Behind the Robot's Smiles and Frowns: In Social Context, People Do Not Mirror Android's Expressions But React to Their Informational Value.

Facial actions are key elements of non-verbal behavior. Perceivers' reactions to others' facial expressions often represent a match or mirroring (e.g., they smile to a smile). However, the information conveyed by an expression depends on context. Thus, when shown by an opponent, a smile conveys bad news and evokes frowning. The availability of anthropomorphic agents capable of facial actions raises the question of how people respond to such agents in social context. We explored this issue in a study where participants played a strategic game with or against a facially expressive android. Electromyography (EMG) recorded participants' reactions over zygomaticus muscle (smiling) and corrugator muscle (frowning). We found that participants' facial responses to android's expressions reflect their informational value, rather than a direct match. Overall, participants smiled more, and frowned less, when winning than losing. Critically, participants' responses to the game outcome were similar regardless of whether it was conveyed via the android's smile or frown. Furthermore, the outcome had greater impact on people's facial reactions when it was conveyed through android's face than a computer screen. These findings demonstrate that facial actions of artificial agents impact human facial responding. They also suggest a sophistication in human-robot communication that highlights the signaling value of facial expressions.

Is that a human? Categorization (dis)fluency drives evaluations of agents ambiguous on human-likeness.

A fundamental and seemingly unbridgeable psychological boundary divides humans and nonhumans. Essentialism theories suggest that mixing these categories violates "natural kinds." Perceptual theories propose that such mixing creates incompatible cues. Most theories suggest that mixed agents, with both human and nonhuman features, obligatorily elicit discomfort. In contrast, we demonstrate top-down, cognitive control of these effects-such that the discomfort with mixed agents is partially driven by disfluent categorization of ambiguous features that are pertinent to the agent. Three experiments tested this idea. Participants classified 3 different agents (humans, androids, and robots) either on the human-likeness or control dimension and then evaluated them. Classifying on the human-likeness dimensions made the mixed agent (android) more disfluent, and in turn, more disliked. Disfluency also mediated the negative affective reaction. Critically, devaluation only resulted from disfluency on human-likeness-and not from an equally disfluent color dimension. We argue that negative consequences on evaluations of mixed agents arise from integral disfluency (on features that are relevant to the judgment at-hand, like ambiguous human-likeness). In contrast, no negative effects stem from incidental disfluency (on features that do not bear on the current judgment, like ambiguous color backgrounds). Overall, these findings support a top-down account of why, when, and how mixed agents elicit conflict and discomfort. (PsycINFO Database Record

Observation and imitation of actions performed by humans, androids, and robots: an EMG study.

Understanding others' actions is essential for functioning in the physical and social world. In the past two decades research has shown that action perception involves the motor system, supporting theories that we understand others' behavior via embodied motor simulation. Recently, empirical approach to action perception has been facilitated by using well-controlled artificial stimuli, such as robots. One broad question this approach can address is what aspects of similarity between the observer and the observed agent facilitate motor simulation. Since humans have evolved among other humans and animals, using artificial stimuli such as robots allows us to probe whether our social perceptual systems are specifically tuned to process other biological entities. In this study, we used humanoid robots with different degrees of human-likeness in appearance and motion along with electromyography (EMG) to measure muscle activity in participants' arms while they either observed or imitated videos of three agents produce actions with their right arm. The agents were a Human (biological appearance and motion), a Robot (mechanical appearance and motion), and an Android (biological appearance and mechanical motion). Right arm muscle activity increased when participants imitated all agents. Increased muscle activation was found also in the stationary arm both during imitation and observation. Furthermore, muscle activity was sensitive to motion dynamics: activity was significantly stronger for imitation of the human than both mechanical agents. There was also a relationship between the dynamics of the muscle activity and motion dynamics in stimuli. Overall our data indicate that motor simulation is not limited to observation and imitation of agents with a biological appearance, but is also found for robots. However we also found sensitivity to human motion in the EMG responses. Combining data from multiple methods allows us to obtain a more complete picture of action understanding and the underlying neural computations.

Bridging the mechanical and the human mind: spontaneous mimicry of a physically present android.

The spontaneous mimicry of others' emotional facial expressions constitutes a rudimentary form of empathy and facilitates social understanding. Here, we show that human participants spontaneously match facial expressions of an android physically present in the room with them. This mimicry occurs even though these participants find the android unsettling and are fully aware that it lacks intentionality. Interestingly, a video of that same android elicits weaker mimicry reactions, occurring only in participants who find the android "humanlike." These findings suggest that spontaneous mimicry depends on the salience of humanlike features highlighted by face-to-face contact, emphasizing the role of presence in human-robot interaction. Further, the findings suggest that mimicry of androids can dissociate from knowledge of artificiality and experienced emotional unease. These findings have implications for theoretical debates about the mechanisms of imitation. They also inform creation of future robots that effectively build rapport and engagement with their human users.