Schyns P G, Goldstone R L, Thibaut J P
Department of Psychology, University of Glasgow, United Kingdom.
Behav Brain Sci. 1998 Feb;21(1):1-17; discussion 17-54. doi: 10.1017/s0140525x98000107.
According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individuals existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction.
根据一种富有成效且具影响力的认知方法，分类、物体识别及更高级别的认知过程基于一组固定特征进行运作，这些特征是较低级别的感知过程的输出。然而，在许多情况下，正是正在执行的更高级别的认知过程影响着所创建的较低级别的特征。我们并非将特征库视为由低级过程固定，而是提出一种理论，即人们创建特征以服务于物体的表征和分类。应区分两种类型的类别学习。当新的分类可用现有的特征集来表征时，就会发生固定空间类别学习。当新的分类无法用可用特征来表征时，就会发生灵活空间类别学习。无论固定还是灵活，学习都取决于要表征的新类别与个体现有概念之间的特征对比和相似性。对于需要新特征的任务，固定特征方法面临以下两个问题之一：如果固定特征相当高级且对分类直接有用，那么它们将不够灵活，无法表征所有可能与新任务相关的物体。如果固定特征是小的、亚符号片段（如图像元素），那么完成分类所需的功能特征层面的规律将无法被这些原语捕捉。我们展示了类别学习引发的灵活感知变化的证据以及关于这种灵活性重要性的理论论证。我们描述了促进特征创建的条件，并反对用固定特征来解释它们。最后，我们讨论功能特征对物体分类、概念发展、组块、建设性归纳以及降维形式模型的影响。
