The Effects of Humanlike and Robot-Specific Affective Nonverbal Behavior on Perception, Emotion, and Behavior

Research demonstrated that humans are able to interpret humanlike (affective) nonverbal behavior (HNB) in artificial entities (e.g. Beck et al., in: Proceedings of the 19th IEEE international symposium on robot and human interactive communication, IEEE Press, Piscataway, 2010. 10.1109/ROMAN.2010.5598649; Bente et al. in J Nonverbal Behav 25: 151-166, 2001; Mumm and Mutlu, in: Proceedings of the 6th international conference on human-robot interaction, HRI. ACM Press, New York, 2011. 10.1145/1957656.1957786). However, some robots lack the possibility to produce HNB. Using robot-specific nonverbal behavior (RNB) such as different eye colors to convey emotional meaning might be a fruitful mechanism to enhance HRI experiences, but it is unclear whether RNB is as effective as HNB. We present a review on affective nonverbal behaviors in robots and an experimental study. We experimentally tested the influence of HNB and RNB (colored LEDs) on users' perception of the robot (e.g. likeability, animacy), their emotional experience, and self-disclosure. In a between-subjects design, users interacted with either (a) a robot displaying no nonverbal behavior, (b) a robot displaying affective RNB, (c) a robot displaying affective HNB or (d) a robot displaying affective HNB and RNB. Results show that HNB, but not RNB, has a significant effect on the perceived animacy of the robot, participants' emotional state, and self-disclosure. However, RNB still slightly influenced participants' perception, emotion, and behavior: Planned contrasts revealed having any type of nonverbal behavior significantly increased perceived animacy, positive affect, and self-disclosure. Moreover, observed linear trends indicate that the effects increased with the addition of nonverbal behaviors (control< RNB< HNB). In combination, our results suggest that HNB is more effective in transporting the robot's communicative message than RNB.