I present a simple model that considers how three factors-change, regularity, and value- influence the evolution of animal learning. Change and regularity are considered by introducing two terms that measure environmental persistence. One term, "between-generation persistence, "defines the extent to which states in the parental generation predict states in the offspring generation; the other term, "within-generation persistence," defines the extent to which today predicts tomorrow within an individual's lifetime. Within-generation persistence is shown to be the most important of these two terms. When there is some change, increasing the within-generation persistence promotes the evolution of learning, and the between-generation persistence term has no effect. However, when the environment is almost completely fixed, then increasing change, either within or between generations, promotes the evolution of learning. This occurs because (1) the change required to promote the evolution of learning can occur either within or between generations even though (2) the regularity required to promote the evolution of learning must come within an animal's lifetime. The region of absolute fixity, in which learning does not generally evolve, is relatively small. The results for value, or payoffs, suggest that learning is most useful when all the alternatives to learning yield about the same payoff. [Behav Ecol 1991; 2: 77-89].
Bibliographical noteFunding Information:
I thank Tom Caraco, Steve Dunbar, Tom Getty, Tony Joern, Timothy Johnston, Al Kami], Don Kramer, Marc Mangel, Sara Shettleworth,John Staddon, and Tony Zcra for their comments. The completion of this work was partially supported by the University of Nebraska, the University of Nebraska Foundation, and a Presidential Young Investigator Award (BNS-8958228) from the National Science Foundation. I am grateful to these organizations for their support.
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