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.

Theta synchronization as a neural marker of flexible (re-)use of socio-cognitive mechanisms for a new category of (artificial) interaction partners.

Previous work shows that in some instances artificial agents, such as robots, can elicit higher-order socio-cognitive mechanisms, similar to those elicited by humans. This suggests that these socio-cognitive mechanisms, such as mentalizing processes, originally developed for interaction with other humans, might be flexibly (re-)used, or "hijacked", for approaching this new category of interaction partners (Wykowska, 2020). In this study, we set out to identify neural markers of such flexible reuse of socio-cognitive mechanisms. We focused on fronto-parietal theta synchronization, as it has been proposed to be a substrate of cognitive flexibility in general (Fries, 2005). We analyzed EEG data from two experiments (Bossi et al., 2020; Roselli et al., submitted), in which participants completed a test measuring their individual likelihood to adopt the intentional stance towards robots, the intentional stance (IST) test. Our results show that participants with higher scores on the IST, indicating that they had higher likelihood of adopting the intentional stance towards a robot, had a significantly higher theta synchronization value, relative to participants with lower scores on the IST. These results suggest that long-range synchronization in the theta band might be a marker socio-cognitive process that can be flexibly applied towards non-human agents, such as robots.

Misleading Robot Signals in a Classification Task Induce Cognitive Load as Measured by Theta Synchronization Between Frontal and Temporo-parietal Brain Regions.

As technological advances progress, we find ourselves in situations where we need to collaborate with artificial agents (e.g., robots, autonomous machines and virtual agents). For example, autonomous machines will be part of search and rescue missions, space exploration and decision aids during monitoring tasks (e.g., baggage-screening at the airport). Efficient communication in these scenarios would be crucial to interact fluently. While studies examined the positive and engaging effect of social signals (i.e., gaze communication) on human-robot interaction, little is known about the effects of conflicting robot signals on the human actor's cognitive load. Moreover, it is unclear from a social neuroergonomics perspective how different brain regions synchronize or communicate with one another to deal with the cognitive load induced by conflicting signals in social situations with robots. The present study asked if neural oscillations that correlate with conflict processing are observed between brain regions when participants view conflicting robot signals. Participants classified different objects based on their color after a robot (i.e., iCub), presented on a screen, simulated handing over the object to them. The robot proceeded to cue participants (with a head shift) to the correct or incorrect target location. Since prior work has shown that unexpected cues can interfere with oculomotor planning and induces conflict, we expected that conflicting robot social signals which would interfere with the execution of actions. Indeed, we found that conflicting social signals elicited neural correlates of cognitive conflict as measured by mid-brain theta oscillations. More importantly, we found higher coherence values between mid-frontal electrode locations and posterior occipital electrode locations in the theta-frequency band for incongruent vs. congruent cues, which suggests that theta-band synchronization between these two regions allows for communication between cognitive control systems and gaze-related attentional mechanisms. We also find correlations between coherence values and behavioral performance (Reaction Times), which are moderated by the congruency of the robot signal. In sum, the influence of irrelevant social signals during goal-oriented tasks can be indexed by behavioral, neural oscillation and brain connectivity patterns. These data provide insights about a new measure for cognitive load, which can also be used in predicting human interaction with autonomous machines.

Risk Perception and Media in Shaping Protective Behaviors: Insights From the Early Phase of COVID-19 Italian Outbreak.

In the absence of target treatments or vaccination, the SARS-CoV-2 pandemic can be impeded by effectively implementing containment measures and behaviors. This relies on individuals' adoption of protective behaviors, their perceived risk, and the use and trust of information sources. During a health emergency, receiving timely and accurate information enables individuals to take appropriate actions to protect themselves, shaping their risk perception. Italy was the first western country plagued by COVID-19 and one of the most affected in the early phase. During this period, we surveyed 2,223 Italians before the national lockdown. A quarter of the sample perceived COVID-19 less threatening than flu and would not vaccinate, if a vaccine was available. Besides, most people perceived containment measures, based on social distancing or wearing masks, not useful. This perceived utility was related to COVID-19 threat perception and efficacy beliefs. All these measures were associated with the use of media and their truthfulness: participants declared to mainly use the Internet, while health organizations' websites were the most trusted. Although social networks were frequently used, they were rated lower for trustfulness. Our data differ from those obtained in other community samples, suggesting the relevance to explore changes across different countries and during the different phases of the pandemic. Understanding these phenomena, and how people access the media, may contribute to improve the efficacy of containment measures, tailoring specific policies and health communications.

Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging.

One of the greatest challenges in providing early effective treatment in mood disorders is the early differential diagnosis between major depression (MDD) and bipolar disorder (BD). A remarkable need exists to identify reliable biomarkers for these disorders. We integrate structural neuroimaging techniques (i.e. Tract-based Spatial Statistics, TBSS, and Voxel-based morphometry) in a multiple kernel learning procedure in order to define a predictive function of BD against MDD diagnosis in a sample of 148 patients. We achieved a balanced accuracy of 73.65% with a sensitivity for BD of 74.32% and specificity for MDD of 72.97%. Mass-univariates analyses showed reduced grey matter volume in right hippocampus, amygdala, parahippocampal, fusiform gyrus, insula, rolandic and frontal operculum and cerebellum, in BD compared to MDD. Volumes in these regions and in anterior cingulate cortex were also reduced in BD compared to healthy controls (n = 74). TBSS analyses revealed widespread significant effects of diagnosis on fractional anisotropy, axial, radial, and mean diffusivity in several white matter tracts, suggesting disruption of white matter microstructure in depressed patients compared to healthy controls, with worse pattern for MDD. To best of our knowledge, this is the first study combining grey matter and diffusion tensor imaging in predicting BD and MDD diagnosis. Our results prompt brain quantitative biomarkers and multiple kernel learning as promising tool for personalized treatment in mood disorders.