Internal states shape stimulus responses and decision-making, but we lack methods to identify them. To address this gap, we developed an unsupervised method to identify internal states from behavioral data and applied it to a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using feedback cues from their partner. Our model uncovers three latent states underlying this behavior and is able to predict moment-to-moment variation in song-patterning decisions. These states correspond to different sensorimotor strategies, each of which is characterized by different mappings from feedback cues to song modes. We show that a pair of neurons previously thought to be command neurons for song production are sufficient to drive switching between states. Our results reveal how animals compose behavior from previously unidentified internal states, which is a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity and motor outputs.

中文翻译：

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对塑造自然行为的内部状态进行无监督识别。

内部状态塑造刺激反应和决策，但我们缺乏识别它们的方法。为了解决这一差距，我们开发了一种无监督方法来从行为数据中识别内部状态，并将其应用于动态社交互动。在求爱过程中，雄性黑腹果蝇使用来自伴侣的反馈线索来模仿他们的歌曲。我们的模型揭示了这种行为背后的三种潜在状态，并且能够预测歌曲模式决策中的瞬间变化。这些状态对应于不同的感觉运动策略，每个策略的特点是从反馈线索到歌曲模式的不同映射。我们表明，一对以前被认为是歌曲产生的命令神经元的神经元足以驱动状态之间的切换。