Two distinct networks for encoding goals and forms of action: An effective connectivity study.

Goal-directed actions are characterized by two main features: the content (i.e., the action goal) and the form, called vitality forms (VF) (i.e., how actions are executed). It is well established that both the action content and the capacity to understand the content of another's action are mediated by a network formed by a set of parietal and frontal brain areas. In contrast, the neural bases of action forms (e.g., gentle or rude actions) have not been characterized. However, there are now studies showing that the observation and execution of actions endowed with VF activate, in addition to the parieto-frontal network, the dorso-central insula (DCI). In the present study, we established-using dynamic causal modeling (DCM)-the direction of information flow during observation and execution of actions endowed with gentle and rude VF in the human brain. Based on previous fMRI studies, the selected nodes for the DCM comprised the posterior superior temporal sulcus (pSTS), the inferior parietal lobule (IPL), the premotor cortex (PM), and the DCI. Bayesian model comparison showed that, during action observation, two streams arose from pSTS: one toward IPL, concerning the action goal, and one toward DCI, concerning the action vitality forms. During action execution, two streams arose from PM: one toward IPL, concerning the action goal and one toward DCI concerning action vitality forms. This last finding opens an interesting question concerning the possibility to elicit VF in two distinct ways: cognitively (from PM to DCI) and affectively (from DCI to PM).

Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents.

The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.

The effect of visuo-haptic exploration on the development of the geometric cross-sectioning ability.

Cross-sectioning is a shape understanding task where the participants must infer and interpret the spatial features of three-dimensional (3D) solids by depicting their internal two-dimensional (2D) arrangement. An increasing body of research provides evidence of the crucial role of sensorimotor experience in acquiring these complex geometrical concepts. Here, we focused on how cross-sectioning ability emerges in young children and the influence of multisensory visuo-haptic experience in geometrical learning through two experiments. In Experiment 1, we compared the 3D printed version of the Santa Barbara Solids Test (SBST) with its classical paper version; in Experiment 2, we contrasted the children's performance in the SBST before and after the visual or visuo-haptic experience. In Experiment 1, we did not identify an advantage in visualizing 3D shapes over the classical 2D paper test. In contrast, in Experiment 2, we found that children who had the experience of a combination of visual and tactile information during the exploration phase improved their performance in the SBST compared with children who were limited to visual exploration. Our study demonstrates how practicing novel multisensory strategies improves children's understanding of complex geometrical concepts. This outcome highlights the importance of introducing multisensory experience in educational training and the need to make way for developing new technologies that could improve learning abilities in children.

Investigating form and content of emotional and non-emotional laughing.

As cold actions (i.e. actions devoid of an emotional content), also emotions are expressed with different vitality forms. For example, when an individual experiences a positive emotion, such as laughing as expression of happiness, this emotion can be conveyed to others by different intensities of face expressions and body postures. In the present study, we investigated whether the observation of emotions, expressed with different vitality forms, activates the same neural structures as those involved in cold action vitality forms processing. To this purpose, we carried out a functional magnetic resonance imaging study in which participants were tested in 2 conditions: emotional and non-emotional laughing both conveying different vitality forms. There are 3 main results. First, the observation of emotional and non-emotional laughing conveying different vitality forms activates the insula. Second, the observation of emotional laughing activates a series of subcortical structures known to be related to emotions. Furthermore, a region of interest analysis carried out in these structures reveals a significant modulation of the blood-oxygen-leveldependent (BOLD) signal during the processing of different vitality forms exclusively in the right amygdala, right anterior thalamus/hypothalamus, and periaqueductal gray. Third, in a subsequent electromyography study, we found a correlation between the zygomatic muscles activity and BOLD signal in the right amygdala only.

The impact of early aging on visual perception of space and time.

Visual perception of space and time has been shown to rely on context dependency, an inferential process by which the average magnitude of a series of stimuli previously experienced acts as a prior during perception. This article aims to investigate the presence and evolution of this phenomenon in early aging. Two groups of participants belonging to two different age ranges (Young Adults: average age 28.8 years old; Older Adults: average age 62.8 years old) participated in the study performing a discrimination and a reproduction task, both in a spatial and temporal conditions. In particular, they were asked to evaluate lengths in the spatial domain and interval durations in the temporal one. Early aging resulted to be associated to a general decline of the perceptual acuity, which is particularly evident in the temporal condition. The context dependency phenomenon was preserved also during aging, maintaining similar levels as those exhibited by the younger group in both space and time perception. However, the older group showed a greater variability in context dependency among participants, perhaps due to different strategies used to face a higher uncertainty in the perceptual process.

Early blindness modulates haptic object recognition.

