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.

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.

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).

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.

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.