University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Avian Cognition Papers Center for Avian Cognition 1983 Optimal Foraging Theory and the Psychology of Learning Alan Kamil Follow this and additional works at: https://digitalcommons.unl.edu/biosciaviancog Part of the Animal Studies Commons, Behavior and Ethology Commons, Cognition and Perception Commons, Forest Sciences Commons, Ornithology Commons, and the Other Psychology Commons This Article is brought to you for free and open access by the Center for Avian Cognition at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Avian Cognition Papers by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. digitalcommons.unl.edu Optimal Foraging Theory and the Psychology of Learning Alan C. Kamil Departments of Psychology and Zoology University of Massachusetts Amherst, Massachusetts 01003 Synopsis The development of optimization theory has made important contri- butions to the study of animal behavior. But the optimization approach needs to be integrated with other methods of ethology and psychology. For example, the ability to learn is an important component of efficient foraging behavior in many species, and the psychology of animal learn- ing could contribute substantially to testing and extending the predic- tions of optimal foraging theory. Introduction When the animal behavior literature of the 1960s is compared with the literature of today, it is apparent that a revolution has taken place. Many of the phenomena of behavioral ecology and sociobiology that are taken for granted today, that are presented in undergraduate text- books (e.g., Krebs and Davies, 1981), would have seemed implausible just 15 to 20 years ago. This radical change in how we regard animal Published in American Zoologist, 23 (1983), pp 291-302. doi 10.1093/icb/23.2.291 Copyright © 1983 by the American Society of Zoologists From the Symposium on Optimization of Behavior presented at the Annual Meeting of the American Society of Zoologists, 27-30 December 1981, at Dallas, Texas. 1 A.C. Kamil in American Zoologist 23 (1983) 2 behavior is due in part to the development of optimization theory. Op- timization theory can be regarded as a logical extension of the adap- tive approach to the study of animal behavior long espoused by ethol- ogists (e.g., Tinbergen, 1963), but the development of optimization models has had enormous impact. By deriving solutions to specific behavioral problems, optimization models have generated testable predictions which have stimulated research, suggesting novel ways in which the fine details of the behavior of an animal could affect bi- ological success. By focusing attention upon the behavior of individual animals, op- timization theory also contributed to an important methodological de- velopment, increased data collection with identifiable individual an- imals over time, under natural conditions. Although this type of data collection was becoming more common before the advent of optimi- zation theory (e.g., Tinbergen, 1960: van Lawick-Goodall, 1968), it re- ceived additional impetus from optimization theory, which focusses on the behavior of individuals. The importance of this methodological development should not be underestimated. Many of the most inter- esting phenomena of behavioral ecology and sociobiology could only become apparent after such data were collected. Examples include ad- justments in territoriality and territory size in response to changes in nectar availability by sunbirds (Gill and Wolf, 1975), social competition within groups of communally nesting birds (Vehrencamp, 1977), the apparent avoidance of toxin-laden leaves by howler monkeys (Glan- der, 1981) and adjustments in diet in response to changes in prey den- sity by redshanks (Goss-Custard, 1981). In spite of these contributions, optimization theory has been sub- jected to substantial criticism (e.g., Gould and Lewontin, 1979; see also Maynard-Smith, 1978). Much of this criticism has centered around three issues. (1) Tests of optimization theory are not legitimate tests of the hypothesis that behavior is adapted. (2) Many optimization mod- els, particularly optimal foraging models, are too simple, in light of the complexity of the challenges an animal faces in nature (Zach and Smith, 1981). (3) The fit between the predictions of the models and the data is often only qualitative, and the apparent quantitative pre- cision of the models is misleading. A problem related to these criticisms is that although optimiza- tion models are based on the assumption that animals are perfectly adapted, there are many reasons to doubt that optimally functional A.C. Kamil in American Zoologist 23 (1983) 3 behavior will be found very often. For one thing, optimality would de- pend upon the rate of evolutionary change being rapid enough to track environmental change, and some lag must occur frequently. There may be costs or constraints which make optimal behavior unlikely or im- possible (Orians, 1981). Many situations may be sufficiently multidi- mensional to rule out any single optimal solution. How