Human Performance Modeling Using Generative Ai Principles

Evaluation of integrated human-machine systems depends on having accurate human performance models. However, such models often provide only instantaneous snapshots of cognitive state and fail to account for ongoing dynamics. We argue that generative AI solutions can be used to alleviate this problem. Generative AI tools have been successful when applied to problems that have repeatable structure captured by a low-dimensional lexicon and associated with large amounts of training data. These properties apply to human performance modeling as well. Here, we introduce our Generative Cognitive Modeling Tool, a prototype human performance model developed using strategies from the generative AI community. We demonstrate the utility of our approach using simulated driving data. Our results show that cognitive states associated with driving errors are not randomized events but rather the outcome of continuous dynamics and are predictable up to 25 secs prior to the error event. We also find that the voluntary utilization of autonomous driving aids can be predicted, in part, by the disruption of ongoing dynamics. Overall, this underscores the importance of ongoing dynamics for human performance modeling and establish that generative AI approaches can provide one way to account for such factors.