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Pilacinski A, Pinto A, Oliveira S, Araújo E, Carvalho C, Silva PA, Matias R, Menezes P, Sousa S. The robot eyes don't have it. The presence of eyes on collaborative robots yields marginally higher user trust but lower performance. Heliyon 2023; 9:e18164. [PMID: 37520993 PMCID: PMC10382291 DOI: 10.1016/j.heliyon.2023.e18164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/21/2023] [Accepted: 07/10/2023] [Indexed: 08/01/2023] Open
Abstract
Eye gaze is a prominent feature of human social lives, but little is known on whether fitting eyes on machines makes humans trust them more. In this study we compared subjective and objective markers of human trust when collaborating with eyed and non-eyed robots of the same type. We used virtual reality scenes in which we manipulated distance and the presence of eyes on a robot's display during simple collaboration scenes. We found that while collaboration with eyed cobots resulted in slightly higher subjective trust ratings, the objective markers such as pupil size and task completion time indicated it was in fact less comfortable to collaborate with eyed robots. These findings are in line with recent suggestions that anthropomorphism may be actually a detrimental feature of collaborative robots. These findings also show the complex relationship between human objective and subjective markers of trust when collaborating with artificial agents.
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Affiliation(s)
- Artur Pilacinski
- Medical Faculty, Ruhr University Bochum, Bochum, Germany
- CINEICC - Center for Research in Neuropsychology and Cognitive Behavioral Intervention, University of Coimbra, Coimbra, Portugal
- Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - Ana Pinto
- Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
- CeBER – Centre for Business and Economics Research, University of Coimbra, Coimbra, Portugal
| | - Soraia Oliveira
- Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - Eduardo Araújo
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
- Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Carla Carvalho
- CINEICC - Center for Research in Neuropsychology and Cognitive Behavioral Intervention, University of Coimbra, Coimbra, Portugal
- Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - Paula Alexandra Silva
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
- Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
- CISUC - Centre for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Ricardo Matias
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
- Electrical and Computer Engineering Department, University of Coimbra, Coimbra, Portugal
| | - Paulo Menezes
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
- Electrical and Computer Engineering Department, University of Coimbra, Coimbra, Portugal
| | - Sonia Sousa
- University of Trás-os-Montes e Alto Douro, Vila Real, Portugal
- School of Digital Technologies, Tallinn University, Tallinn, Estonia
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Dimova-Edeleva V, Rivera OS, Laha R, Figueredo LFC, Zavaglia M, Haddadin S. Error-related Potentials in a Virtual Pick-and-Place Experiment: Toward Real-world Shared-control. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-7. [PMID: 38083754 DOI: 10.1109/embc40787.2023.10340244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In Human-Robot Collaboration setting a robot may be controlled by a user directly or through a Brain-Computer Interface that detects user intention, and it may act as an autonomous agent. As such interaction increases in complexity, conflicts become inevitable. Goal conflicts can arise from different sources, for instance, interface mistakes - related to misinterpretation of human's intention - or errors of the autonomous system to address task and human's expectations. Such conflicts evoke different spontaneous responses in the human's brain, which could be used to regulate intrinsic task parameters and to improve system response to errors - leading to improved transparency, performance, and safety. To study the possibility of detecting interface and agent errors, we designed a virtual pick and place task with sequential human and robot responsibility and recorded the electroencephalography (EEG) activity of six participants. In the virtual environment, the robot received a command from the participants through a computer keyboard or it moved as autonomous agent. In both cases, artificial errors were defined to occur in 20% - 25% of the trials. We found differences in the responses to interface and agent errors. From the EEG data, correct trials, interface errors, and agent errors were truly predicted for 51.62% ± 9.99% (chance level 38.21%) of the pick movements and 46.84%±6.62% (chance level 36.99%) for the place movements in a pseudo-asynchronous fashion. Our study suggests that in a human-robot collaboration setting one may improve the future performance of a system with intention detection and autonomous modes. Specific examples could be Neural Interfaces that replace and restore motor functions.
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Dimova-Edeleva V, Ehrlich SK, Cheng G. Brain computer interface to distinguish between self and other related errors in human agent collaboration. Sci Rep 2022; 12:20764. [PMID: 36456595 PMCID: PMC9715724 DOI: 10.1038/s41598-022-24899-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 11/22/2022] [Indexed: 12/05/2022] Open
Abstract
When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.
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Affiliation(s)
- Viktorija Dimova-Edeleva
- grid.6936.a0000000123222966Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany
| | - Stefan K. Ehrlich
- grid.6936.a0000000123222966TUM School of Computation, Information and Technology, Department of Computer Engineering, Institute of Cognitive Systems, Technical University of Munich, Munich, Germany
| | - Gordon Cheng
- grid.6936.a0000000123222966TUM School of Computation, Information and Technology, Department of Computer Engineering, Institute of Cognitive Systems, Technical University of Munich, Munich, Germany
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