Social cognition in the age of human–robot interaction

Artificial intelligence advances have led to robots endowed with increasingly sophisticated social abilities. These machines speak to our innate desire to perceive social cues in the environment, as well as the promise of robots enhancing our daily lives. However, a strong mismatch still exists between our expectations and the reality of social robots. We argue that careful delineation of the neurocognitive mechanisms supporting human–robot interaction will enable us to gather insights critical for optimising social encounters between humans and robots. To achieve this, the field must incorporate human neuroscience tools including mobile neuroimaging to explore long-term, embodied human–robot interaction in situ. New analytical neuroimaging approaches will enable characterisation of social cognition representations on a finer scale using sensitive and appropriate categorical comparisons (human, animal, tool, or object). The future of social robotics is undeniably exciting, and insights from human neuroscience research will bring us closer to interacting and collaborating with socially sophisticated robots

Comparison of Human Social Brain Activity During Eye-Contact With Another Human and a Humanoid Robot

Robot design to simulate interpersonal social interaction is an active area of research with applications in therapy and companionship. Neural responses to eye-to-eye contact in humans have recently been employed to determine the neural systems that are active during social interactions. Whether eye-contact with a social robot engages the same neural system remains to be seen. Here, we employ a similar approach to compare human-human and human-robot social interactions. We assume that if human-human and human-robot eye-contact elicit similar neural activity in the human, then the perceptual and cognitive processing is also the same for human and robot. That is, the robot is processed similar to the human. However, if neural effects are different, then perceptual and cognitive processing is assumed to be different. In this study neural activity was compared for human-to-human and human-to-robot conditions using near infrared spectroscopy for neural imaging, and a robot (Maki) with eyes that blink and move right and left. Eye-contact was confirmed by eye-tracking for both conditions. Increased neural activity was observed in human social systems including the right temporal parietal junction and the dorsolateral prefrontal cortex during human-human eye contact but not human-robot eye-contact. This suggests that the type of human-robot eye-contact used here is not sufficient to engage the right temporoparietal junction in the human. This study establishes a foundation for future research into human-robot eye-contact to determine how elements of robot design and behavior impact human social processing within this type of interaction and may offer a method for capturing difficult to quantify components of human-robot interaction, such as social engagement

New approaches to the emerging social neuroscience of human-robot interaction

Prehistoric art, like the Venus of Willendorf sculpture, shows that we have always looked for ways to distil fundamental human characteristics and capture them in physically embodied representations of the self. Recently, this undertaking has gained new momentum through the introduction of robots that resemble humans in their shape and their behaviour. These social robots are envisioned to take on important roles: alleviate loneliness, support vulnerable children and serve as helpful companions for the elderly. However, to date, few commercially available social robots are living up to these expectations. Given their importance for an ever older and more socially isolated society, rigorous research at the intersection of psychology, social neuroscience and human-robot interaction is needed to determine to which extent mechanisms active during human-human interaction can be co-opted when we encounter social robots. This thesis takes an anthropocentric approach to answering the question how socially motivated we are to interact with humanoid robots. Across three empirical and one theoretical chapter, I use self-report, behavioural and neural measures relevant to the study of interactions with robots to address this question. With the Social Motivation Theory of Autism as a point of departure, the first empirical chapter (Chapter 3) investigates the relevance of interpersonal synchrony for human-robot interaction. This chapter reports a null effect: participants did not find a robot that synchronised its movement with them on a drawing task more likeable, nor were they more motivated to ask it more questions in a semi-structured interaction scenario. As this chapter heavily relies on self-report as a main outcome measure, Chapter 4 addresses this limitation by adapting an established behavioural paradigm for the study of human-robot interaction. This chapter shows that a failure to conceptually extend an effect in the field of social attentional capture calls for a different approach when seeking to adapt paradigms for HRI. Chapter 5 serves as a moment of reflection on the current state-of-the-art research at the intersection of neuroscience and human-robot interaction. Here, I argue that the future of HRI research will rely on interaction studies with mobile brain imaging systems (like functional near-infrared spectroscopy) that allow data collection during embodied encounters with social robots. However, going forward, the field should slowly and carefully move outside of the lab and into real situations with robots. As the previous chapters have established, well-known effects have to be replicated before they are implemented for robots, and before they are taken out of the lab, into real life. The final empirical chapter (Chapter 6), takes the first step of this proposed slow approach: in addition to establishing the detection rate of a mobile fNIRS system in comparison to fMRI, this chapter contributes a novel way to digitising optode positions by means of photogrammetry. In the final chapter of this thesis, I highlight the main lessons learned conducting studies with social robots. I propose an updated roadmap which takes into account the problems raised in this thesis and emphasise the importance of incorporating more open science practices going forward. Various tools that emerged out of the open science movement will be invaluable for researchers working on this exciting, interdisciplinary endeavour

A Survey of Brain Computer Interface Using Non-Invasive Methods

Research on Brain-Computer Interface (BCI) began in the 1970s and has increased in volume and diversified significantly since then. Today BCI is widely used for applications like assistive devices for physically challenged users, mental state monitoring, input devices for hands-free applications, marketing, education, security, games and entertainment. This article explores the advantages and disadvantages of invasive and non-invasive BCI technologies and focuses on use cases of several non-invasive technologies, namely electroencephalogram (EEG), functional Magnetic Resonance Imaging (fMRI), Near Infrared Spectroscopy (NIRs) and hybrid systems

Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

Functional Near-Infrared Spectroscopy (fNIRS) is an innovative and promising neuroimaging modality for studying brain activity in real-world environments. While fNIRS has seen rapid advancements in hardware, software, and research applications since its emergence nearly 30 years ago, limitations still exist regarding all three areas, where existing practices contribute to greater bias within the neuroscience research community. We spotlight fNIRS through the lens of different end-application users, including the unique perspective of a fNIRS manufacturer, and report the challenges of using this technology across several research disciplines and populations. Through the review of different research domains where fNIRS is utilized, we identify and address the presence of bias, specifically due to the restraints of current fNIRS technology, limited diversity among sample populations, and the societal prejudice that infiltrates today's research. Finally, we provide resources for minimizing bias in neuroscience research and an application agenda for the future use of fNIRS that is equitable, diverse, and inclusive

EEGs as potential predictors of virtual agents' acceptance

Over the last decade, much effort has been made to develop robots and virtual agents acting as assistants of elderly people in order to support them in their daily activities. In this context users' acceptance of such virtual assistants is fundamental for engaging them in order to maximize the assistance's effectiveness and users' comfort. Therefore, improving assessment techniques for elders' acceptance of virtual agents is necessary for understanding the impressions they arouse and determining their design accordingly. This paper is a proposition to introduce an EEG emotion detection procedure to gain further insight in implementing effective virtual agents' acceptance

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 217, March 1981

Approximately 130 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981 are included in this bibliography. Topics include aerospace medicine and biology