Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection.
Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection.
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DOI：
10.1371/journal.pcbi.1009813
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发表时间：
2022-01
影响因子：
4.3
通讯作者：
Hughes AE
中科院分区：
文献类型：
--
作者：
Clarke ADF;Hunt AR;Hughes AE
觅食需要依次寻找多个目标。在人类和其他动物中，一个关键的观察结果是倾向于在同一目标类型的“跑步”中觅食。这种倾向是上下文敏感的，在人类中，当目标难以与干扰物区分时，这种倾向最强烈。关于人类觅食的这种倾向和其他倾向，许多重要的问题尚未得到解决，一个关键的限制是缺乏精确的觅食行为测量。标准的度量往往是运行统计数据，例如最大运行长度和运行次数。但这些措施不仅相互依赖，而且还受到目标数量和分布的限制，因此很难推断环境的这些方面对觅食的影响。此外，运行统计数据是不够详细的基本认知过程决定觅食行为。我们提出了一种替代方法：建模觅食作为一个程序的生成采样没有替换，实施贝叶斯多层次模型。这使我们能够将行为分解为许多影响目标选择的偏差，例如目标的接近度和在运行中选择目标的偏差，以一种不依赖于目标数量的方式。因此，我们的方法便于直接比较搜索环境中不同的理论上重要的尺寸之间的特定觅食倾向。我们演示了我们的模型与模拟的例子和现有数据的重新分析。我们相信我们的模型将为视觉觅食提供更深入的见解，并为该领域的进一步建模工作提供基础。觅食已经在许多依赖广泛分布的食物来源的物种中得到了很好的研究，例如蜜蜂和鸟类。人类如何处理觅食任务，以及我们是否可以识别出描述我们如何搜索数量和分布不同的不同类别的对象的一般策略，这一点还不太清楚。我们提出了一种方法来模拟觅食行为作为一个生成的采样过程中没有替换，实现贝叶斯多层次模型。这使我们能够将行为分解为许多影响目标选择的独立偏差，包括目标的接近度，在运行中选择目标的偏差和特定目标类型的偏差，以不依赖于目标数量的方式存在。我们相信这个工具可以打开觅食的大门，使其成为一项标准任务，以完善我们对注意力、工作记忆、前瞻记忆、学习、计划和决策的理解。
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. Foraging has been well-studied in many species that rely on widely distributed food sources, such as bees and birds. Less well understood is how humans approach foraging tasks, and whether there are general policies we can identify that describe how we search for different categories of objects that can vary in quantity and distribution. We present a way to model foraging behaviour as a generative sampling without replacement procedure, implemented in a Bayesian multilevel model. This allows us to break down behaviour into a number of independent biases that influence target selection, including the proximity of targets, a bias for selecting targets in runs and a bias for a particular target type, in a way that is not dependent on the number of targets present. We believe this tool can open the door for foraging to become a standard task for refining our understanding of attention, working memory, prospective memory, learning, planning and decision-making.
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影响因子：
0.8
作者：
Johannesson, Omar I.;Kristjansson, Arni;Thornton, Ian M.
通讯作者：
Thornton, Ian M.
影响因子：
1.9
作者：
Johannesson, Omar I.;Thornton, Ian M.;Kristjansson, Arni
通讯作者：
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影响因子：
6.1
作者：
HEINRICH, B
通讯作者：
HEINRICH, B
影响因子：
1.8
作者：
Kristjansson, Tomas;Kristjansson, Arni
通讯作者：
Kristjansson, Arni
影响因子：
2.6
作者：
Olafsdottir, Inga Maria;Gestsdottir, Steinunn;Kristjansson, Arni
通讯作者：
Kristjansson, Arni