Humanoid-based protocols to study social cognition

Social cognition is broadly defined as the way humans understand and process their interactions with other humans. In recent years, humans have become more and more used to interact with non-human agents, such as technological artifacts. Although these interactions have been restricted to human-controlled artifacts, they will soon include interactions with embodied and autonomous mechanical agents, i.e., robots. This challenge has motivated an area of research related to the investigation of human reactions towards robots, widely referred to as Human-Robot Interaction (HRI). Classical HRI protocols often rely on explicit measures, e.g., subjective reports. Therefore, they cannot address the quantification of the crucial implicit social cognitive processes that are evoked during an interaction. This thesis aims to develop a link between cognitive neuroscience and human-robot interaction (HRI) to study social cognition. This approach overcomes methodological constraints of both fields, allowing to trigger and capture the mechanisms of real-life social interactions while ensuring high experimental control. The present PhD work demonstrates this through the systematic study of the effect of online eye contact on gaze-mediated orienting of attention. The study presented in Publication I aims to adapt the gaze-cueing paradigm from cognitive science to an objective neuroscientific HRI protocol. Furthermore, it investigates whether the gaze-mediated orienting of attention is sensitive to the establishment of eye contact. The study replicates classic screen-based findings of attentional orienting mediated by gaze both at behavioral and neural levels, highlighting the feasibility and the scientific value of adding neuroscientific methods to HRI protocols. The aim of the study presented in Publication II is to examine whether and how real-time eye contact affects the dual-component model of joint attention orienting. To this end, cue validity and stimulus-to-onset asynchrony are also manipulated. The results show an interactive effect of strategic (cue validity) and social (eye contact) top-down components on the botton-up reflexive component of gaze-mediated orienting of attention. The study presented in Publication III aims to examine the subjective engagement and attribution of human likeness towards the robot depending on established eye contact or not during a joint attention task. Subjective reports show that eye contact increases human likeness attribution and feelings of engagement with the robot compared to a no-eye contact condition. The aim of the study presented in Publication IV is to investigate whether eye contact established by a humanoid robot affects objective measures of engagement (i.e. joint attention and fixation durations), and subjective feelings of engagement with the robot during a joint attention task. Results show that eye contact modulates attentional engagement, with longer fixations at the robot’s face and cueing effect when the robot establishes eye contact. In contrast, subjective reports show that the feeling of being engaged with the robot in an HRI protocol is not modulated by real-time eye contact. This study further supports the necessity for adding objective methods to HRI. Overall, this PhD work shows that embodied artificial agents can advance the theoretical knowledge of social cognitive mechanisms by serving as sophisticated interactive stimuli of high ecological validity and excellent experimental control. Moreover, humanoid-based protocols grounded in cognitive science can advance the HRI community by informing about the exact cognitive mechanisms that are present during HRI

Musical Robots For Children With ASD Using A Client-Server Architecture

Presented at the 22nd International Conference on Auditory Display (ICAD-2016)People with Autistic Spectrum Disorders (ASD) are known to have difficulty recognizing and expressing emotions, which affects their social integration. Leveraging the recent advances in interactive robot and music therapy approaches, and integrating both, we have designed musical robots that can facilitate social and emotional interactions of children with ASD. Robots communicate with children with ASD while detecting their emotional states and physical activities and then, make real-time sonification based on the interaction data. Given that we envision the use of multiple robots with children, we have adopted a client-server architecture. Each robot and sensing device plays a role as a terminal, while the sonification server processes all the data and generates harmonized sonification. After describing our goals for the use of sonification, we detail the system architecture and on-going research scenarios. We believe that the present paper offers a new perspective on the sonification application for assistive technologies

ERP markers of action planning and outcome monitoring in human – robot interaction

The present study aimed to examine event-related potentials (ERPs) of action planning and outcome monitoring in human-robot interaction. To this end, participants were instructed to perform costly actions (i.e. losing points) to stop a balloon from inflating and to prevent its explosion. They performed the task alone (individual condition) or with a robot (joint condition). Similar to findings from human-human interactions, results showed that action planning was affected by the presence of another agent, robot in this case. Specifically, the early readiness potential (eRP) amplitude was larger in the joint, than in the individual, condition. The presence of the robot affected also outcome perception and monitoring. Our results showed that the P1/N1 complex was suppressed in the joint, compared to the individual condition when the worst outcome was expected, suggesting that the presence of the robot affects attention allocation to negative outcomes of one's own actions. Similarly, results also showed that larger losses elicited smaller feedback-related negativity (FRN) in the joint than in the individual condition. Taken together, our results indicate that the social presence of a robot may influence the way we plan our actions and also the way we monitor their consequences. Implications of the study for the human-robot interaction field are discussed

EEG theta and Mu oscillations during perception of human and robot actions.

