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Earlier research has indicated that humans prioritize attention to the space close to their hands, commonly known as the "near-hand effect". This phenomenon also extends to a human partner's hand, but specifically following a shared physical joint action. Consequently, within human dyads, collaborative interaction results in a shared body representation that might impact fundamental attentional mechanisms. Our project investigates whether a similar effect can emerge from a human-robot interaction scenario. In previous work, we have shown that the mere presence of an anthropomorphic robot's hand is not enough to trigger the near-hand effect. Here, we designed an experiment to assess whether a collaborative human-robot interaction with the humanoid robot iCub could bias human attention toward the robot's hand. After the interaction, we replicated a classical psychological paradigm by adding a robotic condition to measure this attentional bias (i.e., the near-hand effect). Our findings indicate the existence of a near-hand effect triggered by the robot's hand, suggesting that HRI can replicate a shared body representation similar to that observed in human dyads, which may influence our basic attentional mechanisms.

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Taking advice from others requires confidence in their competence. This is important for interaction with peers, but also for collaboration with social robots and artificial agents. Nonetheless, we do not always have access to information about others’ competence or performance. In these uncertain environments, do our prior beliefs about the nature and the competence of our interacting partners modulate our willingness to rely on their judgments? In a joint perceptual decision making task, participants made perceptual judgments and observed the simulated estimates of either a human participant, a social humanoid robot or a computer. Then they could modify their estimates based on this feedback. Results show participants’ belief about the nature of their partner biased their compliance with its judgments: participants were more influenced by the social robot than human and computer partners. This difference emerged strongly at the very beginning of the task and decreased with repeated exposure to empirical feedback on the partner’s responses, disclosing the role of prior beliefs in social influence under uncertainty. Furthermore, the results of our functional task suggest an important difference between human–human and human–robot interaction in the absence of overt socially relevant signal from the partner: the former is modulated by social normative mechanisms, whereas the latter is guided by purely informational mechanisms linked to the perceived competence of the partner.

Human perception and motion are continuously influenced by prior experience. However, when humans have to share the same space and time, different previous experience could lead towards opposite percepts and actions, consequently failing in coordination. This study presents a novel experimental setup that aims at exploring the interplay between human perceptual mechanisms and motor strategies during human-robot interaction. To achieve this goal, we developed a complex system to enable the realization of an interactive perceptual task, where the participant has to perceive and estimate temporal durations together with iCub, with the goal of coordinating with the robotic partner. Results show that the experimental setup continuously monitor how participants implement their perceptual and motor behavior during the interaction with a controllable interacting agent. Therefore, it will be possible to produce quantitative models describing the interplay between perceptual and motor adaptation during an interaction.

Previous studies have revealed that humans prioritize attention to the space near their hands (the so-called near-hand effect). This effect may also occur towards a human partner's hand, but only after sharing a physical joint action. Hence, in human dyads, interaction leads to a shared body representation that may influence basic attentional mechanisms. Our project investigates whether a collaborative interaction with a robot might similarly influence attention. To this aim, we designed an experiment to assess whether the mere presence of a robot with an anthropomorphic hand could bias the human partner's attention.We replicated a classical psychological paradigm to measure this attentional bias (i.e., the near-hand effect) by adding a robotic condition. Preliminary results found the near-hand effect when performing the task with the self-hand near the screen, leading to shorter reaction times on the same side of the hand. On the contrary, we found no effect on the robot's hand in the absence of previous collaborative interaction with the robot, in line with studies involving human partners.

Many types of social interaction require the ability to anticipate others' behavior, which is commonly referred to as strategic sophistication. In this context, observational learning can represent a decisive tool for behavioral adaptation. However, little is known on whether and when individuals learn from observation in interactive settings. In the current study, 321 participants played one-shot interactive games and, at a given time along the experiment, they could observe the choices of an overtly efficient player. This social feedback could be provided before or after the participant’s choice in each game. Results reveal that players with a sufficient level of strategic skills increased their level of sophistication only when the social feedback was provided after their choices, whereas they relied on blind imitation when they received feedback before their decision. Conversely, less sophisticated players did not increase their level of sophistication, regardless of the type of social feedback. Our findings disclose the interplay between endogenous and exogenous factors modulating observational learning in strategic interaction.

