Bayes’ Theorem in Animal Behavior
This journal article discusses how animals are aware of the prior distribution, which is basically any prior knowledge they hold for any environment. It could be determined by its past experience, or it can be set by a combination of the environment in which it evolved in and its own past experience. One example that the article gives that relates to Bayes’s Theorem is foraging in patches. As the animals search in a new patch, it updates its estimate of items in patch based on its experience. It’s decision to leave a patch will be influenced by the mean rate that it can get items in other patches, and by the estimation of future prospects for food on the current patch, which is determined by animal experience. Another example is mate choice during annual breeding season. At the start of breeding season, the females doesn’t know what range of qualities of males are high for the specific year since distribution of male qualities varies from year to year, and they will inspect males one by one. The females have prior information on previous years’ data and is able to update her estimate of the distribution for this year as they go.
The article is arguing that animals may be good Bayesian because they will make change to acceptance threshold as they know more information. If the males observed are poor quality, the females will be less choosy and will conclude that this year is a poor year. It is very much like the examples shown in lectures where people will be less certain about their own signals as time goes: if the previously observed signals so far are bad, then the person will learn that this overall state is bad. When there is more information combined, people will be able to adjust their own opinion to form optimal decisions.
link: https://www-jstor-org.proxy.library.cornell.edu/stable/3548663?seq=8#metadata_info_tab_contents