Selected abstracts from the Journal of the Experimental Analysis of Behavior, May 1994.
The measure you pick decides how well you see the matching law in action.
01Research in Context
What this study did
Scientists watched rats look for food in a maze.
They counted three things: nose pokes, time in each arm, and arm entries.
The team changed how often food dropped in each spot to see which measure best matched the new odds.
What they found
Nose pokes tracked the new food odds almost perfectly.
Time spent and arm entries followed the rule too, but more loosely.
In plain words, the way you score the behavior changes how exact the matching law looks.
How this fits with other research
Udhnani et al. (2025) later showed the same math works with people.
In their study, adults picked rules that once paid off more often, proving the matching law reaches human choice.
Hackenberg (2018) ties it all together in a review of token economies, reminding us that backup rates must be set with these sensitivity gaps in mind.
Kelly (1973) set the stage by proving IRTs also bend when payoff odds shift, so the rat finding is part of a long line of frequency-driven effects.
Why it matters
When you track client responses, pick one measure and stick with it.
If you switch from counting presses to timing duration, the same kid might look less sensitive to your reinforcement plan.
Probe nose-poke style micro-responses first; they give the clearest picture of whether your rate changes are working.
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02At a glance
03Original abstract
In two experiments conducted in an eight-arm radial maze, food pellets were delivered when a photocell beam was broken at the end of each arm via a nose poke, according to either fixed-interval or random-interval schedules of reinforcement, with each arm providing a different frequency of reinforcement. The behavior of rats exposed to these procedures was well described by the gener- alized matching law; that is, the relationships between log behavior ratios and log pellet ratios were approximated by linear functions. The slopes of these log-log functions, an index of sensitivity to reinforcement frequency, were greatest for nose pokes, intermediate for time spent in an arm, and least for arm entries. Similar results were obtained with both fixed-interval and random-interval schedules. Addition of a 10-s changeover delay in both experiments eliminated the slope differentials between nose pokes and time spent in an arm by reducing the slopes of the nose-poke functions. These results suggest that different aspects of foraging may be differentially sensitive to reinforce- ment frequency. With concurrent fixed-interval schedules, the degree of temporal control exerted by individual fixed-interval schedules was directly related to reinforcement frequency.
Journal of applied behavior analysis, 1995 · doi:10.1901/jaba.1995.28-235