The classical view of categorization was that we sort things into
categories based on feature detection i.e by those features that are
necessary and sufficient to sort them.
Rosch had several objections to the classical view. She pointed out
that subjects are unable to say which features they are using when
they sort items into categories, but they can tell you which they
believe to be most typical. Rosch also draws on Wittgensteins
arguments, using the oft quoted example of our inability to define a
game based on its necessary and sufficient features. Different games
share family resemblances, but have no common invariant feature.
This led Rosch to build categories around prototypes.
Rosch and Mervis (1975) 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
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
Pattern matching theorists argued against the prototype theory,
maintaining that categories are based on feature detection as there
is a correlation between features and outcomes. In addition, many of
Rosch's criticisms of the feature detection model do not bear
careful analysis. Firstly, it is not a valid argument to claim that
because subjects can't pick out the features they use to categories,
they are not using features. Most of our methods of doing cognitive
tasks are not evident by introspection. Also Rosch confuses ontic
metaphysical questions with epistemic questions. The psychologist is
interested in how we actually categorize, and so there is little
point focussing on the grey areas, where people are not sure which
category to put an item in, as this leads on to questions about what
things really are.
Rosch's prototype idea can also be criticised on the basis that it
doesn't really give any thoughts to the mechanisms that lie behind
our ability to categorize. Any mechanism has to ignore irrelevant
features and pay attention to relevant, invariant features. This can
be illustrated by the ugly duckling theorem, which proves that 7
white geese and 1 black swan are all equally different if every
feature is equally weighted. If every feature is equally weighted,
then (like Funes) we would not be able to abstract the relevant
invariant features - everything would be infinitely unique.
Categories enable cognitive economy - `to reduce the infinite
differences among stimuli to behaviourally and cognitively usable
Computationalists think we categorize on the basis of rules.
Connectioninst think its done by strength of weights - i.e
invariance extraction eg neural nets can take input, get feedback
and find what input is correlated with what feedback - leads to
weighting of relevant features. With unsupervised learning, neural
nets will sort items into categories without feedback, this really
just enhances natural contours.With supervised learning, neural nets
use the feedback signal to pick out the invariant or salient
features. This results in the formation of categories, which we then
The categorization that we have enables us to ground symbols in our
capacity to use them. If we distinguish between inputs, we are
categorizing. Funes and mnemonist provide examples of the problems
that arise from an inability in abstracting the invariant features
A philosophic objection, known as the vanishing intersections
objection claims that the neural net could not be detecting
invariant features of categories relative to other categories as the
invariant feature doesn't exist. But this leads to metaphysics-
psychologists are interested in how we sort stuff out ( eg not
interested in what apples really are, or if its still an apple if
its square and blue).
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