Categories and percepts: a bi-directionnal framework for categorization

Categorization is a fundamental process whereby variable perceptual inputs are reduced progressively to a small number of equivalence classes, called 'categories'. How do theorists frame the categorization problem so that variable face, object and scene information is stabilized for higher-level processing? One approach suggests that perception delivers a fixed set of features that are treated as inputs to categorization processes. This paper questions this unidirectionality and argues instead that perceptual organization is linked intimately with the categorization being accomplished. An example is presented where it can be shown that the history of categorization changes the featural analysis and the perception of identical, but unfamiliar, inputs. Flexible percepts are then generalized to real-world scene categorization in the space of multiple spatial scales. This evidence suggests that a bidirectional framework, with direct interactions between concepts and percepts, is a more appropriate framework for categorization.