The mere presence of an outgroup member disrupts the brain's feedback-monitoring system

Temporal interplay between cognitive conflict and attentional markers in social collaboration

Cognitive processes deal with contradictory demands in social contexts. On the one hand, social interactions imply a demand for cooperation, which requires processing social signals, and on the other, demands for selective attention require ignoring irrelevant signals, to avoid overload. We created a task with a humanoid robot displaying irrelevant social signals, imposing conflicting demands on selective attention. Participants interacted with the robot as a team (high social demand; n = 23) or a passive co-actor (low social demand; n = 19). We observed that theta oscillations indexed conflict processing of social signals. Subsequently, alpha oscillations were sensitive to the conflicting social signals and the mode of interaction. These findings suggest that brains have distinct mechanisms for dealing with the complexity of social interaction and that these mechanisms are activated differently depending on the mode of the interaction. Thus, how we process environmental stimuli depends on the beliefs held regarding our social context.

A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning?

Social species rely on the ability to modulate feedback-monitoring in social contexts to adjust one's actions and obtain desired outcomes. When being awarded positive outcomes during a gambling task, feedback-monitoring is attenuated when strangers are rewarded, as less value is assigned to the awarded outcome. This difference in feedback-monitoring can be indexed by an event-related potential (ERP) component known as the Reward Positivity (RewP), whose amplitude is enhanced when receiving positive feedback. While the degree of familiarity influences the RewP, little is known about how the RewP and reinforcement learning are affected when gambling on behalf of familiar versus nonfamiliar agents, such as robots. This question becomes increasingly important given that robots may be used as teachers and/or social companions in the near future, with whom children and adults will interact with for short or long periods of time. In the present study, we examined whether feedback-monitoring when gambling on behalf of oneself compared with a robot is impacted by whether participants have familiarized themselves with the robot before the task. We expected enhanced RewP amplitude for self versus other for those who did not familiarize with the robot and that self-other differences in the RewP would be attenuated for those who familiarized with the robot. Instead, we observed that the RewP was larger when familiarization with the robot occurred, which corresponded to overall worse learning outcomes. We additionally observed an enhanced P3 effect for the high-familiarity condition, which suggests an increased motivation to reward. These findings suggest that familiarization with robots may cause a positive motivational effect, which positively affects RewP amplitudes, but interferes with learning.

Examining Social Cognition with Embodied Robots: Does Prior Experience with a Robot Impact Feedback-associated Learning in a Gambling Task?

Social agents rely on the ability to use feedback to learn and modify their behavior. The extent to which this happens in social contexts depends on motivational, cognitive and/or affective parameters. For instance, feedback-associated learning occurs at different rates when the outcome of an action (e.g., winning or losing in a gambling task) affects oneself (“Self”) versus another human (“Other”). Here, we examine whether similar context effects on feedback-associated learning can also be observed when the “other” is a social robot (here: Cozmo). We additionally examine whether a “hybrid” version of the gambling paradigm, where participants are free to engage in a dynamic interaction with a robot, then move to a controlled screen-based experiment can be used to examine social cognition in human-robot interaction. This hybrid method is an alternative to current designs where researchers examine the effect of the interaction on social cognition during the interaction with the robot. For that purpose, three groups of participants (n total = 60) interacted with Cozmo over different time periods (no interaction vs. a single 20 minute interaction in the lab vs. daily 20 minute interactions over five consecutive days at home) before performing the gambling task in the lab. The results indicate that prior interactions impact the degree to which participants benefit from feedback during the gambling task, with overall worse learning immediately after short-term interactions with the robot and better learning in the “Self” versus “Other” condition after repeated interactions with the robot. These results indicate that “hybrid” paradigms are a suitable option to investigate social cognition in human-robot interaction when a fully dynamic implementation (i.e., interaction and measurement dynamic) is not feasible.

Making mistakes in public: Being observed magnifies physiological responses to errors

As social beings, humans often find themselves in situations in which they are required to act against the backdrop of public observation. In these situations, the presence of observers may influence how we evaluate feedback and learn from it. The neural basis of such observer-induced behavioral changes is not well understood at present. In the current study, we used event-related potentials (ERPs) and event-related spectral perturbations (ERSPs) combined with a gambling task to examine how the presence of others modulates the neural representations of errors and losses. In the alone condition, participants finished the gambling task alone while in the observed condition, they were observed by two others. Results revealed that the observer effect particularly altered the feedback-related negativity (FRN) and the late positive potential (LPP). The difference in FRN amplitude between error and correct feedback was enlarged when participants were being observed. The LPP also showed a marginally enhanced amplitude difference between error and correct feedback in the observed condition. We also found that theta power was enhanced in the observed condition. Taken together, our findings suggest that neural representations of errors were influenced by the presence of others.