TY - CHAP A1 - Manger, Carina A1 - Vogl, Annalena A1 - Rosbach, Emely A1 - Ziegler, Carina A1 - Riener, Andreas T1 - Automation's Not Perfect But Neither Are We: Unveiling Illusionary Control And Automation Bias In Automated Driving T2 - 15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings UR - https://doi.org/10.1145/3581961.3609897 KW - Automated Driving KW - Cognitive Biases KW - User Study KW - Illusionary Control KW - Automation Bias Y1 - 2023 UR - https://doi.org/10.1145/3581961.3609897 SN - 979-8-4007-0112-2 SP - 25 EP - 29 PB - ACM CY - New York ER - TY - JOUR A1 - Manger, Carina A1 - Vogl, Annalena A1 - Riener, Andreas T1 - Cognitive Biases in User Interaction with Automated Vehicles: The Influence of Explainability and Mental Models JF - Applied Sciences N2 - To develop truly human-centered automated systems, it is essential to acknowledge that human reasoning is prone to systematic deviations from rational judgment, known as Cognitive Biases. The present study investigated such flawed reasoning in the context of automated driving. In a multi-step study with N = 34 participants, the occurrence of four Cognitive Biases was examined: Truthiness Effect, Automation Bias, Action Bias, and Illusory Control. Additionally, the study explored how the Explainability of the automation’s behavior and the driver’s Mental Model influenced the manifestation of these biases. The findings indicate a notable susceptibility to the Truthiness Effect and Illusory Control, although all biases appeared highly dependent on the specific driving context. Moreover, Explainability strongly impacted the perceived credibility of information and participants’ agreement with the system’s behavior. Given the exploratory nature of the study, this work aims to initiate a discussion on how Cognitive Biases shape human reasoning and decision-making in interactions with automated vehicles. Based on the results, several directions for future research are proposed: (1) investigation of additional cognitive biases, (2) analysis of biases across different levels of automation, (3) exploration of mitigation strategies versus deliberate use of biases, (4) examination of dynamic and context-dependent manifestations, and (5) validation in high-fidelity simulations or real-world settings. UR - https://doi.org/10.3390/app152011030 Y1 - 2025 UR - https://doi.org/10.3390/app152011030 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63831 SN - 2076-3417 VL - 15 IS - 20 PB - MDPI CY - Basel ER -