论文标题
学习对象感知的元认知
Learning a metacognition for object perception
论文作者
论文摘要
除了代表外部世界外，人类还代表着自己的认知过程。在感知的背景下，这种元认知有助于我们确定不可靠的感知，例如当我们认识到我们正在看到幻想时。在这里，我们提出了Metagen，这是一种无监督学习元认知的模型。在Metagen中，元认知表示为感知系统如何产生嘈杂感知的生成模型。使用世界如何运作的基本原则（例如对象永久性，婴儿核心知识的一部分），梅蒂根共同渗透世界上的对象，导致感知和代表其自身的感知系统。然后，Metagen可以使用这种元认知来推断世界上实际存在哪些对象。在模拟数据上，我们发现Metagen很快学习了元认知并提高了整体准确性，超过缺乏元认知的模型。
Beyond representing the external world, humans also represent their own cognitive processes. In the context of perception, this metacognition helps us identify unreliable percepts, such as when we recognize that we are seeing an illusion. Here we propose MetaGen, a model for the unsupervised learning of metacognition. In MetaGen, metacognition is expressed as a generative model of how a perceptual system produces noisy percepts. Using basic principles of how the world works (such as object permanence, part of infants' core knowledge), MetaGen jointly infers the objects in the world causing the percepts and a representation of its own perceptual system. MetaGen can then use this metacognition to infer which objects are actually present in the world. On simulated data, we find that MetaGen quickly learns a metacognition and improves overall accuracy, outperforming models that lack a metacognition.
