abstract

Your Eyes Never Lie: A Robot Magician Can Tell if You Are Lying

Authors:

Dario Pasquali,

Alexander Mois Aroyo,

Jonas Gonzalez-Billandon,

Giulio Sandini,

Alessandra SciuttiAuthors Info & Claims

HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

Pages 392 - 394

https://doi.org/10.1145/3371382.3378253

Published: 01 April 2020 Publication History

Abstract

Detecting lies in a real-world scenario is an important skill for a humanoid robot that aims to act as a teacher, a therapist, or a caregiver. In these contexts, it is essential to detect lies while preserving the pleasantness of the social interaction and the informality of the relation. This study investigates whether pupil dilation related to an increase in cognitive load can be used to swiftly identify a lie in an entertaining scenario. The iCub humanoid robot plays the role of a magician in a card game, telling which card the human partner is lying about. The results show a greater pupil dilation in presence of a false statement even if in front of a robot and without the need of a strictly controlled scenario. We developed a heuristic method (accuracy of 71.4% against 16.6% chance level) and a random forest classifier (precision and recall of 83.3%) to detect the false statement. Additionally, the current work suggests a potential method to assess the lying strategy of the partner.

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Cited By

Mayoral EPerez-Campos EPeláez FPeláezd R(2024)From Ancient Oracles to Modern Techniques: The Evolution of Deception Detection and the Benefits of Investigative InterviewingEuropean Polygraph10.2478/ep-2024-000218:1(11-42)Online publication date: 25-Jul-2024
https://doi.org/10.2478/ep-2024-0002
van Otterdijk MLaeng BSaplacan Lindblom DTorresen J(2024)The Effect of Expressive Robot Behavior on Users’ Mental Effort: A Pupillometry StudyIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.3352893(1-10)Online publication date: 2024
https://doi.org/10.1109/TCDS.2024.3352893
Prome SRagavan NIslam MAsirvatham DJegathesan A(2024)Deception detection using machine learning (ML) and deep learning (DL) techniques: A systematic reviewNatural Language Processing Journal10.1016/j.nlp.2024.1000576(100057)Online publication date: Mar-2024
https://doi.org/10.1016/j.nlp.2024.100057
Show More Cited By

Index Terms

Your Eyes Never Lie: A Robot Magician Can Tell if You Are Lying
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods

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Published In

cover image ACM Conferences

HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

March 2020

702 pages

ISBN:9781450370578

DOI:10.1145/3371382

General Chairs:
Tony Belpaeme
Ghent University, Belgium
,
James Young
University of Manitoba, Canada
,
Program Chairs:
Hatice Gunes
University of Cambridge, UK
,
Laurel Riek
UC San Diego, USA

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 01 April 2020

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HRI '20

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HRI '20: ACM/IEEE International Conference on Human-Robot Interaction

March 23 - 26, 2020

Cambridge, United Kingdom

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Cited By

Mayoral EPerez-Campos EPeláez FPeláezd R(2024)From Ancient Oracles to Modern Techniques: The Evolution of Deception Detection and the Benefits of Investigative InterviewingEuropean Polygraph10.2478/ep-2024-000218:1(11-42)Online publication date: 25-Jul-2024
https://doi.org/10.2478/ep-2024-0002
van Otterdijk MLaeng BSaplacan Lindblom DTorresen J(2024)The Effect of Expressive Robot Behavior on Users’ Mental Effort: A Pupillometry StudyIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.3352893(1-10)Online publication date: 2024
https://doi.org/10.1109/TCDS.2024.3352893
Prome SRagavan NIslam MAsirvatham DJegathesan A(2024)Deception detection using machine learning (ML) and deep learning (DL) techniques: A systematic reviewNatural Language Processing Journal10.1016/j.nlp.2024.1000576(100057)Online publication date: Mar-2024
https://doi.org/10.1016/j.nlp.2024.100057
Constâncio ATsunoda DSilva HSilveira JCarvalho D(2023)Deception detection with machine learning: A systematic review and statistical analysisPLOS ONE10.1371/journal.pone.028132318:2(e0281323)Online publication date: 9-Feb-2023
https://doi.org/10.1371/journal.pone.0281323
Strauch CHirzle TVan der Stigchel SBulling A(2021)Decoding binary decisions under differential target probabilities from pupil dilation: A random forest approachJournal of Vision10.1167/jov.21.7.621:7(6)Online publication date: 14-Jul-2021
https://doi.org/10.1167/jov.21.7.6
Pasquali DGonzalez-Billandon JRea FSandini GSciutti ABethel CPaiva ABroadbent EFeil-Seifer DSzafir D(2021)Magic iCubProceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3434073.3444682(293-302)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3434073.3444682
Kothig AMuñoz JMahdi HAroyo ADautenhahn K(2020)HRI Physio Lib: A Software Framework to Support the Integration of Physiological Adaptation in HRISocial Robotics10.1007/978-3-030-62056-1_4(36-47)Online publication date: 14-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-62056-1_4

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