Testing models of the redundant-signals effect: a warning concerning the combination-rule regression analysis

The redundant-signals effect is the observed RT advantage for trials presenting two or more targets, as compared with trials with only one target. Two general classes of parallel-processing model have been proposed to explain this effect: race models (e.g., Raab, 1962) and coactivation models (e.g., Miller, 1982). Various distributional analyses have been used in work aimed at discriminating between these two model classes. The present study reexamined one of these tests--the combination-rule regression analysis based on variable-criterion theory (Grice, Canham, & Boroughs, 1984)--by applying it to the data from two sets of simulated experiments. One set of simulations assumed coactivation; the other set assumed an independent race on redundant-target trials. Nearly identical combination-rule values were observed in the two sets of simulations. This finding shows that the combination rule of variable-criterion theory does not discriminate between models capable of explaining the redundant-signals effect. The implications of this finding are briefly discussed.