Measuring resistance to change at the within-session level

Resistance to change is often studied by measuring response rate in various components of a multiple schedule. Response rate in each component is normalized (that is, divided by its baseline level) and then log-transformed. Differential resistance to change is demonstrated if the normalized, log-transformed response rate in one component decreases more slowly than in another component. A problem with normalization, however, is that it can produce artifactual results if the relation between baseline level and disruption is not multiplicative. One way to address this issue is to fit specific models of disruption to untransformed response rates and evaluate whether or not a multiplicative model accounts for the data. Here we present such a test of resistance to change, using within-session response patterns in rats as a data base for fitting models of disruption. By analyzing response rate at a within-session level, we were able to confirm a central prediction of the resistance-to-change framework while discarding normalization artifacts as a plausible explanation of our results.