Detecting Lies is a Child (Robot)’s Play
Gaze-Based Lie Detection in HRI
Bibliographic Data
| ID | 21199637 |
|---|---|
| Authors | Dario Pasquali (0000-0001-8185-8188, Italian Institute of Technology, corresponding author), Jonas Gonzalez-Billandon (0000-0001-9400-5678, Italian Institute of Technology), Alexander Mois Aroyo (0000-0003-2445-4026, University of Waterloo), Giulio Sandini (0000-0003-3324-985X, Italian Institute of Technology), Alessandra Sciutti (0000-0002-1056-3398, Italian Institute of Technology), Francesco Rea (0000-0001-8535-223X, Italian Institute of Technology) |
| Year | 2023 |
| Volume | 15 |
| Issue | 4 |
| Pages | 583-598 |
| Publication date | 2023-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Social Robotics (JOURNAL) |
| Journal identifiers | ISSN: 1875-4791 • E-ISSN: 1875-4805 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12369-021-00822-5 |
| OpenAlex | W3211992063 |
| Language | EN |
| Citations received | 1 |
| References cited | 65 |
Robots destined to tasks like teaching or caregiving have to build a long-lasting social rapport with their human partners. This requires, from the robot side, to be capable of assessing whether the partner is trustworthy. To this aim a robot should be able to assess whether someone is lying or not, while preserving the pleasantness of the social interaction. We present an approach to promptly detect lies based on the pupil dilation, as intrinsic marker of the lie-associated cognitive load that can be applied in an ecological human–robot interaction, autonomously led by a robot. We demonstrated the validity of the approach with an experiment, in which the iCub humanoid robot engages the human partner by playing the role of a magician in a card game and detects in real-time the partner deceptive behavior. On top of that, we show how the robot can leverage on the gained knowledge about the deceptive behavior of each human partner, to better detect subsequent lies of that individual. Also, we explore whether machine learning models could improve lie detection performances for both known individuals (within-participants) over multiple interaction with the same partner, and with novel partners (between-participant). The proposed setup, interaction and models enable iCub to understand when its partners are lying, which is a fundamental skill for evaluating their trustworthiness and hence improving social human–robot interaction
Cognitive psychology · Deception · Gaze · Human–computer interaction · Human–robot interaction · Humanoid robot · iCub · Lie detection · Lying · Mobile robot · Robot · Robot control · Social robot · Computer Science · Deception detection and forensic psychology · Psychology · Social Psychology · Social Robot Interaction and HRI · User Authentication and Security Systems · Artificial Intelligence
Cognitive Load Theory
Accuracy of Deception Judgments
Pupillometry
Introducing the Short Dark Triad (SD3)
Smote
A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
The role of trust in automation reliance
Random Forests
You Want Me to Trust a Robot? The Development of a Human–Robot Interaction Trust Scale
The Development of a Scale to Evaluate Trust in Industrial Human-robot Collaboration
Deception Detection and Relationship Development
Psychological significance of pupillary movements
Cues to deception
Implicit Theories, Working Memory, and Cognitive Load
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |