I Am Looking for Your Mind: Pupil Dilation Predicts Individual Differences in Sensitivity to Hints of Human-Likeness in Robot Behavior

Tools and methods to study and replicate experiments addressing human social cognition in interactive scenarios

In the last decade, scientists investigating human social cognition have started bringing traditional laboratory paradigms more "into the wild" to examine how socio-cognitive mechanisms of the human brain work in real-life settings. As this implies transferring 2D observational paradigms to 3D interactive environments, there is a risk of compromising experimental control. In this context, we propose a methodological approach which uses humanoid robots as proxies of social interaction partners and embeds them in experimental protocols that adapt classical paradigms of cognitive psychology to interactive scenarios. This allows for a relatively high degree of "naturalness" of interaction and excellent experimental control at the same time. Here, we present two case studies where our methods and tools were applied and replicated across two different laboratories, namely the Italian Institute of Technology in Genova (Italy) and the Agency for Science, Technology and Research in Singapore. In the first case study, we present a replication of an interactive version of a gaze-cueing paradigm reported in Kompatsiari et al. (J Exp Psychol Gen 151(1):121-136, 2022). The second case study presents a replication of a "shared experience" paradigm reported in Marchesi et al. (Technol Mind Behav 3(3):11, 2022). As both studies replicate results across labs and different cultures, we argue that our methods allow for reliable and replicable setups, even though the protocols are complex and involve social interaction. We conclude that our approach can be of benefit to the research field of social cognition and grant higher replicability, for example, in cross-cultural comparisons of social cognition mechanisms.

Differences in Social Expectations About Robot Signals and Human Signals

In our daily lives, we are continually involved in decision-making situations, many of which take place in the context of social interaction. Despite the ubiquity of such situations, there remains a gap in our understanding of how decision-making unfolds in social contexts, and how communicative signals, such as social cues and feedback, impact the choices we make. Interestingly, there is a new social context to which humans are recently increasingly more frequently exposed-social interaction with not only other humans but also artificial agents, such as robots or avatars. Given these new technological developments, it is of great interest to address the question of whether-and in what way-social signals exhibited by non-human agents influence decision-making. The present study aimed to examine whether robot non-verbal communicative behavior has an effect on human decision-making. To this end, we implemented a two-alternative-choice task where participants were to guess which of two presented cups was covering a ball. This game was an adaptation of a "Shell Game." A robot avatar acted as a game partner producing social cues and feedback. We manipulated robot's cues (pointing toward one of the cups) before the participant's decision and the robot's feedback ("thumb up" or no feedback) after the decision. We found that participants were slower (compared to other conditions) when cues were mostly invalid and the robot reacted positively to wins. We argue that this was due to the incongruence of the signals (cue vs. feedback), and thus violation of expectations. In sum, our findings show that incongruence in pre- and post-decision social signals from a robot significantly influences task performance, highlighting the importance of understanding expectations toward social robots for effective human-robot interactions.

Implicit measures of anthropomorphism: affective priming and recognition of apparent animal emotions

It has long been recognized that humans tend to anthropomorphize. That is, we naturally and effortlessly interpret the behaviors of nonhuman agents in the same way we interpret human behaviors. This tendency has only recently become a subject of empirical research. Most of this work uses explicit measures. Participants are asked whether they attribute some human-like trait to a nonhuman agent on some scale. These measures, however, have two limitations. First, they do not capture automatic components of anthropomorphism. Second, they generally only track one anthropomorphic result: the attribution (or non-attribution) of a particular trait. However, anthropomorphism can affect how we interpret animal behavior in other ways as well. For example, the grin of a nonhuman primate often looks to us like a smile, but it actually signals a state more like fear or anxiety. In the present work, we tested for implicit components of anthropomorphism based on an affective priming paradigm. Previous work suggests that priming with human faces displaying emotional expressions facilitated categorization of words into congruent emotion categories. In Experiments 1-3, we primed participants with images of nonhuman animals that appear to express happy or sad emotions, and asked participants to categorize words as positive or negative. Experiment 4 used human faces as control. Overall, we found consistent priming congruency effects in accuracy but not response time. These appeared to be more robust in older adults. They also appear to emerge with more processing time, and the pattern was the same with human as with primate faces. This demonstrates a role for automatic processes of emotion recognition in anthropomorphism. It also provides a potential measure for further exploration of implicit anthropomorphism.