Haptic object recognition is usually an efficient process although slower and less accurate than its visual counterpart. The early loss of vision imposes a greater reliance on haptic perception for recognition compared to the sighted. Therefore, we may expect that congenitally blind persons could recognize objects through touch more quickly and accurately than late blind or sighted people. However, the literature provided mixed results. Furthermore, most of the studies on haptic object recognition focused on performance, devoting little attention to the exploration procedures that conducted to that performance. In this study, we used iCube, an instrumented cube recording its orientation in space as well as the location of the points of contact on its faces. Three groups of congenitally blind, late blind and age and gender-matched blindfolded sighted participants were asked to explore the cube faces where little pins were positioned in varying number. Participants were required to explore the cube twice, reporting whether the cube was the same or it differed in pins disposition. Results showed that recognition accuracy was not modulated by the level of visual ability. However, congenitally blind touched more cells simultaneously while exploring the faces and changed more the pattern of touched cells from one recording sample to the next than late blind and sighted. Furthermore, the number of simultaneously touched cells negatively correlated with exploration duration. These findings indicate that early blindness shapes haptic exploration of objects that can be held in hands.

The world seems different in a social context: A neural network analysis of human experimental data.

Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience.

Perception is Only Real When Shared: A Mathematical Model for Collaborative Shared Perception in Human-Robot Interaction.

Partners have to build a shared understanding of their environment in everyday collaborative tasks by aligning their perceptions and establishing a common ground. This is one of the aims of shared perception: revealing characteristics of the individual perception to others with whom we share the same environment. In this regard, social cognitive processes, such as joint attention and perspective-taking, form a shared perception. From a Human-Robot Interaction (HRI) perspective, robots would benefit from the ability to establish shared perception with humans and a common understanding of the environment with their partners. In this work, we wanted to assess whether a robot, considering the differences in perception between itself and its partner, could be more effective in its helping role and to what extent this improves task completion and the interaction experience. For this purpose, we designed a mathematical model for a collaborative shared perception that aims to maximise the collaborators' knowledge of the environment when there are asymmetries in perception. Moreover, we instantiated and tested our model via a real HRI scenario. The experiment consisted of a cooperative game in which participants had to build towers of Lego bricks, while the robot took the role of a suggester. In particular, we conducted experiments using two different robot behaviours. In one condition, based on shared perception, the robot gave suggestions by considering the partners' point of view and using its inference about their common ground to select the most informative hint. In the other condition, the robot just indicated the brick that would have yielded a higher score from its individual perspective. The adoption of shared perception in the selection of suggestions led to better performances in all the instances of the game where the visual information was not a priori common to both agents. However, the subjective evaluation of the robot's behaviour did not change between conditions.

Human- or object-like? Cognitive anthropomorphism of humanoid robots.

Across three experiments (N = 302), we explored whether people cognitively elaborate humanoid robots as human- or object-like. In doing so, we relied on the inversion paradigm, which is an experimental procedure extensively used by cognitive research to investigate the elaboration of social (vs. non-social) stimuli. Overall, mixed-model analyses revealed that full-bodies of humanoid robots were subjected to the inversion effect (body-inversion effect) and, thus, followed a configural processing similar to that activated for human beings. Such a pattern of finding emerged regardless of the similarity of the considered humanoid robots to human beings. That is, it occurred when considering bodies of humanoid robots with medium (Experiment 1), high and low (Experiment 2) levels of human likeness. Instead, Experiment 3 revealed that only faces of humanoid robots with high (vs. low) levels of human likeness were subjected to the inversion effects and, thus, cognitively anthropomorphized. Theoretical and practical implications of these findings for robotic and psychological research are discussed.

All by Myself: Learning individualized competitive behavior with a contrastive reinforcement learning optimization.

In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. Besides dealing with the increased dynamics of the scenarios due to the opponents' actions, they usually have to understand how to overcome the opponent's strategies. Most of the common solutions, usually based on continual learning or centralized multi-agent experiences, however, do not allow the development of personalized strategies to face individual opponents. In this paper, we propose a novel model composed of three neural layers that learn a representation of a competitive game, learn how to map the strategy of specific opponents, and how to disrupt them. The entire model is trained online, using a composed loss based on a contrastive optimization, to learn competitive and multiplayer games. We evaluate our model on a pokemon duel scenario and the four-player competitive Chef's Hat card game. Our experiments demonstrate that our model achieves better performance when playing against offline, online, and competitive-specific models, in particular when playing against the same opponent multiple times. We also present a discussion on the impact of our model, in particular on how well it deals with on specific strategy learning for each of the two scenarios.

Mental Rotation Skill Shapes Haptic Exploration Strategies.

Haptic exploration strategies have been traditionally studied focusing on hand movements and neglecting how objects are moved in space. However, in daily life situations touch and movement cannot be disentangled. Furthermore, the relation between object manipulation as well as performance in haptic tasks and spatial skill is still little understood. In this study, we used iCube, a sensorized cube recording its orientation in space as well as the location of the points of contact on its faces. Participants had to explore the cube faces where little pins were positioned in varying number and count the number of pins on the faces with either even or odd number of pins. At the end of this task, they also completed a standard visual mental rotation test (MRT). Results showed that higher MRT scores were associated with better performance in the task with iCube both in term of accuracy and exploration speed and exploration strategies associated with better performance were identified. High performers tended to rotate the cube so that the explored face had the same spatial orientation (i.e., they preferentially explored the upward face and rotated iCube to explore the next face in the same orientation). They also explored less often twice the same face and were faster and more systematic in moving from one face to the next. These findings indicate that iCube could be used to infer subjects' spatial skill in a more natural and unobtrusive fashion than with standard MRTs.