In our everyday life, we often need to anticipate the potential occurrence of events and their consequences. In this context, the way we represent contingencies can determine our ability to adapt to the environment. However, it is not clear how agents encode and organize available knowledge about the future to react to possible states of the world. In the present study, we investigated the process of contingency representation with three eye-tracking experiments. In Experiment 1, we introduced a novel relational-inference task in which participants had to learn and represent conditional rules regulating the occurrence of interdependent future events. A cluster analysis on early gaze data revealed the existence of 2 distinct types of encoders. A group of (sophisticated) participants built exhaustive contingency models that explicitly linked states with each of their potential consequences. Another group of (unsophisticated) participants simply learned binary conditional rules without exploring the underlying relational complexity. Analyses of individual cognitive measures revealed that cognitive reflection is associated with the emergence of either sophisticated or unsophisticated representation behavior. In Experiment 2, we observed that unsophisticated participants switched toward the sophisticated strategy after having received information about its existence, suggesting that representation behavior was modulated by strategy generation mechanisms. In Experiment 3, we showed that the heterogeneity in representation strategy emerges also in conditional reasoning with verbal sequences, indicating the existence of a general disposition in building either sophisticated or unsophisticated models of contingencies.

We run an eye-tracking experiment to investigate whether players change their gaze patterns and choices after they experience alternative models of choice in one-shot games. In phase 1 and 3, participants play 2 × 2 matrix games with a human counterpart; in phase 2, they apply specific decision rules while playing with a computer with known behavior. We classify participants in types based on their gaze patterns in phase 1 and explore attentional shifts in phase 3, after players were exposed to the alternative decision rules. Results show that less sophisticated players, who focus mainly on their own payoffs, change their gaze patterns towards the evaluation of others’ incentives in phase 3. This attentional shift predicts an increase in equilibrium responses in relevant classes of games. Conversely, cooperative players do not change their visual analysis. Our results shed new light on theories of bounded rationality and on theories of social preferences.

In early deaf individuals, the auditory deprived temporal brain regions become engaged in visual processing. In our study we tested further the hypothesis that intrinsic functional specialization guides the expression of cross-modal responses in the deprived auditory cortex. We used functional MRI to characterize the brain response to horizontal, radial and stochastic visual motion in early deaf and hearing individuals matched for the use of oral or sign language. Visual motion showed enhanced response in the ‘deaf’ mid-lateral planum temporale, a region selective to auditory motion as demonstrated by a separate auditory motion localizer in hearing people. Moreover, multivariate pattern analysis revealed that this reorganized temporal region showed enhanced decoding of motion categories in the deaf group, while visual motion-selective region hMT+/V5 showed reduced decoding when compared to hearing people. Dynamic Causal Modelling revealed that the ‘deaf’ motion-selective temporal region shows a specific increase of its functional interactions with hMT+/V5 and is now part of a large-scale visual motion selective network. In addition, we observed preferential responses to radial, compared to horizontal, visual motion in the ‘deaf’ right superior temporal cortex region that also show preferential response to approaching/receding sounds in the hearing brain. Overall, our results suggest that the early experience of auditory deprivation interacts with intrinsic constraints and triggers a large-scale reallocation of computational load between auditory and visual brain regions that typically support the multisensory processing of motion information.

Human decisions are often influenced by others' opinions. This process is regulated by social norms: for instance, we tend to reciprocate the consideration received from others, independently of their reliability as information sources. Nonetheless, no study to date has investigated whether and how reciprocity modulates social influence in child-adult interaction. We tested 6-, 8- and 10-year-old children in a novel joint perceptual task. A child and an adult experimenter made perceptual estimates and then took turns in making a final decision, choosing between their own and partner's response. We manipulated the final choices of the adult partner, who in one condition chose often the child's estimates, whereas in another condition tended to confirm her own response. Results reveal that 10-year-old children reciprocated the consideration received from the partner, increasing their level of conformity to the adult's judgements when the partner had shown high consideration towards them. At the same time, 10-year-old children employed more elaborate decision criteria in choosing when trusting the adult partner compared to younger children and did not show egocentric biases in their final decisions. Our results shed light on the development of the cognitive and normative mechanisms modulating reciprocal social influence in child-adult interaction.

