scholarly journals Precuneus Brain Response Changes Differently During Human-Robot and Human-Human Dyadic Social Interaction

2021 ◽  
Author(s):  
Nicolas Spatola ◽  
Thierry Chaminade
Keyword(s):  
Social Cognition ◽  
Cognitive Processes ◽  
Social Engagement ◽  
Brain Activity ◽  
Posterior Cingulate Cortex ◽  
Human Robot Interaction ◽  
Brain Response ◽  
Robot Interaction ◽  
The Social ◽  
Crucial Parameter

Abstract Human-human and human-robot interaction are often compared with the overarching question of the differences in terms of cognitive processes engaged and what can explain these differences. However, research addressing this topic, especially in neuro-imagery, use extremely artificial interaction settings. Also, they neglect a crucial parameter of human social cognition: interaction is an adaptive (rather than fixed) process. Building upon the first fMRI paradigm requiring participants to interact online with both a human and a robot in a dyadic setting, we investigate the differences and changes of brain activity during the two type of interactions in a whole brain analysis. Our results show that, grounding on a common default level, the activity in specific neural regions associated with social cognition (e.g. Posterior Cingulate Cortex) increase in HHI while remaining stable in HRI. We discuss these results regarding the iterative process of deepening the social engagement facing humans but not robots.

scholarly journals Social robotics and the modulation of social perception and bias

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180037 ◽  
Author(s):  
Joshua Skewes ◽  
David M. Amodio ◽  
Johanna Seibt
Keyword(s):  
Social Cognition ◽  
Impression Formation ◽  
Psychological Research ◽  
Social Robotics ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Mediated Communication ◽  
Robot Interaction ◽  
The Social ◽  
Identity Based

The field of social robotics offers an unprecedented opportunity to probe the process of impression formation and the effects of identity-based stereotypes (e.g. about gender or race) on social judgements and interactions. We present the concept of fair proxy communication—a form of robot-mediated communication that proceeds in the absence of potentially biasing identity cues—and describe how this application of social robotics may be used to illuminate implicit bias in social cognition and inform novel interventions to reduce bias. We discuss key questions and challenges for the use of robots in research on the social cognition of bias and offer some practical recommendations. We conclude by discussing boundary conditions of this new form of interaction and by raising some ethical concerns about the inclusion of social robots in psychological research and interventions. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Shall I trust you? From child human-robot interaction to trusting relationships

2019 ◽  
Author(s):  
Cinzia Di Dio ◽  
Federico Manzi ◽  
Giulia Peretti ◽  
Angelo Cangelosi ◽  
Paul L. Harris ◽  
...  
Keyword(s):  
Behavioral Pattern ◽  
Human Robot Interaction ◽  
School Age Children ◽  
Social Relevance ◽  
Robot Interaction ◽  
Cognitive Correlates ◽  
The Social ◽  
The Relationship

Studying trust within human-robot interaction is of great importance given the social relevance of robotic agents in a variety of contexts. We investigated the acquisition, loss and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in-vivo. The relationship between trust and the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. No differences were found in children’s trust in the play-partner as a function of agency (human or robot). Nevertheless, 3-years-olds showed a trend toward trusting the human more than the robot, while 7-years-olds displayed the reverse behavioral pattern, thus highlighting the developing interplay between affective and cognitive correlates of trust.

scholarly journals Perceptual and Semantic Processing in Cognitive Robots

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2216
Author(s):  
Syed Tanweer Shah Bukhari ◽  
Wajahat Mahmood Qazi
Keyword(s):  
Visual Perception ◽  
Cognitive Processes ◽  
Semantic Processing ◽  
Similarity Index ◽  
Semantic Networks ◽  
Human Robot Interaction ◽  
Conceptual Graphs ◽  
Average Precision ◽  
Robot Interaction ◽  
Cognitive Robots

