TY - JOUR
T1 - Trust and deception with high stakes
T2 - Evidence from the friend or foe dataset
AU - Chen, Xunyu
AU - Wang, Xinran
AU - Spitzley, Lee
AU - Nunamaker, Jay
N1 - Funding Information:
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023
Y1 - 2023
N2 - Many social interactions rely on the premise of mutual trust, but deception violates trust and poses risk. Empirically examining trust and deception, particularly in high-stakes situations, is challenging but essential for improving the research realism and generalizability. To address this difficulty, we study trusting and deceptive behaviors in a high-stakes situation by using a novel dataset created from an American game show, Friend or Foe (FoF). In the show, a contestant's reward was determined through a trust game modified from the prisoner's dilemma. We explore how numerous human behaviors including facial expressions, gaze, head pose, body motion, language, and socio-demographic attributes, were related to a contestant's trusting or deceptive decision. Using a data-driven approach, we find that the deceivers' (contestants who chose Foe) behavior featured a neutralized face, negative facial emotions, enhanced upper body motion, and language with a lower sense of immediacy and agreeableness. The contestants who chose to trust (chose Friend) exhibited opposite behavioral patterns. Socio-demographic factors such as age, height, and facial attractiveness were also associated with a contestant's choice. Combining multimodal information, machine learning classifiers could predict the contestant's choice with an accuracy about 25% greater than earlier reported human accuracy. We contribute to both trust and deception literature by examining the generalizability of trusting and deceptive behaviors to a new high-stakes scenario. We also add to the decision support literature by showing the superior predictive performances of combining behavioral and socio-demographic features. Furthermore, we contribute to the academic community by introducing the FoF dataset.
AB - Many social interactions rely on the premise of mutual trust, but deception violates trust and poses risk. Empirically examining trust and deception, particularly in high-stakes situations, is challenging but essential for improving the research realism and generalizability. To address this difficulty, we study trusting and deceptive behaviors in a high-stakes situation by using a novel dataset created from an American game show, Friend or Foe (FoF). In the show, a contestant's reward was determined through a trust game modified from the prisoner's dilemma. We explore how numerous human behaviors including facial expressions, gaze, head pose, body motion, language, and socio-demographic attributes, were related to a contestant's trusting or deceptive decision. Using a data-driven approach, we find that the deceivers' (contestants who chose Foe) behavior featured a neutralized face, negative facial emotions, enhanced upper body motion, and language with a lower sense of immediacy and agreeableness. The contestants who chose to trust (chose Friend) exhibited opposite behavioral patterns. Socio-demographic factors such as age, height, and facial attractiveness were also associated with a contestant's choice. Combining multimodal information, machine learning classifiers could predict the contestant's choice with an accuracy about 25% greater than earlier reported human accuracy. We contribute to both trust and deception literature by examining the generalizability of trusting and deceptive behaviors to a new high-stakes scenario. We also add to the decision support literature by showing the superior predictive performances of combining behavioral and socio-demographic features. Furthermore, we contribute to the academic community by introducing the FoF dataset.
KW - Affective computing
KW - Deception
KW - Game show
KW - Prisoner's dilemma
KW - Trust
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U2 - 10.1016/j.dss.2023.113997
DO - 10.1016/j.dss.2023.113997
M3 - Article
AN - SCOPUS:85159926356
SN - 0167-9236
JO - Decision Support Systems
JF - Decision Support Systems
M1 - 113997
ER -