#9282. A Bayesian model of capacity across trials

August 2026publication date
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Journal’s subject area:
Applied Mathematics;
Psychology (all);
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Abstract:
In nearly all situations, a persons performance varies across time (i.e., trials), for example due to vigilance decrements or training. When the performance depends on multiple sources of information, the variation could be either due to changes in processing each source or the efficiency of combining the sources. The capacity coefficient is a frequently used statistic for measuring the efficiency of combining multiple sources of information, but in most applications the analyses assume stationary performance across trials. A trial-varying capacity measure would be valuable in determining the nature of the change over trials but dropping the stationarity assumption results in a severe loss in power. In this work, we develop a Bayesian trial-varying model of the capacity coefficient to estimate the efficiency of an individual processing multiple sources of information. This model is based on the Weibull distribution to approximately characterize the processing time of each source, with an inverse gamma distribution prior for the scale parameter and a known shape. This approach provided us a tractable way to update prior estimates for real-time estimation of capacity across trials. We demonstrate the approach with both simulated and human data.
Keywords:
Bayesian data analysis; Time series; Workload capacity analysis

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