Some remarks on the quantitative analysis of behavior.

This paper is a call to arms, not an experiment. The author urges behavior analysts to treat response rate like physicists treat motion. Measure it precisely. Model it with equations. Link tiny moment-to-moment responses to big daily patterns.

No clients, no sessions—just a map for future science.

The big idea: reinforcement works like a physical force. Push it and the rate of response changes. Track that rate as your main number. Build Newton-style formulas that connect single clicks, bites, or words to long stretches of behavior.

Staddon et al. (2002) took the advice and built real math. They used Poisson and Markov models to turn response probability into predicted rate. Jones et al. (2010) went further, writing two-part differential equations that show how reinforcement ‘force’ moves choice on concurrent schedules.

Skinner et al. (1958) had already shown how to define an operant unit so rate could even be counted; Marr (1989) simply asked us to treat that number as the center of our universe.

Sosa et al. (2022) push back. They say Newton is the wrong metaphor. Feedback-control loops, not force-and-motion, better explain why behavior stays stable. The clash is friendly: both camps want equations, but one wants pushes, the other wants thermostats.

If you graph rate in real time you already follow this paper. Next step: try the simple equations from Staddon et al. (2002) or Jones et al. (2010) in Excel. See if your client’s data fit. The exercise sharpens treatment decisions and keeps us close to hard science.