scholarly journals I, robot: depression plays different roles in human–human and human–robot interactions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dandan Zhang ◽  
Junshi Shen ◽  
Sijin Li ◽  
Kexiang Gao ◽  
Ruolei Gu
Keyword(s):  
Performance Feedback ◽  
Social Life ◽  
Late Phase ◽  
Feedback Condition ◽  
Reward Processing ◽  
Human Robot Interaction ◽  
Event Related Potential ◽  
Human Interaction ◽  
Social Reward ◽  
Mild Depression

AbstractSocially engaging robots have been increasingly applied to alleviate depressive symptoms and to improve the quality of social life among different populations. Seeing that depression negatively influences social reward processing in everyday interaction, we investigate this influence during simulated interactions with humans or robots. In this study, 35 participants with mild depression and 35 controls (all from nonclinical populations) finished the social incentive delay task with event-related potential recording, in which they received performance feedback from other persons or from a robot. Compared to the controls, the mild depressive symptom (MDS) group represented abnormalities of social reward processing in the human feedback condition: first, the MDS group showed a lower hit rate and a smaller contingent-negative variation (correlated with each other) during reward anticipation; second, depression level modulated both the early phase (indexed by the feedback-related negativity (FRN)) and the late phase (indexed by the P3) of reward consumption. In contrast, the effect of depression was evident only on FRN amplitude in the robot feedback condition. We suggest that compared to human–human interaction, the rewarding properties of human–robot interaction are less likely to be affected by depression. These findings have implications for the utilization of robot-assisted intervention in clinical practice.

Differentiating the abnormalities of social and monetary reward processing associated with depressive symptoms

2020 ◽  
pp. 1-15
Author(s):  
Dandan Zhang ◽  
Junshi Shen ◽  
Rong Bi ◽  
Yueyao Zhang ◽  
Fang Zhou ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Depressive Symptomatology ◽  
Reward Processing ◽  
Event Related Potential ◽  
Control Group ◽  
Social Reward ◽  
Small Magnitude ◽  
Hit Rate ◽  
Reward Anticipation ◽  
Monetary Incentive Delay

Abstract Background Reward dysfunction is a major dimension of depressive symptomatology, but it remains obscure if that dysfunction varies across different reward types. In this study, we focus on the abnormalities in anticipatory/consummatory processing of monetary and social reward associated with depressive symptoms. Methods Forty participants with depressive symptoms and forty normal controls completed the monetary incentive delay (MID) and social incentive delay (SID) tasks with event-related potential (ERP) recording. Results In the SID but not the MID task, both the behavioral hit rate and the ERP component contingent negative variation (CNV; indicating reward anticipation) were sensitive to the interaction between the grouping factor and reward magnitude; that is, the depressive group showed a lower hit rate and a smaller CNV to large-magnitude (but not small-magnitude) social reward cues compared to the control group. Further, these two indexes were correlated with each other. Meanwhile, the ERP components feedback-related negativity and P3 (indicating reward consumption) were sensitive to the main effect of depression across the MID and SID tasks, though this effect was more prominent in the SID task. Conclusions Overall, we suggest that depressive symptoms are associated with deficits in both the reward anticipation and reward consumption stages, particularly for social rewards. These findings have a potential to characterize the profile of functional impairment that comprises and maintains depression.

Principles of a Successful Social Life, In The Light of Islamic Teachings

2020 ◽  
Vol 3 (3) ◽  
pp. 37-50
Author(s):  
Muhammad Suleman Nasir
Keyword(s):  
Social Life ◽  
Code Of Conduct ◽  
Well Being ◽  
Human Interaction ◽  
Way Of Life ◽  
The People ◽  
Analytical Research ◽  
Sound Foundation ◽  
Human Intellect ◽  
Islamic Society

Society means a group of people who are living together. People need society from birth to death. Without a collective life, man's deeds, intentions, and habits have no value. Islamic society is the name of a balanced and moderate life in which human intellect, customs, and social etiquette are determined in the light of divine revelation. This system is so comprehensive and all-encompassing that it covers all aspects and activities of life. Islam is a comprehensive, universal, complete code of conduct, and an ideal way of life It not only recognizes the collectiveness of human interaction. Rather, it helps in the development of the community and gives it natural principles that strengthen the community and provides good foundations for it and eliminates the factors that spoil it or make it limited and useless. The Principles of a successful social life in Islamic society seem to reflect the Islamic code of conduct and human nature. Islam is the only religion that advocates goodness and guarantees well-being. Islam gives us self-sacrifice, generosity, trust and honesty, service to the people, justice and fairness, forgiveness and kindness, good society and economy, good deeds, mutual unity, harmony, and brotherhood. Only by practicing the pure thoughts, beliefs, and unparalleled ideas of the religion of Islam, can a person live a prosperous life and he can feel real peace and lasting contentment in the moments of his life. A descriptive and analytical research methodology will be used in this study. It is concluded that for a prosperous social life it is necessary to abide by the injunction of Islamic principles, which provides a sound foundation for a successful social life here in the world and hereafter.