The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to process object grounding and affordance learning from acquired knowledge. Affordance has been the driving force for agents to construct relationships between objects, their effects, and actions, whereas grounding is effective in the understanding of spatial maps of objects present in the environment. The main contribution of this paper is to propose a methodology for the extension of object affordance and grounding, the Bloom-based cognitive cycle, and the formulation of perceptual semantics for the context-based human–robot interaction. In this study, we implemented YOLOv3 to formulate visual perception and LSTM to identify the level of the cognitive cycle, as cognitive processes synchronized in the cognitive cycle. In addition, we used semantic networks and conceptual graphs as a method to represent knowledge in various dimensions related to the cognitive cycle. The visual perception showed average precision of 0.78, an average recall of 0.87, and an average F1 score of 0.80, indicating an improvement in the generation of semantic networks and conceptual graphs. The similarity index used for the lingual and visual association showed promising results and improves the overall experience of human–robot interaction.

scholarly journals Brain activity during reciprocal social interaction investigated using conversational robots as control condition

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180033 ◽  
Author(s):  
Birgit Rauchbauer ◽  
Bruno Nazarian ◽  
Morgane Bourhis ◽  
Magalie Ochs ◽  
Laurent Prévot ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Social Interaction ◽  
Medial Prefrontal Cortex ◽  
Brain Activity ◽  
Blood Oxygen Level Dependent ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Peripheral Blood Flow ◽  
Robot Interaction ◽  
Temporoparietal Junction

We present a novel functional magnetic resonance imaging paradigm for second-person neuroscience. The paradigm compares a human social interaction (human–human interaction, HHI) to an interaction with a conversational robot (human–robot interaction, HRI). The social interaction consists of 1 min blocks of live bidirectional discussion between the scanned participant and the human or robot agent. A final sample of 21 participants is included in the corpus comprising physiological (blood oxygen level-dependent, respiration and peripheral blood flow) and behavioural (recorded speech from all interlocutors, eye tracking from the scanned participant, face recording of the human and robot agents) data. Here, we present the first analysis of this corpus, contrasting neural activity between HHI and HRI. We hypothesized that independently of differences in behaviour between interactions with the human and robot agent, neural markers of mentalizing (temporoparietal junction (TPJ) and medial prefrontal cortex) and social motivation (hypothalamus and amygdala) would only be active in HHI. Results confirmed significantly increased response associated with HHI in the TPJ, hypothalamus and amygdala, but not in the medial prefrontal cortex. Future analysis of this corpus will include fine-grained characterization of verbal and non-verbal behaviours recorded during the interaction to investigate their neural correlates. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction'.

scholarly journals Enactivism and Robotic Language Acquisition: A Report from the Frontier

Philosophies ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 11 ◽  
Author(s):  
Frank Förster
Keyword(s):  
Language Acquisition ◽  
Scientific Investigation ◽  
Human Robot Interaction ◽  
Sense Making ◽  
Robot Interaction ◽  
Design Decisions ◽  
Potential Value ◽  
The Social ◽  
Architecture And Design

In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.

scholarly journals Social Cognition in the Age of Human–Robot Interaction

2020 ◽  
Vol 43 (6) ◽  
pp. 373-384 ◽  
Author(s):  
Anna Henschel ◽  
Ruud Hortensius ◽  
Emily S. Cross
Keyword(s):  
Social Cognition ◽  
Human Robot Interaction ◽  
Robot Interaction

scholarly journals The Fundamental Attribution Error in Human-Robot Interaction: An Experimental Investigation on Attributing Responsibility to a Social Robot for Its Pre-Programmed Behavior

2022 ◽  
Author(s):  
Aike C. Horstmann ◽  
Nicole C. Krämer
Keyword(s):  
Experimental Investigation ◽  
Attribution Theory ◽  
Social Robots ◽  
Social Robot ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Fundamental Attribution Error ◽  
Online Study ◽  
The Social ◽  
Attribution Error

