Learning new features of representation.

Goldstone, R. L., & Schyns, P. (1994). Learning new features of representation. Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. (pp. 974-978). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

One productive and influential approach to cognition maintains that categorization, object recognition, and higher-level cognitive processes operate on the output of lower-level perceptual processing.That is, our perceptual systems provide us with a set of fixed features. These features are the inputs to higher-level cognitive processes.

Recently, researchers in psychology, computer science, and philosophy have questioned this unidirectional approach, arguing that in many situations, the high-level cognitive process being executed has an influence on the lower-level features that are created. For example, in addition to categorization being based on featural descriptions of objects, it might also be the case that the categorization process partially creates the featural decriptions that are used. Rather than viewing the “vocabulary” of primitives to be fixed by low-level processes, this view maintains that the vocabulary is dependent on the higher-level process that uses the vocabulary. This symposium will investigate several issues related to bidirectional interactions between high-level and low-level cognitive processes.

  • Medin, Douglas L. The Pervasiveness of Constructive Processes
  • Thibaut, Jean-Pierre. Role of Variation and Knowledge on Stimuli Segmentation: Developmental Aspects
  • Mozer, Michael. Computational Approaches to Functional Feature Learning
  • French, Robert. Representation-building in Analogical Reasoning
  • Schyns, Philippe G. A Functional Approach to Feature Learning