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Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection

Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in ‘runs’ of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractor...

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Autores principales: Clarke, Alasdair D. F., Hunt, Amelia R., Hughes, Anna E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812991/
https://www.ncbi.nlm.nih.gov/pubmed/35073315
http://dx.doi.org/10.1371/journal.pcbi.1009813
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author Clarke, Alasdair D. F.
Hunt, Amelia R.
Hughes, Anna E.
author_facet Clarke, Alasdair D. F.
Hunt, Amelia R.
Hughes, Anna E.
author_sort Clarke, Alasdair D. F.
collection PubMed
description Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in ‘runs’ of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, making it difficult to make inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area.
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spelling pubmed-88129912022-02-04 Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection Clarke, Alasdair D. F. Hunt, Amelia R. Hughes, Anna E. PLoS Comput Biol Research Article Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in ‘runs’ of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, making it difficult to make inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area. Public Library of Science 2022-01-24 /pmc/articles/PMC8812991/ /pubmed/35073315 http://dx.doi.org/10.1371/journal.pcbi.1009813 Text en © 2022 Clarke et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Clarke, Alasdair D. F.
Hunt, Amelia R.
Hughes, Anna E.
Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection
title Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection
title_full Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection
title_fullStr Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection
title_full_unstemmed Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection
title_short Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection
title_sort foraging as sampling without replacement: a bayesian statistical model for estimating biases in target selection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812991/
https://www.ncbi.nlm.nih.gov/pubmed/35073315
http://dx.doi.org/10.1371/journal.pcbi.1009813
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