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 individual's 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.
On the crustal bias of repeat stations in Romania
