhonetic Sciences.

Breazeal, C. and Scassellati, B. (1999), “A context-dependent attention system for a social robot,”Proceedings of the Sixteenth International JointConference on Artifical Intelligence (IJCAI 99).

Breazeal, C. (2002), Designing Sociable Robots.Cambridge, MA: MIT Press.

Breazeal, C. (2003), “Emotion and sociable humanoidrobots,” International Journal of Human ComputerStudies, vol. 59.

Breazeal, C., Buchsbaum, D., Grey, J., and Blumberg, B.(2005), “Learning from and about others: Toward usingimitation to bootstrap the social competence of robots,”Artificial Life, vol. 11.

Butterworth, G. (1991), “The ontogeny and phylogeny ofjoint visual attention,” in Natural Theories of Mind, A.Whiten, Ed. Oxford, UK: Blackwell Press.

Damasio, A. (1994), Descartes Error: Emotion, Reason, andthe Human Brain. New York: G. P. Putnam and Sons.

Davies, M. and Stone, T. (1995), “Introduction” in FolkPsychology: The Theory of Mind Debate, M. Davies andT. Stone Eds. Cambridge: Blackwell.

Feinman, S., Roberts, D., Hsieh, K. F., Sawyer, D. andSwanson, K. (1992), “A critical review of socialreferencing in infancy,” in Social Referencing and theSocial Construction of Reality in Infancy, S. Feinman, Ed.New York: Plenum Press.

Fernald, A. (1989), “Intonation and communicative intent inmother’s speech to infants: Is the melody the message?”,Child Development 60.

Frijda, N. (1994), “Emotions require cognitions, even insimple ones,” in The Nature of Emotion, P. Ekman and R.Davidson, Eds. New York: Oxford University Press.

Izard, C. (1977), Human Emotions. New York: PlenumPress.

Lamere, P., Kwok, P., Walker, W., Gouvea, E. Singh, R.Raj, B.and Wolf, P. (2003), “Design of the CMU sphinx-4decoder,” in 8th European Conf. on SpeechCommunication and Technology (EUROSPEECH 2003).

Meltzoff, A. (1996), “The human infant as imitativegeneralist: A 20-year progress report on infant imitationwith implications for comparative psychology,” in SocialLearning in Animals: The Roots of Culture, B. G. C. M.Heyes, Ed. San Diego, CA: Academic Press.

Meltzoff, A. and Moore, K. (1997), Explaining facialimitation: a theoretical model, Early Development andParenting, Vol. 6.

Morency, L.-P., Rahimi, A., Checka, N. and Darrell, T.(2002), “Fast stereobased head tracking for interactiveenvironment,” in Int. Conference on Automatic Face andGesture Recognition.

Ortony, A., Clore, G. and Collins, A. (1988), The CognitiveStructure of Emotions. Cambridge: Cambridge UniversityPress.

Plutchik, R. (1984), “Emotions: A generalpsychoevolutionary theory,” in Approaches to Emotion,K. Sherer and P. Elkman, Eds. New Jersey: LawrenceErlbaum Associates.

Sawada, Takagi, and Fujita (2004), “Behavior selection andmotion modulation in emotionally grounded architecturefor QRIO SDR-4x II,” in IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS).

Scassellati, B. (2001), “Foundations for a theory of mind fora humanoid robot,” Massachusetts Institute ofTechnology, Department of Electrical Engineering andComputer Science, PhD Thesis.

Smith, L. (2003), The role of space in binding names toobjects, Cognitive Science, Boston.

Strack, F., Martin, L., Stepper, S. (1988), “Inhibiting andfacilitating conditions of the human smile: Anonobtrusive test of the facial feedback hypothesis.”Journal of Personality and Social Psychology, vol. 54.

Swettenham, J., Baron-Cohen, S., Charman, T., Cox, A.,Baird, G., Drew, A., Rees, L., & Wheelwright, S. (1998).The frequency and distribution of spontaneous attentionshifts between social and non-social stimuli in autistic,typically developing, and non-autistic developmentallydelayed infants. Journal of Child Psychology andPsychiatry, 9

Thayer (1989). The biopsychology of mood and arousal.Oxford University Press.

Trevarthen, C. (1979), “Communication and cooperation inearly infancy: A description of primary intersubjectivity,”in Before Speech: The Beginning of InterpersonalCommunication, M. Bullowa, Ed. Cambridge: CambridgeUniversity Press.

Watson D. & Tellegen A. (1985). Toward a consensualstructure of mood. Psychological Bulletin, 98, 219-235.