can we resolve the apparent paradox of the practical utility of optimization theory with the substantial criticisms it has received and with the probable inaccuracy of its basic premise? The answer to this question is simple, and involves placing empha- sis on the term practical utility. Our goal as biological scientists is to understand the animals we study. Ethologists and behavioral ecolo- gists need to remember that the subject under investigation is behav- ior, not adaptation, optimization or even evolution. Concepts such as optimization are theoretical constructs that we use to help us under- stand and predict behavior, but are not themselves the objects of study. In this context, the question is whether a particular concept has heu- ristic value, and for optimization theory the answer is clearly yes. As indicated above, elsewhere in this symposium, and throughout the an- imal behavior literature of today, many experiments in both laboratory and field have demonstrated the practical value of optimization theory. From this point of view, many of the criticisms of optimization the- ory seem unimportant. The argument that optimization theory does not provide a legitimate test of the adaptedness of behavior is cor- rect, but largely irrelevant. There are other, more appropriate meth- ods available to test the adaptedness hypothesis (e.g.. Curio, 1973). The qualitative fit between theory and data, and the relative simplic- ity of optimization models present challenges for future theoretical and empirical work. And the reasons why nonoptimality often may be expected are reminders that optimization is a useful concept, but not necessarily an inherent characteristic of behavior. We are now at the beginning of the second stage of the develop- ment of optimization theory. The first stage has been relatively short and quite exciting. During this initial stage theoretical advances often have proceeded more rapidly than empirical developments, and atten- tion frequently has been focused narrowly on optimization theory as an end in and of itself. In a way, it reminds me of what happened in my laboratory when we got our first computer for the on-line control of experiments. At first we tried to do everything imaginable with it. A.C. Kamil in American Zoologist 23 (1983) 4 There were even experiments performed which, in retrospect, seem to have been intended to explore the limits of the computer rather than the behavior of the blue jays we were studying. But that first stage passed. We came to realize that some things could still best be done with the older tools—relays or stop watches. The computer then as- sumed its rightful place as one of the tools we use for accomplishing our goal. Similarly, we must recognize that optimization theory can- not do it all. The complete understanding of behavior will require the use of optimization theory, but only as one of the tools we use. Dur- ing the second stage of the development of optimization theory, the information gained because of optimization theory must be consoli- dated and integrated with other aspects of animal behavior. One of the functions of scientific theories which optimization the- ory has served successfully is to direct our observations to particu- larly enlightening cases. Situations such as the depleting food patch or the group of animals of varying kinship already have repaid our ef- forts handsomely. However, the situations highlighted by optimization theory need to be examined more closely and comprehensively. There has been an occasional tendency to conduct research with a view to- wards testing optimization theory predictions rather narrowly, and this has led to limited data collection. This may lie at the heart of the criticisms Heinrich (1983) has levelled at some optimization research. In other instances, systematic departures from randomness have been labelled optimal very quickly, sometimes even in the absence of an\ particular model, ignoring the definition of the word optimal and pos- sibly curtailing further investigation. These problems ma) point out the potential costs of any device which directs our attention in a par- ticular direction. When looking very hard for one phenomenon, oth- ers may be overlooked easily. Although it may be difficult, we must guard against tunnel vision. When the behavior of an animal does fit the predictions of optimiza- tion theory, the case is not closed. Many questions remain. We still need to determine how this behavior comes about, in terms of its physiology and ontogeny, for instance. And when behavior does not match our predictions very well, similar problems still remain; re- working our models is not the sole task confronting us when the mod- els are in error. Optimization theory overlaps substantially with ethology and com- parative psychology in several areas. One of the most important of A.C. Kamil in American Zoologist 23 (1983) 5 these is the effects of individual experience on behavior. Many optimi- zation models imply that previous experience plays an essential role in the behaviors that these models predict.