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

Lookup NU author(s): Dr John Skelhorn



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Oxford University Press, 2023.

For re-use rights please refer to the publisher's terms and conditions.


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 vary 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 which 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 “stronger” ones.

Publication metadata

Author(s): Sherratt TN, Dewan I, Skelhorn J

Publication type: Article

Publication status: Published

Journal: Behavioral Ecology

Year: 2023

Volume: 34

Issue: 5

Pages: 840–849

Print publication date: 01/10/2023

Online publication date: 24/06/2023

Acceptance date: 14/04/2023

Date deposited: 02/06/2023

ISSN (print): 1045-2249

ISSN (electronic): 1465-7279

Publisher: Oxford University Press


DOI: 10.1093/beheco/arad032

ePrints DOI: 10.57711/nnqz-6d10


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