vdoc.pub_an-introduction-to-cognitive-linguistics

PROTOTYPES AND CATEGORIES 31
lines in Figure 1.8); category membership is also supported by the fact that
the ostrich has a long neck like a flamingo, and decorative feathers like a
peacock (see thin lines in Figure 1.8). In defining the position of a category
member in its category, we are thus justified in considering any sensible
attribute proposed for this item. This is the theoretical background of the
attribute-listing experiments carried out by Rosch and her associates and
the typicality ratings that were based on them.
Attribute listing and attribute-based typicality ratings
Given the ease with which we seem to be able to call up the attributes for
familiar objects and organisms, attributes can be collected in a fairly simple
test procedure that can be easily administered to a large number of subjects.
In the attribute-listing experiments conducted by Rosch and Mervis
(1975), each of the subjects (400 American psychology students) was given
six sheets of paper with the test item written on the top of the page. The
subjects had a minute and a half to write down all the attributes that they
could think of. To eliminate answers that were obviously false or wrongly
attributed to an item or too general in meaning, the attribute lists were
checked by two judges.
The test items used were selected from the lists obtained in the goodnessof-example
ratings described in Figure 1.3 and consisted of sets of 20
graded category members, one set for each of the categories FURNITURE, VEHI-
CLE, FRUIT, WEAPON, VEGETABLE and CLOTHING. Altogether, 120 items from >CHAIR<
to >TELEPHONE< (for FURNITURE), >CAR< to >ELEVATOR< (for VEHICLE) and from
>ORANGE< to >OLIVE< (for FRUIT) were tested.
The experiment had two aims: to demonstrate the notion of family resemblance
(see above) and, more important for Rosch and Mervis, to supply
attribute-based typicality ratings (this neutral term seems preferable to Rosch
and Mervis’s own term ‘measure of family resemblance’). These typicality ratings
could then be used to verify the earlier goodness-of-example ratings.
How were the attribute-based ratings calculated? Leaving aside mathematical
details, two stages can be distinguished. First, the attributes were
‘weighted’, that is, it was established for how many of the 20 tested category
members each attribute had been listed. The top score of 20 was given if
an attribute was shared by all category members (‘means of transport’ in
the case of vehicles). An attribute listed for only one category member (think
of ‘installed in buildings’, which would only fit >LIFT< or >ELEVATOR<) received
the score 1; attributes applying to several but not all category members were
assigned intermediate scores. The result was a list of weighted attributes.