Cognitive Modeling of Automation Adaptation in a Time Critical Task

Abstract

This paper presents a cognitive model that simulates an adaptation process to automation in a time-critical task. The paper uses a simple tracking task (which represents vehicle operation) to reveal how the reliance on automation changes as the success probabilities of the automatic and manual mode vary. The model was developed by using a cognitive architecture, ACT-R (Adaptive Control of Thought-Rational). We also introduce two methods of reinforcement learning: the summation of rewards over time and a gating mechanism. The model performs this task through productions that manage perception and motor control. The utility values of these productions are updated based on rewards in every perception-action cycle. A run of this model simulated the overall trends of the behavioral data such as the performance (tracking accuracy), the auto use ratio, and the number of switches between the two modes, suggesting some validity of the assumptions made in our model. This work shows how combining different paradigms of cognitive modeling can lead to practical representations and solutions to automation and trust in automation.

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

“…Cognitive representations can also be utilized to define an agent intended to interact or coordinate with a human. For example, [84] utilizes a cognitive architecture and RL concepts to predict and model behaviors in a HAI context.…”

Change in Human (Moral) Decision-Making and Performance with Intelligent Machines: The Good, the Bad, and the Ugly in Human-Autonomous Systems Interactions

“…Applications of computational modeling to learning and decision-making phenomena have shown several important successes [e.g., 120], and we strongly believe that this could also be beneficial for better understanding human decision-making and performance in the context of human-automation interaction. This suggestion has already been proposed in the literature [e.g., 121] and computational models of human decision-making during humanautonomous system interaction have been developed [e.g., [122][123][124][125]. To date, however, the proportion of models using computational methods remains relatively small, and much more investigations are needed for the systematic use of these models.…”

A Cognitive Model to Anticipate Variations of Situation Awareness and Attention for the Takeover in Highly Automated Driving

“…The representation of such differences can be modeled using ACT-R. Some authors [18], for example, developed a cognitive model to simulate automated driving using reinforcement learning for adaptions on automated vehicle operations. This adaptation process is situated and very relevant for automated driving.…”