Research Projects

Here is a sample of the on-going research projects in the lab. Always under construction.

Research Theme 1: Human-Automation Interaction

Trust toward Automated Systems/Agents

Human-Automation Trust is a critical factor that modulates operator behaviors in applied cognitive tasks. The overarching goal of this research is to determine trajectories of trust development for general automation users, using virtual environments, and support ideal levels of human-automation trust for the optimal use. Additionally, we are exploring how information sampling patterns and workload influence trust in a simulated multitasking environment.

Selected Publications:

  • Long, S. K., Lee, J., Yamani, Y., Unverricht, J., & Itoh, M. (in press). Does automation trust evolve from a leap of faith? An analysis using a reprogrammed pasteurizer simulation task. Applied Ergonomics. 

  • Lee, J., Yamani, Y., Long, S., K., Unverricht, J., & Itoh, M. (2021). Revisiting human-machine trust: A replication study of Muir & Moray (1996) using a simulated pasteurizer plant task. Ergonomics 64, 1132-1145.

  • Yamani, Y., Long, S. L., & Itoh, M. (2020). Human-automation trust to technologies for naïve users amidst and following the COVID-19 pandemic. Human Factors, 62(7), 1087-1094.

  • Bliss, J. P., Gao*, Q., Hu*, X., Itoh*, M., Long*, S., Papelis*, Y., & Yamani*, Y. (2019). Cross-cultural trust of robot peacekeepers: Investigation of robot appearance, weaponry, and task variants. Homeland Defense & Security Information Analysis Center (HDSIAC) Journal.

  • Chancey, E.T., Bliss, J.P., Yamani, Y., & Handley, H. (2017). Trust and the compliance-reliance paradigm: The effects of risk, error bias, and reliability on trust and dependence behaviors. Human Factors, 59, 333-345.

Automated Driving

Modern automation technologies have advanced to a point where drivers can be completely freed from supervision of autonomous vehicles. However, in-depth analysis of human performance in different levels of vehicle automation technology is necessary to model and control inherently complex interactions between human drivers and automated/autonomous vehicles. Our approach is to investigate drivers’ eye movement patterns and visual/attentional performance in a human-in-the-loop driving simulation and offer empirical guidelines to automation designers.

Selected Publications:

  • Samuel, S., Yamani, Y., & Fisher, D. L. (2020). Understanding latent hazard anticipation in partial vehicle automation systems. International Journal of Human Factors and Ergonomics, 7(3), 282-296.

  • Samuel, S., Yahoodik, S., Yamani, Y., Valluru, K., & Fisher, D. L. (2020). Ethical decision making behind the wheel – A driving simulator study. Transportation Research Interdisciplinary Perspectives. 

  • Hatfield, N., Yamani, Y., Palmer, D. B., Yahoodik, S., Vasquez, V., Horrey, W. J., & Samuel, S. (2019). An analysis of visual scanning patterns comparing drivers of simulated L2 and L0 systems. Transportation Research Record. 

  • Yamani, Y. & Horrey, W. J. (2018). A theoretical model of human-automation interaction grounded in attention allocation policy during automated driving. International Journal of Human Factors and Ergonomics, 5, 225-239.

Cognitive Modeling of Human-Autonomy Team Efficiency

This project applies a novel measure of parallel processing efficiency such as systems factorial technology, a tool to reveal the underlying architecture of multi-channel cognitive system developed in Mathematical Psychology, to quantify human-autonomy team performance. For example, workload capacity analysis offers an analytic means to quantitatively analyze human-autonomy joint performance in speeded perceptual-cognitive tasks. We are currently testing the effects of perceptual separability and integrality, cognitive load and levels of automation on human-autonomy team efficiency in decision-making tasks.

Selected Publications:

  • Scott-Sharoni, S., Yamani, Y., Kneeland, C., Long, S., Chen, J., & Houpt, J. W. (2021). Exploring the effects of perceptual separability on human-automation team efficiency. Computational Brain and Behavior. 

  • Yamani, Y. & McCarley, J. S. (2018). Effects of task difficulty and display format on automation usage strategy: A workload capacity analysis. Human Factors, 60, 527-537.

  • Yamani, Y., & McCarley, J.S. (2016). Workload capacity: An RT-based measure of automation dependence. Human Factors, 58, 462-471.

Research Theme 2: Transportation Safety

Higher Cognitive Skills in Young Drivers

Young drivers are known to poor at anticipating upcoming road hazard (latent hazard anticipation) and keeping their attention to the forward roadway when performing distracting in-vehicle tasks (attention maintenance) than experienced drivers. Using eye tracking technology and a high-fidelity driving simulator, we examine factors that influence the higher cognitive skills critical for young drivers’ road safety and for development of countermeasures such as in-vehicle technologies and driver training programs. Our current projects includes the effects of voluntary and involuntary takeovers on latent hazard anticipation during L3 automated driving.

Selected Publications:

  • Yamani, Y., Samuel, S., Yahoodik, S., & Fisher, D. L. (in press). Identifying and remedying failures of hazard anticipation in young drivers. Theoretical Issues in Ergonomics Science. 

  • Yahoodik, S. & Yamani, Y. (2021). Effectiveness of risk awareness perception training in dynamic simulator scenarios involving salient distractors. Transportation Research: Part F, 81, 295-305.

  • Unverricht, J., Yamani, Y., Chen, J., & Horrey, W. J. (2020). Minding the gap: Effects of a training program on driver calibration in attention maintenance. Human Factors. 

  • Unverricht, J., Samuel, S., & Yamani, Y. (2018). Latent hazard anticipation in young drivers: A review and meta-analysis of training studies. Transportation Research Record. 

Advanced Air Mobility

Advanced Air Mobility (AAM) is an emerging aviation ecosystem with increasingly automated technology that allows safe, reliable, and accessible aerial transportation of passengers and goods within and among rural and urban areas. AAM will rely on increasingly autonomous aircraft (e.g., drones) that will require automated systems to perform multiple tasks where a limited number of human operators interact and control them. We are investigating attentional limitations of human operators in simulated AAM tasks and develop a data-driven guideline for efficient and safe human-autonomy interactions in AAM operations.

Sponsor: NASA Langley Research Center

Publications:

  • Yamani, Y., Sato, T., & Inman, J. (2023). Attentional limits and advanced air mobility ecosystem: A model of attention allocation, trust, and system performance. Presented at AHFE 2023 Conference.

  • Sato, T., Islam, S., Still, J. D., Scerbo, M. W., & Yamani, Y. (2023). Task priority reduces an adverse effect of task load on automation trust: A multitasking flight simulator study. Cognition, Technology & Work, 25, 1-13.

  • Politowicz, M. S., Sato, T., Chancey, E. T., & Yamani, Y. (2022). Pathfinder networks for measuring operator mental model structure with a simple autopilot system. Proceedings of the Human Factors and Ergonomics Society 2022 Annual Meeting, 883-887.