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Full-Text Articles in Statistical Models
An Extension Of Sic Predictions To The Wiener Coactive Model, Joseph W. Houpt, James T. Townsend
An Extension Of Sic Predictions To The Wiener Coactive Model, Joseph W. Houpt, James T. Townsend
Psychology Faculty Publications
The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down--up--down form, with an early negative region that is smaller than the …
An Extension Of Sic Predictions To The Wiener Coactive Model, Joseph W. Houpt, James T. Townsend
An Extension Of Sic Predictions To The Wiener Coactive Model, Joseph W. Houpt, James T. Townsend
Joseph W. Houpt
The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down--up--down form, with an early negative region that is smaller than the …