scholarly journals Mind Perception in a Competitive Human-Robot Interaction Game

2019 ◽  
Vol 63 (1) ◽  
pp. 1957-1961
Author(s):  
Levern Q. Currie ◽  
Eva Wiese
Keyword(s):  
Reaction Times ◽  
Human Robot Interaction ◽  
Action Understanding ◽  
Healthcare Education ◽  
Action Perception ◽  
Action Execution ◽  
Robot Interaction ◽  
Emotional Skills ◽  
Mind Perception ◽  
Interaction Game

Robotic agents are becoming increasingly pervasive in society, and have already begun advancing fields such as healthcare, education, and industry. However, despite their potential to do good for society, many people still feel unease when imaging a future where robots and humans work and live together in shared environments, partly because robots are not generally trusted or ascribed human-like socio-emotional skills such as mentalizing and empathizing. In addition, performing tasks conjointly with robots can be frustrating and ineffective partially due to the fact that neuronal networks involved in action understanding and execution (i.e., the action-perception network; APN) are underactivated in human-robot interaction (HRI). While a number of studies has linked underactivation in APN to reduced abilities to predict a robot’s actions, little is known about how performing a competitive task together with a robot affects one’s own ability to execute or suppress an action. In the current experiment, we use a Go/No-Go task that requires participants to give a response on Go trials and suppress a response on No-Go trials to examine whether the performance of human players is impacted by whether they play the game against a robot believed to be controlled by a human as opposed to being pre-programmed. Preliminary data shows higher false alarm rates on No-Go trials, higher hit rates on Go trials, longer reaction times on Go trials and higher inverse efficiency scores in the human-controlled versus the pre-programmed condition. The results show that mind perception (here: perceiving actions as human-controlled) significantly impacted action execution of human players in a competitive human-robot interaction game.

scholarly journals Measuring engagement elicited by eye contact in Human-Robot Interaction

2019 ◽  
Author(s):  
Kyveli Kompatsiari ◽  
Francesca Ciardo ◽  
Davide De Tommaso ◽  
Agnieszka Wykowska
Keyword(s):  
Joint Attention ◽  
Reaction Times ◽  
Eye Contact ◽  
Human Robot Interaction ◽  
Implicit Measures ◽  
Subjective Ratings ◽  
Cognitive Mechanisms ◽  
Robot Interaction ◽  
Subjective Evaluations ◽  
Explicit Measures

The present study aimed at investigating how eye contact established by a humanoid robot affects engagement in human-robot interaction (HRI). To this end, we combined explicit subjective evaluations with implicit measures, i.e. reaction times and eye tracking. More specifically, we employed a gaze cueing paradigm in HRI protocol involving the iCub robot. Critically, before moving its gaze, iCub either established eye contact or not with the user. We investigated the patterns of fixations of participants’ gaze on the robot’s face, joint attention and the subjective ratings of engagement as a function of eye contact or no eye contact. We found that eye contact affected implicit measures of engagement, i.e. longer fixation times on the robot’s face during eye contact, and joint attention elicited only after the robot established eye contact. On the contrary, explicit measures of engagement with the robot did not vary across conditions. Our results highlight the value of combining explicit with implicit measures in an HRI protocol in order to unveil underlying human cognitive mechanisms, which might be at stake during the interactions. These mechanisms could be crucial for establishing an effective and engaging HRI, and could potentially provide guidelines to the robotics community with respect to better robot design.

scholarly journals Automatic Learning Improves Human-Robot Interaction in Productive Environments

2017 ◽  
Vol 7 (3) ◽  
pp. 65-75 ◽  
Author(s):  
Mauricio Andres Zamora Hernandez ◽  
Eldon Caldwell Marin ◽  
Jose Garcia-Rodriguez ◽  
Jorge Azorin-Lopez ◽  
Miguel Cazorla
Keyword(s):  
Computer Vision ◽  
Occupational Safety ◽  
Industrial Revolution ◽  
Technological Development ◽  
Reaction Times ◽  
Human Robot Interaction ◽  
Automatic Learning ◽  
Robot Interaction ◽  
Planning Processes ◽  
Increase Productivity

In the creation of new industries, products and services -- all of which are advances of the Fourth Industrial Revolution -- the human-robot interaction that includes automatic learning and computer vision are elements to consider since they promote collaborative environments between people and robots. The use of machine learning and computer vision provides the tools needed to increase productivity and minimizes delivery reaction times by assisting in the optimization of complex production planning processes. This review of the state of the art presents the main trends that seek to improve human-robot interaction in productive environments, and identifies challenges in research as well as in industrial - technological development in this topic. In addition, this review offers a proposal on the needs of use of artificial intelligence in all processes of industry 4.0 as a crucial linking element among humans, robots, intelligent and traditional machines; as well as a mechanism for quality control and occupational safety.

