Insects search for and find odor sources as their basic behaviors, such as when looking for food or a mate. This has motivated research to describe how they achieve such behavior under turbulent odor plumes with a small number of neurons. Among different insects, the silk moth has been studied owing to its clear motor response to olfactory input. In past studies, the “programmed behavior” of the silk moth has been modeled as the average duration of a sequence of maneuvers based on the duration of periods without odor hits. However, this model does not fully represent the fine variations in their behavior. In this study, we used silk moth olfactory search trajectories from an experimental virtual reality device. We achieved an accurate input by using optogenetic silk moths that react to blue light. We then modeled such trajectories as a probabilistic learning agent with a belief of possible source locations. We found that maneuvers mismatching the programmed behavior are related to larger entropy decrease, that is, they are more likely to increase the certainty of the belief. This implies that silkmoths include some stochasticity in their search policy to balance the exploration and exploitation of olfactory information by matching or mismatching the programmed behavior model. We believe that this information-theoretic representation of insect behavior is important for the future implementation of olfactory searches in artificial agents such as robots.

昆虫寻找并找到气味来源作为其基本行为，例如在寻找食物或伴侣时。这激发了研究来描述它们如何在具有少量神经元的湍流气味羽流下实现这种行为。在不同的昆虫中，由于蚕蛾对嗅觉输入具有明显的运动反应，因此对其进行了研究。在过去的研究中，基于没有气味侵袭的持续时间，将蚕蛾的“程序行为”建模为一系列动作的平均持续时间。但是，此模型不能完全代表其行为的细微变化。在这项研究中，我们使用了来自实验性虚拟现实设备的蛾嗅觉搜索轨迹。我们通过使用对蓝光起反应的光遗传蚕蛾获得了准确的输入。然后，我们将这种轨迹建模为概率学习代理，并相信可能的源位置。我们发现，与编程行为不匹配的演习与更大的熵降低有关，也就是说，它们更有可能增加信念的确定性。这意味着家蝇在其搜索策略中包括一定的随机性，以通过匹配或不匹配编程的行为模型来平衡对嗅觉信息的探索和利用。我们相信，昆虫行为的这种信息理论表示对于在人工代理（例如机器人）中进行嗅觉搜索的未来实现非常重要。他们更有可能增加信念的确定性。这意味着家蝇在其搜索策略中包括一定的随机性，以通过匹配或不匹配编程的行为模型来平衡对嗅觉信息的探索和利用。我们相信，昆虫行为的这种信息理论表示对于在人工代理（例如机器人）中进行嗅觉搜索的未来实现非常重要。他们更有可能增加信念的确定性。这意味着家蝇在其搜索策略中包括一定的随机性，以通过匹配或不匹配编程的行为模型来平衡对嗅觉信息的探索和利用。我们相信，昆虫行为的这种信息理论表示对于在人工代理（例如机器人）中进行嗅觉搜索的未来实现非常重要。