The perception of others' actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion. We explored whether sensorimotor mu (8-13 Hz) and frontal theta (4-8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other

The distracted robot: what happens when artificial agents behave like us

In everyday life, we are frequently exposed to different smart technologies. From our smartphones to avatars in computer games, and soon perhaps humanoid robots, we are surrounded by artificial agents created to interact with us. Already during the design phase of an artificial agent, engineers often endow it with functions aimed to promote the interaction and engagement with it, ranging from its \u201ccommunicative\u201d abilities to the movements it produces. Still, whether an artificial agent that can behave like a human could boost the spontaneity and naturalness of interaction is still an open question. Even during the interaction with conspecifics, humans rely partially on motion cues when they need to infer the mental states underpinning behavior. Similar processes may be activated during the interaction with embodied artificial agents, such as humanoid robots. At the same time, a humanoid robot that can faithfully reproduce human-like behavior may undermine the interaction, causing a shift in attribution: from being endearing to being uncanny. Furthermore, it is still not clear whether individual biases and prior knowledge related to artificial agents can override perceptual evidence of human-like traits. A relatively new area of research emerged in the context of investigating individuals\u2019 reactions towards robots, widely referred to as Human-Robot Interaction (HRI). HRI is a multidisciplinary community that comprises psychologists, neuroscientists, philosophers as well as roboticists, and engineers. However, HRI research has been often based on explicit measures (i.e. self-report questionnaires, a-posteriori interviews), while more implicit social cognitive processes that are elicited during the interaction with artificial agents took second place behind more qualitative and anecdotal results. The present work aims to demonstrate the usefulness of combining the systematic approach of cognitive neuroscience with HRI paradigms to further investigate social cognition processes evoked by artificial agents. Thus, this thesis aimed at exploring human sensitivity to anthropomorphic characteristics of a humanoid robot's (i.e. iCub robot) behavior, based on motion cues, under different conditions of prior knowledge. To meet this aim, we manipulated the human-likeness of the behaviors displayed by the robot and the explicitness of instructions provided to the participants, in both screen-based and real-time interaction scenarios. Furthermore, we explored some of the individual differences that affect general attitudes towards robots, and the attribution of human-likeness consequently

Brain response to a humanoid robot in areas implicated in the perception of human emotional gestures

BACKGROUND: The humanoid robot WE4-RII was designed to express human emotions in order to improve human-robot interaction. We can read the emotions depicted in its gestures, yet might utilize different neural processes than those used for reading the emotions in human agents. METHODOLOGY: Here, fMRI was used to assess how brain areas activated by the perception of human basic emotions (facial expression of Anger, Joy, Disgust) and silent speech respond to a humanoid robot impersonating the same emotions, while participants were instructed to attend either to the emotion or to the motion depicted. PRINCIPAL FINDINGS: Increased responses to robot compared to human stimuli in the occipital and posterior temporal cortices suggest additional visual processing when perceiving a mechanical anthropomorphic agent. In contrast, activity in cortical areas endowed with mirror properties, like left Broca’s area for the perception of speech, and in the processing of emotions like the left anterior insula for the perception of disgust and the orbitofrontal cortex for the perception of anger, is reduced for robot stimuli, suggesting lesser resonance with the mechanical agent. Finally, instructions to explicitly attend to the emotion significantly increased response to robot, but not human facial expressions in the anterior part of the left inferior frontal gyrus, a neural marker of motor resonance. CONCLUSIONS: Motor resonance towards a humanoid robot, but not a human, display of facial emotion is increased when attention is directed towards judging emotions. SIGNIFICANCE: Artificial agents can be used to assess how factors like anthropomorphism affect neural response to the perception of human actions

A Review of Possible EEG Markers of Abstraction, Attentiveness and Memorisation in Cyber-Physical Systems for Special Education

[EN]Cyber-physical systems (CPSs) for special education rely on effective mental and brain processing during the lesson, performed with the assistance of humanoid robots. The improved diagnostic ability of the CPS is a prerogative of the system for efficient technological support of the pedagogical process. The article focuses on the available knowledge of possible EEG markers of abstraction, attentiveness, and memorisation (in some cases combined with eye tracking) related to predicting effective mental and brain processing during the lesson. The role of processing abstraction is emphasised as the learning mechanism, which is given priority over the other mechanisms by the cognitive system. The main markers in focus are P1, N170, Novelty P3, RewP, N400, and P600. The description of the effects is accompanied by the analysis of some implications for the design of novel educational scenarios in inclusive classes.The presented research received funding from the EC for project CybSPEED,. 777720, H2020-MSCA-RISE-2017; the Bulgarian Research Fund project,. KP-06-H42/4 (2020-2023); and the project Competence Center "Intelligent mechatronic, eco-and energy saving systems and technologies". BG05M2OP0011.002-0023, OP Science and Education for Smart Growth (2014-2020)

Robots that Say ‘No’. Affective Symbol Grounding and the Case of Intent Interpretations

© 2017 IEEE. This article has been accepted for publication in a forthcoming issue of IEEE Transactions on Cognitive and Developmental Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-of-concept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A child-like humanoid robot is deployed in speech-wise unconstrained interaction with naïve human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.Peer reviewe

Measuring Human-Robot Interaction Through Motor Resonance

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