Modeling Decision Making on the Use of Automation

Junya Morita, Kazuhisa Miwa, Akihiro Maehigashi, Hitoshi Terai, Kazuaki Kojima, Frank E. Ritter

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper presents a cognitive model that simulates reliance on automation using a line-tracing task similar to driving where an operator has to track a moving line with a circle by pressing keys on a keyboard (manual control) or rely on automation (auto control). An operator can switch between auto and manual control during the task. The success probabilities of each control mode were systematically varied. An ACT-R model to perform this task was constructed by representing reliance on the automation as production. The model performs this task through productions that manage the perceptual/motor modules. The utility values of these productions are updated based on the rewards in every screen update. We also introduce a meta-level monitoring the internal state of the model. A preliminary run of this model simulated the overall trends of the behavioral data, suggesting some validity of the assumptions made in our model.

Original languageEnglish (US)
Title of host publicationExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
EditorsLaura Carlson, Christoph Hoelscher, Thomas F. Shipley
PublisherThe Cognitive Science Society
Pages1971-1976
Number of pages6
ISBN (Electronic)9780976831877
StatePublished - 2011
Event33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011 - Boston, United States
Duration: Jul 20 2011Jul 23 2011

Publication series

NameExpanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Conference

Conference33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011
Country/TerritoryUnited States
CityBoston
Period7/20/117/23/11

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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