Concepts, Perceptions and Their Interactions

所提出的实验和模型探讨了人们如何学习新概念。中心论点是，概念学习往往会改变物体的感知方式。也就是说，概念和范畴可以根据对象的感知特征来构建，但这些概念反过来又会影响我们所看到的感知特征。拟议的工作的目的是指定和提供一个正式的帐户概念学习和感知之间的这种相互作用。 在第一系列的实验中，概念学习对随后的知觉判断的影响进行了研究。 概念学习导致新的对象描述（基于感知特征）被创建的可能性被认为是在第二系列的实验。提出了一种概念学习的神经网络计算机模型。在这个模型中，要获得的概念改变用于分类的感知特征。 而不是假设固定的感知特征相结合，以确定分类规则，该模型允许概念和感知之间的相互和同时的影响。 这项研究将带来两个切实的好处。第一个好处将是进一步了解人们如何自然学习。 与大多数实验室研究不同，真实的世界中的概念学习通常涉及感知的改变。 例如，专业专家学会改善他们的感知，并能够更好地分类，例如，将一些患者归类为患有癌症，将一些肿瘤归类为恶性肿瘤，或将一些油田归类为有希望的。 如果我们能够理解专家如何通过经验学习来改善他们的感知，我们将能够更好地有效地培养新人才。第二个切实的好处将是帮助构建自动化设备，以进行类似人类的分类。 通过理解人们用来创建新类别的机制，我们将能够更好地开发出能够以智能方式对世界上的对象进行分类的程序。

The proposed experiments and model explore how people learn new concepts. The central thesis is that concept learning often changes how objects are perceived. That is, concepts and categories may be constructed on the basis of the perceptual features of objects, but these concepts may in turn influence what we see as the perceptual features. The aim of the proposed work is to specify and provide a formal account of such interactions between concept learning and perception. The influences of concept learning on subsequent perceptual judgments are investigated in a first series of experiments. The possibility that concept learning causes new object descriptions (based on perceptual features) to be created is considered in the second series of experiments. A neutral network computer model of concept learning is proposed. In this model, the concepts to be acquired alter the perceptual features used for categorization. Rather than assuming that fixed perceptual features are combined to determine categorization rules, this model allows for a mutual and simultaneous influence between concepts and perceptions. The research will have two tangible benefits. The first benefit will be to further our understanding of how people naturally learn. Unlike most laboratory studies, concept learning in the real world does typically involve alterations in what is perceived. For example, specialized experts learn to improve their perception and come to be able to classify better, for example, to classify some patients as suffering from cancer, some tumors as malignant, or some oil fields as promising. If we can understand how experts learn to improve their perception with experience, we will better be able to effectively train new personnel. The second tangible benefit will be to aid the construction of automatic devices for human-like categorization. By understanding the mechanisms that people use to create new categories, we will be in a better position to develop procedures that also can categorize objec ts in the world in an intelligent manner.