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This article provides important mathematical descriptions and computer algorithms in relation to the responding optimally with unknown sources of evidence (ROUSE) model of Huber, Shiffrin, Lyle, and Ruys (2001), which has been applied to short-term priming phenomena. In the first section, techniques for obtaining parameter confidence intervals and parameter correlations are described, which are generally applicable to any mathematical model. In the second section, a technique for producing analytic ROUSE predictions is described. Huber et al. (2001) averaged many stochastic trials to obtain stable behavior. By appropriately weighting all possible combinations of feature states, an alternative analytic version is developed, yielding asymptotic model behavior with fewer computations. The third section ties together these separate techniques, obtaining parameter confidence and correlations for the analytic version of the ROUSE model. In doing so, previously unreported behaviors of the model are revealed. In particular, complications due to local minima are discussed, in terms of both variance-covariance analyses and bootstrap sampling analyses.

behavior research methods

Computer simulations of the ROUSE model: An analytic simulation technique and a comparison between the error variance—covariance and bootstrap methods for estimating parameter confidence