Concepts, Perceptions and Their Interactions

提出的实验和模型探索人们如何学习新概念。中心论点是概念学习经常改变物体的感知方式。也就是说，概念和类别可能是在对象的感知特征的基础上构建的，但这些概念可能反过来影响我们所看到的感知特征。所提出的工作的目的是详细说明并提供概念学习和感知之间这种相互作用的正式说明。在第一个系列的实验中，研究了概念学习对后续知觉判断的影响。第二个系列的实验考虑了概念学习导致新的对象描述（基于感知特征）产生的可能性。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，被获取的概念改变了用于分类的感知特征。这个模型不是假设固定的感知特征被结合起来决定分类规则，而是允许概念和感知之间相互和同时的影响。这项研究将有两个切实的好处。第一个好处是进一步加深我们对人类自然学习方式的理解。与大多数实验室研究不同，现实世界中的概念学习通常涉及感知内容的改变。例如，专业专家学会提高他们的洞察力，并能够更好地进行分类，例如，将某些患者分类为癌症患者，将某些肿瘤分类为恶性肿瘤，或将某些油田分类为有前途的油田。如果我们能够理解专家是如何通过经验来提高他们的感知能力的，我们将能够更好地有效地培训新员工。第二个切实的好处将是帮助构建类似人类的自动分类设备。通过理解人们用来创建新类别的机制，我们将处于更好的位置来开发程序，这些程序也可以以智能的方式对世界上的对象进行分类。

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.