Measuring resistance to change at the within-session level.
Use raw response rates, not normalized ones, to measure resistance to change and still get clean results.
01Research in Context
What this study did
Tonneau et al. (2006) tested a new way to measure resistance to change. They looked at response rates minute-by-minute inside one session.
Instead of normalizing the data, they kept the raw counts. They wanted to see if the model still worked without extra math steps.
What they found
The raw-rate model fit the data just as well as the old way. Skipping normalization did not create fake patterns.
Within-session dips and rises matched the resistance-to-change rules. The method saved time and stayed accurate.
How this fits with other research
Zigman et al. (1997) review shows that basic choice principles guide everyday behavior. François fits inside that big picture by giving clinicians a faster ruler.
LeBlanc et al. (2003) also used within-session tracking to watch resurgence. Both papers prove that moment-to-moment data can reveal hidden patterns.
Rutherford et al. (2003) compared duration versus rate for spotting awareness. Like François, they found that simpler raw measures can beat fancy transforms.
Why it matters
You can now skip the normalization step when you check if a client’s behavior resists change. Fit the model to raw counts and you get the same answer in less time. Try it next time you run a multiple-schedule probe or track response strength under extinction.
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02At a glance
03Original abstract
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.
Journal of the experimental analysis of behavior, 2006 · doi:10.1901/jeab.2006.74-05