Complex signals alter recognition accuracy and conspecific acceptance thresholds
Complex signals alter recognition accuracy and conspecific acceptance thresholds
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复杂信号改变识别准确性和同种接受阈值
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Sheng
中科院分区：
文献类型：
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作者：
E. Tibbetts;Ming Liu;Emily C. Laub;Sheng
行为的许多方面都依赖于识别，但准确的识别是困难的，因为用于识别的特征经常重叠。例如，育雏寄生鸟模仿宿主的卵，所以宿主很难区分自己的卵和寄生卵。出现在多个感觉模态中或涉及多个信号分量的复杂信号被认为有助于准确识别。然而，我们缺乏探索复杂信号对识别系统进化影响的模型。在这里，我们使用基于个体的模型与遗传算法来测试复杂的信号如何影响识别阈值，信号表型和接收器响应。该模型有三个主要结果。首先，复杂信号导致比简单信号更准确的识别。其次，当两个信号提供不同的信息量时，接收器将依赖于信息量更大的信号来做出识别决策，并且可能忽略信息量更小的信号。因此，用于识别的特定特征随着发送者和接收者表型的进化而变化。第三，当识别错误的代价高时，复杂信号比当错误的代价低时更有可能演变。总的来说，冗余的复杂信号是减少识别错误的进化稳定机制。这篇文章是“识别系统中的信号检测理论：从进化模型到实验测试”主题的一部分。
Many aspects of behaviour depend on recognition, but accurate recognition is difficult because the traits used for recognition often overlap. For example, brood parasitic birds mimic host eggs, so it is challenging for hosts to discriminate between their own eggs and parasitic eggs. Complex signals that occur in multiple sensory modalities or involve multiple signal components are thought to facilitate accurate recognition. However, we lack models that explore the effect of complex signals on the evolution of recognition systems. Here, we use individual-based models with a genetic algorithm to test how complex signals influence recognition thresholds, signaller phenotypes and receiver responses. The model has three main results. First, complex signals lead to more accurate recognition than simple signals. Second, when two signals provide different amounts of information, receivers will rely on the more informative signal to make recognition decisions and may ignore the less informative signal. As a result, the particular traits used for recognition change over evolutionary time as sender and receiver phenotypes evolve. Third, complex signals are more likely to evolve when recognition errors are high cost than when errors are low cost. Overall, redundant, complex signals are an evolutionarily stable mechanism to reduce recognition errors. This article is part of the theme issue ‘Signal detection theory in recognition systems: from evolving models to experimental tests’.