PRINCIPLES OF CATEGORIZATION by Eleanor Rosch
Rosch begins by making the point that human categorization should
not be considered as arbitrary, eg as of the product of historical
accident or whimsy. Alovely quote by Borges is given to demonstrate
the sort of system that doesn't occur in the classification of
animals: `a) those that belong to the emperor, b) embalmed ones, c)
those that are trained.....'
Two general principles are proposed for the formation of categories.
First, cognitive economy - `to reduce the infinite differences among
stimuli to behaviourally and cognitively usable proportions'.
Second, it reflects the perceived world structure, as the objects in
the world possess high correlational structure e.g. it is an
empirical fact that wings co-occur more often with feathers than
with fur.
The distinction between vertical and horizontal levels of
categorization is made. The vertical dimension concerns the level of
inclusiveness of the category - the dimension along which the terms
collie, dog, mammal, animal vary. The horizontal dimension concerns
the segmentation of categories at the same level of inclusiveness -
the dimension along which the terms car, dog, chair etc vary. Rosch
argues that the use of prototypes, which contain the most
representative attributes inside the category, would increase the
flexibility and distinctiveness of categories along the horizontal
dimension.
Rosch did a series of experiments which showed the way in which these
principles appeared to result in a basic level of categorization, as
opposed to a superordinate or subordinate level (eg
superordinate=furniture; basic=chair; subordinate=kitchen chair.
Basic level categories had more cue validity and category
resemblance than other levels. Cue validity is a probabilistic
concept; the validity of a given cue x as a predictor of a given
category y. A category with high cue validity is, by definition,
more differentiated from other categories than one with low cue
validity. Category resemblance is defined as the weighted sum of the
measures of all the common features within a category minus the sum
of the measues of all the distinctive features. Thus superordinate
categories have lower cue validity and category resemblance than
basic level categories, because they have fewer common attributes.
Subordinate categories have lower total cue validity than basic
categories, because they share more attributes with other
subordinate categories (eg kitcen chair & dining room chair) Rosch
investigated 4 converging operational definitions of the basic level
of abstraction: attributes in common, motor movements in common,
objective similarity in shape, and identifiability of averaged
shapes. She found that objects were first percieved at their basic
level; that basic level words were the first to be acquired in
infancy and that the basic level is linguistically the most useful.
Prototypicality was found to be governed by the same principles such
as maximization of cue validity and category resemblance as those
principles governing the formation of the categories themselves.
Rosch and Mervis (1975) thus found that the more prototypical of a
category a member is rated, the more attributes it has in common
with other members of a category and the fewer attributes in common
with members of contrasting categories. This may be explained in 2
ways. 1. that such structure is given by the correlated clusters of
attributes in the real world. 2. the structure may be the result of
the human tendency, once a contrast exists, to define attributes for
contrasting categories so that the categories will be maximally
distinctive.
Rosch also presented evidence that prototypes of categories are
related to the major dependent variables with which psychological
processes are usually measured. These include things like speed of
processing, where subjects decide whether x is a member of category
y, speed of learning of artificial categories, order and probability
of item output, i.e. when subjects list examples of a category.
However prototypes do not specify representation and process models.
For example, in pattern recognition, prototypes can be described as
well by feature lists, structural descriptions or templates. Also
prototypes can be represented by both propositional and image
systems.
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