A Theory of Associating Food Types with Their Postingestive Consequences

• Published in: The American naturalist 2006-05, Vol.167 (5), p.705-716
• Main Authors: Yearsley, Jonathan M., Villalba, Juan J., Gordon, Iain J., Kyriazakis, Ilias, Speakman, John R., Tolkamp, Bert J., Illius, Andrew W., Duncan, Alan J.
• Format: Article
• Language: English
• Subjects: Acquisitions & mergers; Animal behavior; Animal feeding behavior; Animal Nutritional Physiological Phenomena; Animals; Associations; Autocorrelation; Choice Behavior - physiology; Computer Simulation; Defense mechanisms; Diet; Feeding Behavior - physiology; Food; Food consumption; Foraging; Herbivores; Industrial research; Information Theory; Learning - physiology; Models, Biological; Plants; Preferences; Processes; Theory; Time Factors; Time series
• ISSN: 0003-0147; 1537-5323
• DOI: 10.1086/502805

Animals often face complex and changing food environments. While such environments are challenging, an animal should make an association between a food type and its properties (such as the presence of a nutrient or toxin). We use information theory concepts, such as mutual information, to establish a theory for the development of these associations. In this theory, associations are assumed to maximize the mutual information between foods and their consequences. We show that associations are invariably imperfect. An association’s accuracy increases with the length of a feeding session and the relative frequency of a food type but decreases as time delay between consumption and postingestive consequence increases. Surprisingly, the accuracy of an association is independent of the number of additional food types in the environment. The rate of information transfer between novel foods and a forager depends on the forager’s diet. In light of this theory, an animal’s diet may have two competing goals: first, the provision of an appropriate balance of nutrients, and second, the ability to quickly and accurately learn the properties of novel foods. We discuss the ecological and behavioral implications of making associational errors and contrast the timescale and mechanisms of our theory with those of existing theory.