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.
Highlights
Automation technology, which can partially substitute for human cognitive functions, has made remarkably progress recently
We present a simple task that has some characteristics of continuous vehicle operation with automation and construct a model to reveal what type of mechanisms can simulate human adaptation to automation in a time-critical task like automatic vehicle operation
Among several cognitive architectures that have been proposed so far, the present study focuses on a cognitive process model (ACT-R) (Adaptive Control of Thought-Rational; Anderson, 2007; Ritter et al, 2019 for a recent review) because this architecture has a large community and the mechanisms are well-tested
Summary
Automation technology, which can partially substitute for human cognitive functions, has made remarkably progress recently. The application area of such technology is diverse, one of the recent prominent areas is the automatic operation of vehicles. Automation of some functions such as speed control (i.e., adaptive cruise control) and braking (anti-lock) have been used for a long time. Automatic control of steering has been actively developed due to the rapid progress of sensing and machine learning technologies. There are still barriers to the full application of automatic driving (self-driving cars). It has been assumed that automatic control will be used with driver’s monitoring to intervene immediately at any time if the automatic control fails to respond properly (National Highway Traffic Safety Administration, 2016)
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