Computational behavior dynamics: an alternative description of Nevin (1969).
A simple computer program that jitters from key to key can recreate matching, showing that tiny random pushes—not a fixed equation—may run the choices you see in clinic.
01Research in Context
What this study did
Szatmari (1992) built a computer model that acts like a live pigeon. The model runs thousands of tiny random steps. It picks one key or the other, just like Nevin’s birds did in 1969.
The program spat out the same matching curves and run lengths that real data show. No algebra, just simple rules repeated over time.
What they found
The fake bird matched the real birds. The same shallow slope and little runs of staying on one side showed up.
The model also made new guesses no one had tested yet. It said choice is driven by moment-to-moment noise, not by a tidy equation.
How this fits with other research
White (1979) had already shown that real data almost always fall short of perfect matching. The 1992 model copies that undermatching without extra tweaks.
Van der Molen et al. (2010) took the idea further. They added Darwinian selection inside the computer. Virtual organisms evolve and still end up at the same matching steady state. Their evolutionary model is the grandchild of the 1992 stochastic one.
Morris et al. (2023) now use the evolutionary version to fit hospital data on self-injury. The chain is clear: 1992 stochastic → 2010 evolutionary → 2023 clinical tool.
Why it matters
If moment-to-moment noise drives choice, you can shift that noise in session. Try shortening the lean schedule’s timeout or adding a brief change-over delay. These micro-changes tweak the same dynamic the model uses, giving you a fresh way to bend choice without rewriting the whole contingency.
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02At a glance
03Original abstract
A computational processing behavior-dynamic model was instantiated in the form of a computer program that "behaved" on the task developed by Nevin (1969). In this classic discrete-trials experiment, the relative frequency of choosing a response alternative matched the relative frequency of reinforcement for that alternative, the local structure of responding was opposite that predicted by momentary maximizing (i.e., the probability of a changeover decreased with run length), and absolute and relative response rates varied independently. The behavior-dynamic model developed here qualitatively reproduced these three results (but not in quantitative and specific detail) and also generated some interesting, as-yet-untested predictions about performance in Nevin's task. The model was discussed as an example of a stochastic behavior-dynamic alternative to algebraic behavior theory.
Journal of the experimental analysis of behavior, 1992 · doi:10.1901/jeab.1992.57-289