AbstractSince social robots are rapidly advancing and thus increasingly entering people’s everyday environments, interactions with robots also progress. For these interactions to be designed and executed successfully, this study considers insights of attribution theory to explore the circumstances under which people attribute responsibility for the robot’s actions to the robot. In an experimental online study with a 2 × 2 × 2 between-subjects design (N = 394), people read a vignette describing the social robot Pepper either as an assistant or a competitor and its feedback, which was either positive or negative during a subsequently executed quiz, to be generated autonomously by the robot or to be pre-programmed by programmers. Results showed that feedback believed to be autonomous leads to more attributed agency, responsibility, and competence to the robot than feedback believed to be pre-programmed. Moreover, the more agency is ascribed to the robot, the better the evaluation of its sociability and the interaction with it. However, only the valence of the feedback affects the evaluation of the robot’s sociability and the interaction with it directly, which points to the occurrence of a fundamental attribution error.

scholarly journals I–C–E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

2020 ◽  
Vol 12 (6) ◽  
pp. 1213-1229
Author(s):  
Anna M. H. Abrams ◽  
Astrid M. Rosenthal-von der Pütten
Keyword(s):  
Social Sciences ◽  
Group Dynamics ◽  
Theoretical Foundation ◽  
Human Robot Interaction ◽  
Future Research ◽  
Robot Interaction ◽  
The Past ◽  
Social Settings ◽  
The Social ◽  
Near Future

AbstractThe research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: ingroup identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I–C–E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research.

scholarly journals Drawing parallels in human–other interactions: a trans-disciplinary approach to developing human–robot interaction methodologies

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180433 ◽  
Author(s):  
Emily C. Collins
Keyword(s):  
Social Cognition ◽  
Attachment Styles ◽  
Empirical Work ◽  
Human Robot Interaction ◽  
Proof Of Concept ◽  
Robot Interaction ◽  
Methodology Development ◽  
Psychological Measures ◽  
Object Interaction ◽  
Human Object

This opinion paper discusses how human–robot interaction (HRI) methodologies can be robustly developed by drawing on insights from fields outside of HRI that explore human–other interactions. The paper presents a framework that draws parallels between HRIs, and human–human, human–animal and human–object interaction literature, by considering the morphology and use of a robot to aid the development of robust HRI methodologies. The paper then briefly presents some novel empirical work as proof of concept to exemplify how the framework can help researchers define the mechanism of effect taking place within specific HRIs. The empirical work draws on known mechanisms of effect in animal-assisted therapy, and behavioural observations of touch patterns and their relation to individual differences in caring and attachment styles, and details how this trans-disciplinary approach to HRI methodology development was used to explore how an interaction with an animal-like robot was impacting a user. In doing so, this opinion piece outlines how useful objective, psychological measures of social cognition can be for deepening our understanding of HRI, and developing richer HRI methodologies, which take us away from questions that simply ask ‘Is this a good robot?’, and closer towards questions that ask ‘What mechanism of effect is occurring here, through which effective HRI is being performed?’ This paper further proposes that in using trans-disciplinary methodologies, experimental HRI can also be used to study human social cognition in and of itself. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Of Mental States and Machine Learning

2016 ◽  
Vol 60 (1) ◽  
pp. 1362-1366 ◽  
Author(s):  
Andrew Best ◽  
Samantha F. Warta ◽  
Katelynn A. Kapalo ◽  
Stephen M. Fiore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Cognition ◽  
Mental State ◽  
Response Times ◽  
Interdisciplinary Approach ◽  
Mental States ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Machine Learning Classifiers

Using research in social cognition as a foundation, we studied rapid versus reflective mental state attributions and the degree to which machine learning classifiers can be trained to make such judgments. We observed differences in response times between conditions, but did not find significant differences in the accuracy of mental state attributions. We additionally demonstrate how to train machine classifiers to identify mental states. We discuss advantages of using an interdisciplinary approach to understand and improve human-robot interaction and to further the development of social cognition in artificial intelligence.

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