The optimal time to approach an unfamiliar object: a Bayesian model

Tom N Sherratt, Ian Dewan and John Skelhorn

Behavioral Ecology, 2023, vol. 34, issue 5, 840-849

Abstract: Many organisms take time before approaching unfamiliar objects. This caution forms the basis of some well-known assays in the fields of behavioral ecology, comparative psychology, and animal welfare, including quantifying the personality traits of individuals and evaluating the extent of their neophobia. In this paper, we present a mathematical model which identifies the optimal time an observer should wait before approaching an unfamiliar object. The model is Bayesian, and simply assumes that the longer the observer goes without being attacked by an unfamiliar object, the lower will be the observer’s estimated probability that the object is dangerous. Given the information gained, a time is reached at which the expected benefits from approaching the object begin to exceed the costs. The model not only explains why latency to approach may be repeatable among individuals and varies with the object’s appearance but also why individuals habituate to the stimulus, approaching it more rapidly over repeated trials. We demonstrate the applicability of our model by fitting it to published data on the time taken by chicks to attack artificial caterpillars that share no, one, or two signaling traits with snakes (eyespots and posture). We use this example to show that while the optimal time to attack an unfamiliar object reflects the observer’s expectation that the object is dangerous, the rate at which habituation arises is also a function of the observer’s certainty in their belief. In so doing, we explain why observers become more rapidly habituated to “weaker” stimuli than the “stronger” ones.

Keywords: Bayesian inference; boldness; habituation; latency; multi-component signals; neophobia (search for similar items in EconPapers)
Date: 2023
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