The role and relationship of mindreading and social attunement in HRI – position statements of interdisciplinary researchers. Workshop HRI'20

2020 ◽  
Author(s):  
Agnieszka Wykowska ◽  
Jairo Pérez-Osorio ◽  
Stefan Kopp
Keyword(s):  
Mental States ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Artificial Agents ◽  
The Novel ◽  
Robot Interaction ◽  
Novel Coronavirus ◽  
Relationship Of ◽  
The Relationship

This booklet is a collection of the position statements accepted for the HRI’20 conference workshop “Social Cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction” (Wykowska, Perez-Osorio & Kopp, 2020). Unfortunately, due to the rapid unfolding of the novel coronavirus at the beginning of the present year, the conference and consequently our workshop, were canceled. On the light of these events, we decided to put together the positions statements accepted for the workshop. The contributions collected in these pages highlight the role of attribution of mental states to artificial agents in human-robot interaction, and precisely the quality and presence of social attunement mechanisms that are known to make human interaction smooth, efficient, and robust. These papers also accentuate the importance of the multidisciplinary approach to advance the understanding of the factors and the consequences of social interactions with artificial agents.

scholarly journals Design choices: Mechanism design and platform capitalism

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110343
Author(s):  
Salomé Viljoen ◽  
Jake Goldenfein ◽  
Lee McGuigan
Keyword(s):  
Economic Theory ◽  
Computer Science ◽  
Mechanism Design ◽  
Social Life ◽  
Online Advertising ◽  
Social Costs ◽  
Human Interaction ◽  
Digital Platforms ◽  
Market Rules ◽  
Work Mechanism

Mechanism design is a form of optimization developed in economic theory. It casts economists as institutional engineers, choosing an outcome and then arranging a set of market rules and conditions to achieve it. The toolkit from mechanism design is widely used in economics, policymaking, and now in building and managing online environments. Mechanism design has become one of the most pervasive yet inconspicuous influences on the digital mediation of social life. Its optimizing schemes structure online advertising markets and other multi-sided platform businesses. Whatever normative rationales mechanism design might draw on in its economic origins, as its influence has grown and its applications have become more computational, we suggest those justifications for using mechanism design to orchestrate and optimize human interaction are losing traction. In this article, we ask what ideological work mechanism design is doing in economics, computer science, and its applications to the governance of digital platforms. Observing mechanism design in action in algorithmic environments, we argue it has become a tool for producing information domination, distributing social costs in ways that benefit designers, and controlling and coordinating participants in multi-sided platforms.

scholarly journals Social reward processing: A biomarker for predicting psychosis risk?

2020 ◽  
Vol 226 ◽  
pp. 129-137 ◽  
Author(s):  
Andrea Pelletier-Baldelli ◽  
Joseph M. Orr ◽  
Jessica A. Bernard ◽  
Vijay A. Mittal
Keyword(s):  
Reward Processing ◽  
Social Reward ◽  
Psychosis Risk

P.334 An event-related potential study of reward processing in subjects with schizophrenia and healthy controls

2020 ◽  
Vol 31 ◽  
pp. S72-S73
Author(s):  
Ö. Akgül ◽  
E. Fide ◽  
F. Özel ◽  
K. Alptekin ◽  
G. Yener ◽  
...  
Keyword(s):  
Reward Processing ◽  
Event Related Potential ◽  
Healthy Controls ◽  
Potential Study

scholarly journals The Role of Social Reward and Corticostriatal Connectivity in Substance Use

2020 ◽  
Author(s):  
Daniel Sazhin ◽  
Angelique Frazier ◽  
Caleb River Haynes ◽  
Camille Johnston ◽  
Iris Ka-Yi Chat ◽  
...  
Keyword(s):  
Substance Use ◽  
Social Context ◽  
Orbitofrontal Cortex ◽  
Strategic Behavior ◽  
Reward Processing ◽  
Reward Sensitivity ◽  
Social Reward ◽  
Neural Responses ◽  
Social Stimuli ◽  
Neuroimaging Study

This report describes an ongoing R03 grant that explores the links between trait reward sensitivity, substance use, and neural responses to social and nonsocial reward. Although previous research has shown that trait reward sensitivity and neural responses to reward are linked to substance use, whether this relationship is impacted by how people process social stimuli remains unclear. We are investigating these questions via a neuroimaging study with college-aged participants, using individual difference measures that examine the relation between substance use, social context, and trait reward sensitivity with tasks that measure reward anticipation, strategic behavior, social reward consumption, and the influence of social context on reward processing. We predict that substance use will be tied to distinct patterns of striatal dysfunction. Specifically, reward hyposensitive individuals will exhibit blunted striatal responses to social and non-social reward and enhanced connectivity with the orbitofrontal cortex; in contrast, reward hypersensitive individuals will exhibit enhanced striatal responses to social and non-social reward and blunted connectivity with the orbitofrontal cortex. We also will examine the relation between self-reported reward sensitivity, substance use, and striatal responses to social reward and social context. We predict that individuals reporting the highest levels of substance use will show exaggerated striatal responses to social reward and social context, independent of self-reported reward sensitivity. Examining corticostriatal responses to reward processing will help characterize the relation between reward sensitivity, social context and substance use while providing a foundation for understanding risk factors and isolating neurocognitive mechanisms that may be targeted to increase the efficacy of interventions.

scholarly journals Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6438
Author(s):  
Chiara Filippini ◽  
David Perpetuini ◽  
Daniela Cardone ◽  
Arcangelo Merla
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Affective Computing ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Expression Recognition ◽  
Robot Interaction ◽  
Interaction Field ◽  
Nao Robot

An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s.

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