The ability to extract reliable segments from the sensory data-stream is necessary for learning and for creating a reliable representation of the environment. Here we focused on segmentation in visual learning associated with foraging. Fifty-one pigeons (Columba livia) were trained to recognize two food-predicting signals, one presented against fixed background elements and the other against varying backgrounds. The pigeons were divided into two groups: 32 pigeons were trained with short intervals of 3−5 min between sessions and 19 with long intervals of 60 min between sessions. When tested on the signal with a plain background, 24 pigeons trained with the short time intervals preferred the signal presented against a varying background during training and eight preferred the signal presented against a fixed background during training. Nine pigeons trained with the long intervals preferred the signal presented against a varying background during training and ten the signal presented against a fixed background during training.

Our findings indicate that pigeons possess an ability to use variation-set structure to segment visual data. To date, the effect of variation-set structure has been demonstrated only in the context of human language acquisition. Our current findings suggest that variation sets structure may be linked to other learning abilities too.

从感觉数据流中提取可靠段的能力对于学习和创建环境的可靠表示是必不可少的。在这里，我们专注于与觅食相关的视觉学习中的细分。51羽（Columba livia）经过训练，可以识别两种食物预测信号，一种针对固定的背景元素呈现，另一种针对变化的背景呈现。将鸽子分为两组：训练32羽鸽子，每节间隔3-5分钟，而训练19羽，长间隔60分钟。当对具有纯背景的信号进行测试时，以短时间间隔训练的24羽鸽子在训练过程中倾向于在变化的背景下呈现信号，而八羽则在训练过程中倾向于在固定的背景下呈现信号。经过长时间间隔训练的9羽鸽子更喜欢在训练过程中针对变化的背景呈现的信号，而十种在训练过程中针对固定背景的呈现信号。

我们的发现表明，鸽子具有使用变异集结构分割视觉数据的能力。迄今为止，仅在人类语言习得的背景下证明了变异集结构的效果。我们目前的发现表明，变异集结构也可能与其他学习能力有关。