Affect Recognition in Hand-Object Interaction Using Object-Sensed Tactile and Kinematic Data.

We investigate the recognition of the affective states of a person performing an action with an object, by processing the object-sensed data. We focus on sequences of basic actions such as grasping and rotating, which are constituents of daily-life interactions. iCube, a 5 cm cube, was used to collect tactile and kinematics data that consist of tactile maps (without information on the pressure applied to the surface), and rotations. We conduct two studies: classification of i) emotions and ii) the vitality forms. In both, the participants perform a semi-structured task composed of basic actions. For emotion recognition, 237 trials by 11 participants associated with anger, sadness, excitement, and gratitude were used to train models using 10 hand-crafted features. The classifier accuracy reaches up to 82.7%. Interestingly, the same classifier when learned exclusively with the tactile data performs on par with its counterpart modeled with all 10 features. For the second study, 1135 trials by 10 participants were used to classify two vitality forms. The best-performing model differentiated gentle actions from rude ones with an accuracy of 84.85%. The results also confirm that people touch objects differently when performing these basic actions with different affective states and attitudes.

The role of reciprocity in human-robot social influence.

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.

Corrigendum: A Socially Adaptable Framework for Human-Robot Interaction.

[This corrects the article DOI: 10.3389/frobt.2020.00121.].

Affective Contagion: How Attitudes Expressed by Others Influence Our Perception of Actions.

Vitality forms represent a fundamental aspect of social interactions by characterizing how actions are performed and how words are pronounced on the basis of the attitude of the agent. Same action, such as a handshake, may have a different impact on the receiver when it is performed kindly or vigorously, and similarly, a gentle or rude tone of voice may have a different impact on the listener. In the present study, we carried out two experiments that aimed to investigate whether and how vocal requests conveying different vitality forms can influence the perception of goal-directed actions and to measure the duration of this effect over time. More specifically, participants were asked to listen to the voice of an actor pronouncing "give me" in a rude or gentle way. Then, they were asked to observe the initial part of a rude or a gentle passing action, continue it mentally, and estimate the time of its completion. Results showed that the perception of different vitality forms expressed by vocal requests influenced the estimation of action duration. Moreover, we found that this effect was limited to a certain time interval (800 ms), after which it started to decay.

I'm not a little kid anymore! Reciprocal social influence in child-adult interaction.

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.

You Were Always on My Mind: Introducing Chef's Hat and COPPER for Personalized Reinforcement Learning.

Reinforcement learning simulation environments pose an important experimental test bed and facilitate data collection for developing AI-based robot applications. Most of them, however, focus on single-agent tasks, which limits their application to the development of social agents. This study proposes the Chef's Hat simulation environment, which implements a multi-agent competitive card game that is a complete reproduction of the homonymous board game, designed to provoke competitive strategies in humans and emotional responses. The game was shown to be ideal for developing personalized reinforcement learning, in an online learning closed-loop scenario, as its state representation is extremely dynamic and directly related to each of the opponent's actions. To adapt current reinforcement learning agents to this scenario, we also developed the COmPetitive Prioritized Experience Replay (COPPER) algorithm. With the help of COPPER and the Chef's Hat simulation environment, we evaluated the following: (1) 12 experimental learning agents, trained via four different regimens (self-play, play against a naive baseline, PER, or COPPER) with three algorithms based on different state-of-the-art learning paradigms (PPO, DQN, and ACER), and two "dummy" baseline agents that take random actions, (2) the performance difference between COPPER and PER agents trained using the PPO algorithm and playing against different agents (PPO, DQN, and ACER) or all DQN agents, and (3) human performance when playing against two different collections of agents. Our experiments demonstrate that COPPER helps agents learn to adapt to different types of opponents, improving the performance when compared to off-line learning models. An additional contribution of the study is the formalization of the Chef's Hat competitive game and the implementation of the Chef's Hat Player Club, a collection of trained and assessed agents as an enabler for embedding human competitive strategies in social continual and competitive reinforcement learning.

Dynamic modulation of social influence by indirect reciprocity.

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.

Machines Like Us and People Like You: Toward Human-Robot Shared Experience.

In the past years, the field of collaborative robots has been developing fast, with applications ranging from health care to search and rescue, construction, entertainment, sports, and many others. However, current social robotics is still far from the general abilities we expect in a robot collaborator. This limitation is more evident when robots are faced with real-life contexts and activities occurring over long periods. In this article, we argue that human-robot collaboration is more than just being able to work side by side on complementary tasks: collaboration is a complex relational process that entails mutual understanding and reciprocal adaptation. Drawing on this assumption, we propose to shift the focus from "human-robot interaction" to "human-robot shared experience." We hold that for enabling the emergence of such shared experiential space between humans and robots, constructs such as coadaptation, intersubjectivity, individual differences, and identity should become the central focus of modeling. Finally, we suggest that this shift in perspective would imply changing current mainstream design approaches, which are mainly focused on functional aspects of the human-robot interaction, to the development of architectural frameworks that integrate the enabling dimensions of social cognition.