Humans are constantly influenced by others' behavior and opinions. Of importance, social influence among humans is shaped by reciprocity: we follow more the advice of someone who has been taking into consideration our opinions. In the current work, we investigate whether reciprocal social influence can emerge while interacting with a social humanoid robot. In a joint task, a human participant and a humanoid robot made perceptual estimates and then could overtly modify them after observing the partner's judgment. Results show that endowing the robot with the ability to express and modulate its own level of susceptibility to the human's judgments represented a double-edged sword. On the one hand, participants lost confidence in the robot's competence when the robot was following their advice; on the other hand, participants were unwilling to disclose their lack of confidence to the susceptible robot, suggesting the emergence of reciprocal mechanisms of social influence supporting human-robot collaboration.

Indirect reciprocity is a pervasive social norm that promotes human cooperation. Helping someone establishes a good reputation, increasing the probability of receiving help from others. Here we hypothesize that indirect reciprocity regulates not only cooperative behavior but also the exchange of opinions within a social group. In a novel interactive perceptual task (Experiment 1), we show that participants relied more on the judgments of an alleged human partner when a second alleged peer had been endorsing participants’ opinions. By doing so, participants did not take into account the reliability of their partners’ judgments and did not maximize behavioral accuracy and monetary reward. This effect declined when participants did not expect future interactions with their partners, suggesting the emergence of downstream mechanisms of reciprocity linked to the management of reputation. Importantly, all these effects disappeared when participants knew that the partners’ responses were computer-generated (Experiment 2). Our results suggest that, within a social group, individuals may weight others’ opinions through indirect reciprocity, highlighting the emergence of normative distortions in the process of information transmission among humans.

Trust is one of the hallmarks of human-human and human-robot interaction. Extensive evidence has shown that trust among humans requires reciprocity. Conversely, research in human-robot interaction (HRI) has mostly relied on a unidirectional view of trust that focuses on robots' reliability and performance. The current paper argues that reciprocity may also play a key role in the emergence of mutual trust and successful collaboration between humans and robots. We will gather and discuss works that reveal a reciprocal dimension in human-robot trust, paving the way to a bidirectional and dynamic view of trust in HRI.

In social contexts, we refer to strategic sophistication as the ability to adapt our own behavior based on the possible actions of others. In the current study, we explore the role of other-oriented attention and cognitive reflection in explaining heterogeneity in strategic sophistication. In two eye-tracking experiments, we registered eye movements of participants while playing matrix games of increasing relational complexity (2x2 and 3x3 matrices), and we analyzed individual gaze patterns to reveal the ongoing mechanisms of integration of own and others’ incentives in the current game representation. Moreover, participants completed the Cognitive Reflection Test (CRT), in addition to alternative measures of cognitive ability. In both classes of games, higher cognitive reflection levels specifically predict the ability to incorporate the counterpart’s incentives in the current model of the game, as well as higher levels of strategic sophistication. Conversely, players exhibiting low cognitive reflection tend to pay less attention to relevant transitions between the counterpart’s payoffs, and such incomplete visual analysis leads to out-of-equilibrium choices. Gaze patterns appear to completely mediate the relationship between cognitive reflection and strategic choices. Our results shed new light on the cognitive factors driving heterogeneity in strategic thinking and on theories of bounded rationality.

Brain systems supporting face and voice processing both contribute to the extraction of important information for social interaction (e.g., person identity). How does the brain reorganize when one of these channels is absent? Here, we explore this question by combining behavioral and multimodal neuroimaging measures (magneto-encephalography and functional imaging) in a group of early deaf humans. We show enhanced selective neural response for faces and for individual face coding in a specific region of the auditory cortex that is typically specialized for voice perception in hearing individuals. In this region, selectivity to face signals emerges early in the visual processing hierarchy, shortly after typical face-selective responses in the ventral visual pathway. Functional and effective connectivity analyses suggest reorganization in long-range connections from early visual areas to the face-selective temporal area in individuals with early and profound deafness. Altogether, these observations demonstrate that regions that typically specialize for voice processing in the hearing brain preferentially reorganize for face processing in born-deaf people. Our results support the idea that cross-modal plasticity in the case of early sensory deprivation relates to the original functional specialization of the reorganized brain regions.