Born to run? Quantifying the balance of prior bias and new information in prey escape decisions

Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new information is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes' theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new information gathered by individuals. We present methods for inferring distributions of prior biases within populations rather than assuming known priors, as is common in Bayesian approaches to modelling behavior, and for evaluating the goodness of fit of overall model descriptions. We apply this framework to predict optimal escape during predator-prey encounters, based on prior biases and variation in what information prey use. Using th...

动物行为的建模与预测往往具有挑战性，尽管最优性理论已提升了我们对此的能力。尽管存在许多关于行为的定性预测，但经实证数据检验的准确量化模型却常付阙如。这可能源于个体间偏见的差异，以及新信息收集与使用方式的不同。我们提出一种基于贝叶斯定理（Bayes' theorem）新诠释的建模框架，将能量约束优化与个体的先验偏见及新信息的特定来源相整合。我们提出了两种方法：其一，推断种群内先验偏见的分布，而非像行为建模的贝叶斯方法中常见的那样假设已知先验；其二，评估整体模型描述的拟合优度。我们应用该框架，基于先验偏见及猎物所使用信息的差异，预测捕食者-猎物相遇时的最优逃逸行为。利用...