Department of Evolution, Ecology, and Behavior, School of Integrative Biology, University of Illinois, Urbana-Champaign, IL 61801, USA.
Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA.
Philos Trans R Soc Lond B Biol Sci. 2020 Jul 6;375(1802):20190475. doi: 10.1098/rstb.2019.0475. Epub 2020 May 18.
How do organisms balance different types of recognition errors when cues associated with desirable and undesirable individuals or resources overlap? This is a fundamental question of signal detection theory (SDT). As applied in sociobiology, SDT is not limited to a single context or animal taxon, therefore its application can span what may be considered dissimilar systems. One of the applications of SDT is the suite of acceptance threshold models proposed by Reeve (1989), which analysed how individuals should balance acceptance and rejection errors in social discrimination decisions across a variety of recognition contexts, distinguished by how these costs and benefits relatively combine. We conducted a literature review to evaluate whether these models' specific predictions have been upheld. By examining over 350 research papers, we quantify how Reeve's models (Reeve 1989 , 407-435 (doi:10.1086/284926)) have influenced the field of ecological and behavioural recognition systems research. We found overall empirical support for the predictions of the specific models proposed by Reeve, and argue for further expansion of their applications into more diverse taxonomic and additional recognition contexts. This article is part of the theme issue 'Signal detection theory in recognition systems: from evolving models to experimental tests'.
当与理想个体或资源相关的线索与不理想个体或资源的线索重叠时，生物如何平衡不同类型的识别错误？这是信号检测理论 (SDT) 的一个基本问题。SDT 在应用于社会生物学时，不仅限于单一的背景或动物分类群，因此它的应用可以跨越可能被认为是不同的系统。SDT 的应用之一是 Reeves（1989 年）提出的一系列接受阈值模型，这些模型分析了个体在各种识别背景下应该如何平衡接受和拒绝错误，这些背景的区别在于这些成本和收益如何相对结合。我们进行了文献综述，以评估这些模型的具体预测是否得到了支持。通过检查超过 350 篇研究论文，我们量化了 Reeves 的模型（Reeves 1989，407-435（doi:10.1086/284926））如何影响生态和行为识别系统研究领域。我们发现，总体上，对 Reeves 提出的具体模型的预测有实证支持，并主张进一步扩大这些模型在更多不同分类群和额外识别背景中的应用。本文是主题为“识别系统中的信号检测理论：从不断发展的模型到实验检验”的一部分。
