Typical delay determines waiting time on periodic-food schedules: Static and dynamic tests.
Abstract
Pigeons and other animals soon learn to wait (pause) after food delivery on periodic-food
schedules before resuming the food-rewarded response. Under most conditions the steady-state
duration of the average waiting time, t, is a linear function of the typical interfood
interval. We describe three experiments designed to explore the limits of this process.
In all experiments, t was associated with one key color and the subsequent food delay,
T, with another. In the first experiment, we compared the relation between t (waiting
time) and T (food delay) under two conditions: when T was held constant, and when
T was an inverse function of t. The pigeons could maximize the rate of food delivery
under the first condition by setting t to a consistently short value; optimal behavior
under the second condition required a linear relation with unit slope between t and
T. Despite this difference in optimal policy, the pigeons in both cases showed the
same linear relation, with slope less than one, between t and T. This result was confirmed
in a second parametric experiment that added a third condition, in which T + t was
held constant. Linear waiting appears to be an obligatory rule for pigeons. In a third
experiment we arranged for a multiplicative relation between t and T (positive feedback),
and produced either very short or very long waiting times as predicted by a quasi-dynamic
model in which waiting time is strongly determined by the just-preceding food delay.
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John E. R. Staddon
James B. Duke Distinguished Professor Emeritus of Psychology and Neuroscience
Until my retirement in 2007, my laboratory did experimental research on learning and
adaptive behavior, mostly with animals: pigeons, rats, fish, parakeets. We were particularly
interested in timing and memory, feeding regulation, habituation and the ways in which
pigeons and rats adapt to reward schedules. The aim is to arrive at simple models
for learning that can help to identify the underlying neural mechanisms. I continue
to do theoretical and historical work on the power law in

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