Automatic Learning Improves Human-Robot Interaction in Productive Environments

2018 ◽  
pp. 2014-2024
Author(s):  
Mauricio Andres Zamora Hernandez ◽  
Eldon Caldwell Marin ◽  
Jose Garcia-Rodriguez ◽  
Jorge Azorin-Lopez ◽  
Miguel Cazorla
Keyword(s):  
Computer Vision ◽  
Occupational Safety ◽  
Industrial Revolution ◽  
Technological Development ◽  
Reaction Times ◽  
Human Robot Interaction ◽  
Automatic Learning ◽  
Robot Interaction ◽  
Planning Processes ◽  
Increase Productivity

In the creation of new industries, products and services -- all of which are advances of the Fourth Industrial Revolution -- the human-robot interaction that includes automatic learning and computer vision are elements to consider since they promote collaborative environments between people and robots. The use of machine learning and computer vision provides the tools needed to increase productivity and minimizes delivery reaction times by assisting in the optimization of complex production planning processes. This review of the state of the art presents the main trends that seek to improve human-robot interaction in productive environments, and identifies challenges in research as well as in industrial - technological development in this topic. In addition, this review offers a proposal on the needs of use of artificial intelligence in all processes of industry 4.0 as a crucial linking element among humans, robots, intelligent and traditional machines; as well as a mechanism for quality control and occupational safety.

scholarly journals Effect of brain stimulation on mechanisms of social cognition is modulated by individual preferences for human versus robot agents

2019 ◽  
Vol 63 (1) ◽  
pp. 858-862 ◽  
Author(s):  
Abdulaziz Abubshait ◽  
Eva Wiese
Keyword(s):  
Cognitive Processes ◽  
Brain Stimulation ◽  
Social Cognitive ◽  
Human Robot Interaction ◽  
Gaze Following ◽  
Attentional Orienting ◽  
Social Brain ◽  
Robot Interaction ◽  
Mind Perception ◽  
Neural Substrate

When we interact with others, we use nonverbal behavior such as changes in gaze direction to make inferences about what people think or what they want to do next – a process called mentalizing. Previous studies have shown that how we react to others’ gaze signals depends on how much “mind” we ascribe to the gazer, and that this process of mind perception is related to activation in brain areas that process social information (i.e., social brain). Although brain stimulation studies have identified prefrontal structures like the ventromedial prefrontal cortex (vmPFC) as the potential neural substrate through which mind perception modulates social-cognitive processes like attentional orienting to gaze cues (i.e., gaze following), little is known about whether and how individual differences in preferences for human versus robot agents modulate this relationship. To address this question, the current study examines how transcranial direct current stimulation (tDCS) of left prefrontal versus left temporo-parietal areas affects attentional orienting to gaze signals as a function of the participants’ preferences for human ( Human Gaze Followers, HGF) versus robot ( Robot Gaze Followers; RGF) agents at baseline (prior to brain stimulation). Results show that prefrontal (but not temporo-parietal) stimulation positively affected attentional orienting to gaze signals for HGFs for the human but not the robot gazer; RGFs showed no effect of brain stimulation in neither of the stimulation conditions. These findings inform how preferences for human versus nonhuman agent types can influence subsequent interactions and communications in human-robot interaction.

Does Long-Term Exposure To Robots Affect Mind Perception? An Exploratory Study

2020 ◽  
Vol 64 (1) ◽  
pp. 1820-1824
Author(s):  
Min Ji Kim ◽  
Spencer Kohn ◽  
Tyler Shaw
Keyword(s):  
Exploratory Study ◽  
Human Robot Interaction ◽  
University Campus ◽  
Robot Interaction ◽  
Mind Perception ◽  
The Everyday ◽  
And Behavior ◽  
Interaction Research ◽  
Influence Behavior

From cleaning houses to assisting airport passengers, robots are rapidly becoming an integral part of society. As they transition into a facet of the everyday environment, it’s important to understand how beliefs and behavior concerning these robots will change over time. While human-robot interaction research is common, there is a significant literature gap concerning long-term interaction with these agents. The current study seeks to better understand how exposure over a time-scale of months changes mind perception towards robots. This area of research is especially critical given that mind perception has been shown to influence behavior. A fortuitous deployment of robots across the authors’ university campus provided an unprecedented opportunity to study this interaction via a naturalistic experiment.

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