Behavioral variability in an evolutionary theory of behavior dynamics
Reinforcer rate and size act like a volume knob on behavioral variability, not just response speed.
01Research in Context
What this study did
Popa et al. (2016) built a computer model of how behavior changes over time.
The model added random small changes, called mutations, to virtual responses.
It then asked: do reinforcer size and reinforcer rate change how many mutations survive?
What they found
High reinforcer rates trimmed the mutations and made behavior more steady.
Low reinforcer rates let the mutations grow and made behavior more varied.
The two factors worked together, not just added on top of each other.
How this fits with other research
Older animal work already showed that bigger or tastier reinforcers push response rates up (M et al. 1978, M et al. 1981).
Popa’s model flips the lens: the same reinforcer parameters now control how much new behavior appears, not just how fast old behavior runs.
Todorov et al. (1984) found frequency matters more than magnitude for speed; Popa adds that frequency also gates variability, tying the two ideas into one story.
Why it matters
When you thin a schedule, expect more novel responses to pop up. If you want steady, low-variability performance, keep the reinforcer rate rich or the reinforcer size large. You can now plan schedule thinning with variability in mind, not just rate.
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02At a glance
03Original abstract
McDowell's evolutionary theory of behavior dynamics (McDowell, 2004) instantiates populations of behaviors (abstractly represented by integers) that evolve under the selection pressure of the environment in the form of positive reinforcement. Each generation gives rise to the next via low-level Darwinian processes of selection, recombination, and mutation. The emergent patterns can be analyzed and compared to those produced by biological organisms. The purpose of this project was to explore the effects of high mutation rates on behavioral variability in environments that arranged different reinforcer rates and magnitudes. Behavioral variability increased with the rate of mutation. High reinforcer rates and magnitudes reduced these effects; low reinforcer rates and magnitudes augmented them. These results are in agreement with live-organism research on behavioral variability. Various combinations of mutation rates, reinforcer rates, and reinforcer magnitudes produced similar high-level outcomes (equifinality). These findings suggest that the independent variables that describe an experimental condition interact; that is, they do not influence behavior independently. These conclusions have implications for the interpretation of high levels of variability, mathematical undermatching, and the matching theory. The last part of the discussion centers on a potential biological counterpart for the rate of mutation, namely spontaneous fluctuations in the brain's default mode network.
Journal of the Experimental Analysis of Behavior, 2016 · doi:10.1002/jeab.199