Gaze following occurs automatically in social interactions, but the degree to which gaze is followed depends on whether an agent is perceived to have a mind, making its behavior socially more relevant for the interaction. Mind perception also modulates the attitudes we have toward others, and determines the degree of empathy, prosociality, and morality invested in social interactions. Seeing mind in others is not exclusive to human agents, but mind can also be ascribed to non-human agents like robots, as long as their appearance and/or behavior allows them to be perceived as intentional beings. Previous studies have shown that human appearance and reliable behavior induce mind perception to robot agents, and positively affect attitudes and performance in human–robot interaction. What has not been investigated so far is whether different triggers of mind perception have an independent or interactive effect on attitudes and performance in human–robot interaction. We examine this question by manipulating agent appearance (human vs. robot) and behavior (reliable vs. random) within the same paradigm and examine how congruent (human/reliable vs. robot/random) versus incongruent (human/random vs. robot/reliable) combinations of these triggers affect performance (i.e., gaze following) and attitudes (i.e., agent ratings) in human–robot interaction. The results show that both appearance and behavior affect human–robot interaction but that the two triggers seem to operate in isolation, with appearance more strongly impacting attitudes, and behavior more strongly affecting performance. The implications of these findings for human–robot interaction are discussed.

With the rise of increasingly complex artificial intelligence (AI), there is a need to design new methods to monitor AI in a transparent, human-aware manner. Decades of research have demonstrated that people, who are not aware of the exact performance levels of automated algorithms, often experience a mismatch in expectations. Consequently, they will often provide either too little or too much trust in an algorithm. Detecting such a mismatch in expectations, or trust calibration, remains a fundamental challenge in research investigating the use of automation. Due to the context-dependent nature of trust, universal measures of trust have not been established. Trust is a difficult construct to investigate because even the act of reflecting on how much a person trusts a certain agent can change the perception of that agent. We hypothesized that electroencephalograms (EEGs) would be able to provide such a universal index of trust without the need of self-report. In this work, EEGs were recorded for 21 participants (mean age = 22.1; 13 females) while they observed a series of algorithms perform a modified version of a flanker task. Each algorithm’s degree of credibility and reliability were manipulated. We hypothesized that neural markers of action monitoring, such as the observational error-related negativity (oERN) and observational error positivity (oPe), are potential candidates for monitoring computer algorithm performance. Our findings demonstrate that (1) it is possible to reliably elicit both the oERN and oPe while participants monitored these computer algorithms, (2) the oPe, as opposed to the oERN, significantly distinguished between high and low reliability algorithms, and (3) the oPe significantly correlated with subjective measures of trust. This work provides the first evidence for the utility of neural correlates of error monitoring for examining trust in computer algorithms.

In social interactions, we rely on nonverbal cues like gaze direction to understand the behavior of others. How we react to these cues is affected by whether they are believed to originate from an entity with a mind, capable of having internal states (i.e., mind perception). While prior work has established a set of neural regions linked to social-cognitive processes like mind perception, the degree to which activation within this network relates to performance in subsequent social-cognitive tasks remains unclear. In the current study, participants performed a mind perception task (i.e., judging the likelihood that faces, varying in physical human-likeness, have internal states) while event-related fMRI was collected. Afterwards, participants performed a social attention task outside the scanner, during which they were cued by the gaze of the same faces that they previously judged within the mind perception task. Parametric analyses of the fMRI data revealed that activity within ventromedial prefrontal cortex (vmPFC) was related to both mind ratings inside the scanner and gaze-cueing performance outside the scanner. In addition, other social brain regions were related to gaze-cueing performance, including frontal areas like the left insula, dorsolateral prefrontal cortex, and inferior frontal gyrus, as well as temporal areas like the left temporo-parietal junction and bilateral temporal gyri. The findings suggest that functions subserved by the vmPFC are relevant to both mind perception and social attention, implicating a role of vmPFC in the top-down modulation of low-level social-cognitive processes.

Understanding others' nonverbal behavior is essential for social interaction, as it allows, among others, to infer mental states. Although gaze communication, a well-established nonverbal social behavior, has shown its importance in inferring others' mental states, not much is known about the effects of irrelevant gaze signals on cognitive conflict markers during collaborative settings. Here, participants completed a categorization task where they categorized objects based on their color while observing images of a robot. On each trial, participants observed the robot iCub grasping an object from a table and offering it to them to simulate a handover. Once the robot “moved” the object forward, participants were asked to categorize the object according to its color. Before participants were allowed to respond, the robot made a lateral head/gaze shift. The gaze shifts were either congruent or incongruent with the object's color. We expected that incongruent head cues would induce more errors (Study 1), would be associated with more curvature in eye-tracking trajectories (Study 2), and induce larger amplitude in electrophysiological markers of cognitive conflict (Study 3). Results of the three studies show more oculomotor interference as measured in error rates (Study 1), larger curvatures eye-tracking trajectories (Study 2), and higher amplitudes of the N2 ERP of the EEG signals as well as higher event-related spectral perturbation amplitudes (Study 3) for incongruent trials compared with congruent trials. Our findings reveal that behavioral, ocular, and electrophysiological markers can index the influence of irrelevant signals during goal-oriented tasks.

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