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Geometric patterns of the type used in studying concept formation
object number
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size
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colour
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shape
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1
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big
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green
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triangle
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2
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big
|
green
|
circle
|
3
|
big
|
red
|
triangle
|
4
|
big
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red
|
circle
|
5
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small
|
green
|
triangle
|
6
|
small
|
green
|
circle
|
7
|
small
|
red
|
triangle
|
8
|
small
|
red
|
circle
|
The experimenter may concoct the rule that all green objects are called GEK. The subject is shown some of the figures, told which are named GEK, and asked to infer the rule or to apply it to other figures. This is roughly akin to teaching a young child to identify a class of barking animals with the name DOG. In both cases a general rule is derived from specific examples.
The problem of discovering that GEK = GREEN is almost trivial when four GEK and four NOT GEK figures are presented at once, but the problem becomes surprisingly difficult if the figures are presented one at a time and need to be remembered. Furthermore, when two concepts are to be learned together (e.g., JIG = TRIANGLE and GEK = GREEN), memory for each concept tends to be mixed, and it becomes a formidable task to solve either problem. This suggests that short-term memory is important to concept learning and that short-term memory can often serve as a limiting factor in performance. The mastery of more-complex concept learning often depends on allotting enough time for the information to be fixed in memory.
Most such experiments involve very simple rules. They properly concern concept identification (rather than formation) when the learner is asked to recognize rules he already knows. Adult subjects tend to focus on one stimulus attribute after another (e.g., shape or colour) until the answer is found. (This represents problem solving with a minimum of thinking; they simply keep guessing until they are right.) People tend to avoid repeating errors but seem to make surprisingly little use of very recent short-term experience.
Most people try out attributes in an orderly manner, first considering such striking features as size, shape, and colour and only later turning to the more abstract attributes (e.g., number of similar figures, or equilateral versus isosceles triangles). This suggests that there is no sharp distinction between discrimination learning (relatively concrete) and concept formation (more abstract); instead, one progresses from the concrete to the abstract.
Study can shift from concept identification to concept learning by requiring combinations of previously learned rules. A conjunctive concept (in which the rule is based on the joint presence of two or more features; e.g., GEK patterns now are LARGE and GREEN) is fairly easy to learn when the common characteristics stand out. But learning a disjunctive rule (e.g., GEK objects now are either LARGE or GREEN but not both) is quite difficult; there is no invariant, relatively concrete feature on which to rely.
Concept learning in adults may be understood as a two-step process: first the discovery of which attributes are relevant, then the discovery of how they are relevant. In the conjunctive illustration used here, the learner is likely first to notice that size and colour have something to do with the answer and then to determine what it is. This two-step interpretation presupposes that the subject has already learned rules for colour, size, shape, or similar dimensions.
In an example of what is called “intradimensional” shift, initially the subject learns that GEK = GREEN; then, without warning, the experimenter changes the rule to GEK = RED. The same attribute or dimension (colour) is still relevant, but the way in which it is used has been changed. In “extradimensional” shift, the relevant dimension is changed (e.g., from GEK = GREEN to GEK = TRIANGLE), but the classification of some objects does not change (GREEN TRIANGLE is a GEK under both rules). The relative ease with which subjects handle such problems suggests something about how they learn. If they tend to learn simply by associating GEK with specific figures without considering the selected attribute, then they should find extradimensional-shift problems easier, since only some of their associations need be relearned. But if they have learned stepwise in terms of relevant attributes (e.g., to say “What is the colour?…Ah, that colour means it is GEK”), intradimensional shift should be easier, since only the “how” phase of the two-step process need be relearned.
College students tend to find intradimensional-shift problems easier, indicating that they are prone to use the two-step process. On the other hand, suppose a rat initially is rewarded when it runs into the right-hand side of a maze for food, then a change is made by rewarding entries to the left (intradimensional shift) or by rewarding entries to any brightly lighted alley regardless of location (extradimensional shift). The rat will perform best on the extradimensional-shift problem. Among children, performance depends substantially on age. Preschool children are likely to do best with extradimensional shifts (as rats do), but children beyond kindergarten age tend to find the intradimensional shift easiest.
Concepts need not be limited to simple classifications. They also can be interpreted as models or rules that reflect crucial possibilities for change. To take a simple case, an adult is not apt to think that the volume of water changes when it is poured into a container of different shape.
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