Steady-state distributions for human decisions in two-alternative choice tasks

Andrew Stewart, Ming Cao, Naomi Ehrich Leonard

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

5 Scopus citations

Abstract

In human-in-the-loop systems, humans are often faced with making repeated choices among finite alternatives in response to observations of the evolving system performance. In order to design humans into such systems, it is important to develop a systematic description of human decision making in this context. We examine a commonly used, drift-diffusion, decision-making model that has been fit to human neural and behavioral data in sequential, two-alternative, forced-choice tasks. We show how this model and type of task together can be regarded as a Markov process, and we derive the steady-state probability distribution for choice sequences. Using the analytic expression for this distribution, we prove matching behavior for tasks that exhibit a matching point and we compute the sensitivity of steady-state choices to a model parameter that measures the decision maker's "exploratory" tendency.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 American Control Conference, ACC 2010
PublisherIEEE Computer Society
Pages2378-2383
Number of pages6
ISBN (Print)9781424474264
DOIs
StatePublished - 2010

Publication series

NameProceedings of the 2010 American Control Conference, ACC 2010

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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    Stewart, A., Cao, M., & Leonard, N. E. (2010). Steady-state distributions for human decisions in two-alternative choice tasks. In Proceedings of the 2010 American Control Conference, ACC 2010 (pp. 2378-2383). [5530563] (Proceedings of the 2010 American Control Conference, ACC 2010). IEEE Computer Society. https://doi.org/10.1109/acc.2010.5530563