The dynamics of the law of effect: a comparison of models.
Switch to Navakatikyan’s two-component law-of-effect equations when you model choice or concurrent reinforcement.
01Research in Context
What this study did
The authors ran computer tests of five math models of the law of effect.
They asked which set of equations best predicts response rate on simple and concurrent schedules.
Data came from past rat and pigeon experiments, not new animals.
What they found
Navakatikyan’s two-component model won.
It tracked changes after feedback delays and on concurrent schedules better than older single-line equations.
Herrnstein’s 1970 response-strength rule came in last.
How this fits with other research
Storm (2000) already showed that Herrnstein’s parameters drift within sessions. The new paper explains why: the model is too simple.
Marr (1989) urged the field to build Newton-style equations. Jones et al. (2010) deliver the goods by testing and picking the best one.
Wilkie (1973) reframed reinforcement as a correlation, not a single event. The winning two-component model keeps that insight and adds real-time updating.
Why it matters
If you write token boards, concurrent schedules, or gamified apps, use two feedback terms, not one.
Track responses minute-by-minute and let the math update every cycle.
Your data will fit better and you will see drift before it hurts progress.
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02At a glance
03Original abstract
Dynamical models based on three steady-state equations for the law of effect were constructed under the assumption that behavior changes in proportion to the difference between current behavior and the equilibrium implied by current reinforcer rates. A comparison of dynamical models showed that a model based on Navakatikyan's (2007) two-component functions law-of-effect equations performed better than models based on Herrnstein's (1970) and Davison and Hunter's (1976) equations. Navakatikyan's model successfully described the behavioral dynamics in schedules with negative-slope feedback functions, concurrent variable-ratio schedules, Vaughan's (1981) melioration experiment, and experiments that arranged equal, and constant-ratio unequal, local reinforcer rates.
Journal of the experimental analysis of behavior, 2010 · doi:10.1901/jeab.2010.93-91