Foraging as an evidence accumulation process
Foraging as an evidence accumulation process
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DOI：
10.1371/journal.pcbi.1007060
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发表时间：
2019-07-01
影响因子：
4.3
通讯作者：
El Hady, Ahmed
中科院分区：
文献类型：
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作者：
Davidson, Jacob D.;El Hady, Ahmed
补丁离开问题是一个典型的觅食任务，觅食者必须决定离开当前的资源去寻找另一个资源。理论工作已经得出了何时离开斑块的最佳策略，实验已经测试了动物是否遵循最佳策略的情况。然而，斑块离开决策模型没有考虑到动物收集信息的不完善和嘈杂的采样过程，以及这个过程如何受到神经生物学机制的约束。在这项理论研究中，我们制定了一个证据积累模型，在该模型中，动物对噪声测量进行平均，以估计当前斑块和整体环境的状态。我们求解了觅食决策最优且等价于边际值定理的条件下的模型，并进行了模拟，分析了不满足这些条件时与最优的偏差。通过调整漂移率和决策阈值，模型可以表示不同的“策略”，例如增量、递减或计数策略。这些策略在极限情况下产生相同的决策，但在觅食环境不确定时斑块停留时间的适应方式不同。为了描述次优决策，我们引入了一个能量依赖的边际效用函数，该函数在食物充足时预测比最优斑块停留时间更长。我们的模型在觅食行为的生态模型和决策的证据积累模型之间提供了定量的联系。此外，它为潜在的实验提供了一个理论框架，这些实验试图识别补丁离开决策背后的神经回路。
The patch-leaving problem is a canonical foraging task, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different "strategies", for example an incremental, decremental, or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To describe sub-optimal decisions, we introduce an energy-dependent marginal utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch-leaving decisions.