The assumption that responses are controlled by distinct serially arranged processes was used by Sternberg (1969a) to explain the result, observed in many human experiments, that two factors have additive effects on reaction time (RT). With slight changes, Sternberg's explanation of additive factors with RT also explains the result, observed in animal experiments, that two factors have multiplicative effects on response rate. This article describes and interprets 17 examples of multiplicative factors from response-rate experiments with rats, pigeons, and goldfish, as well as some other animal evidence for distinct serial processes. The examples suggest and/or support new and old ideas about generalization, attention, timing, learning, motivation, and response production. Most important, the animal evidence makes the case for distinct serial processes considerably stronger. Since the procedures used in the two sets of experiments (human and animal) have little in common, distinct serial processes may control behavior in a very wide range of situations.Sternberg (l969a) introduced the additive-factor method (AFM), a guide to designing and interpreting reaction time (RT) experiments, and used the method to show that there was evidence for distinct serially arranged processes (which Sternberg called processing stages) in a number of human RT experiments. The data that he used to illustrate the method are unusual. They come from factorial RT experiments, and the key result, found in a number of experiments, was that pairs of factors (treatments) had close-to-additive effects on mean RT. One unusual feature of the data is the precision of the additivity. In most cases, an additive model fit so well that in graphs of the data, all of the points (representing the data) touched the fitted lines (representing the model). In a few cases, it was not obvious that the lines had not been drawn through the points. Ten of Sternberg's examples provided enough data to calculate the percentage of variance explained by an additive model; on the average (median over the 10 cases), an additive model describes 99.89% of the variance. In one set of experiments, not done with the method in mind, an additive model described RTs with median absolute error of 1.8 msec (median taken over five pairs of factors). In an experiment designed for the method, the errors were .9 msec (one pair offactors) and.4 msec (same pair at a different level of a third factor), with RTs on the order of 500 msec. Sternberg (1971) described 9