Deriving exact predictions from the cascade model.

McClelland's (1979) cascade model is investigated, and it is shown that the model does not have a well-defined reaction time (RT) distribution function because it always predicts a nonzero probability that a response never occurs. By conditioning on the event that a response does occur, RT density and distribution functions are derived, thus allowing most RT statistics to be computed directly and eliminating the need for computer simulations. Using these results, an investigation of the model revealed that (a) it predicts mean RT additivity in most cases of pure insertion or selective influence; (b) it predicts only a very small increase in standard deviations as mean RT increases; and (c) it does not mimic the distribution of discrete-stage models that have a serial stage with an exponentially distributed duration. Recently, McClelland (1979) proposed a continuous-time linear systems model of simple cognitive processes based on sequential banks of parallel integrators. This model, referred to by