Associative System and Sensitivity to Structure

Representation in the Associative System

All the associative responses discussed previously were based on fairly global correspondences between concepts represented as (more or less structured) sets of features. Concepts were not first distilled into one or two relevant features. For example, participants had to use features to compute the similarity between Linda and feminists because they had no information about Linda other than a feature list. Little task-specific selection or differential weighting of features took place because performance was predictable from similarity judgments taken out of the problem context.

The conspicuous feature of the data I have reviewed is the extent to which people’s modal inferences involved computations that considered only similarity structure and associative relations. This claim might appear contrary to work showing that associative judgments of similarity and probability can depend on hierarchical and causal structure. Markman and Gentner (1993) and Medin, Goldstone, and Gentner (1993) have shown that similarity judgments can be strongly influenced by structured relations. The point is buttressed by Tversky and Kahneman (1983), who showed that the presence of a causal relation can increase a statement’s representativeness. Their participants judged that John P. was less likely to kill one of his employees than he was to kill one of his employees to prevent him from talking to the police (a conjunction fallacy). The added motivation produced by the causal relation made the proposition seem more likely. The causal statement is more representative than the noncausal one of the standard model of murderers; we tend to think of murderers as motivated. In short, people seem to be sensitive to both hierarchical and causal structure when performing associative operations.

On one hand, I argue that certain judgments are associative and yet they are sensitive to hierarchical and causal structure. On the other hand, I argue that only rules, and not associations, can represent such structure. These arguments are not contradictory because mere sensitivity does not imply representational capacity. Similarity and probability judgments could be sensitive to hierarchical and causal relations because they depend on representations constructed by rules and those rules could construct different representations depending on hierarchical and causal knowledge. To illustrate, we can conclude from the example of our suspected murderer John P. that the similarity of an action to the actions we expect of a murderer is increased by providing a cause for the action, in particular a motivation for a murder. Such a conclusion has two conditions. First, we must be able to comprehend that killing an employee to prevent him from talking to the police is a causal relation from the motive of preventing to the action of killing. Because it involves a causal relation, I claim that such comprehension involves at least one rule. Second, we must decide that a description that includes a motivation is more similar to our model of a murderer than a description that does not. I claim that this operation is associative. In this fashion, we can take the causal analysis out of the associative part of the computation.

Two further properties of associative thought are noteworthy. The first is attributable to James (1890), who pointed out that although associative thought often deals in concrete images, it can also deal in abstract concepts. For instance, we can easily think about water or sheep as general categories, not only as particular instances. When thinking about wool, we might make use of an association to sheep, but not to any sheep in particular, rather to sheep as a category. Second, and contrary to James, the associative system is not simply reproductive, but can deal with novel stimuli. The similarity judgments underlying the conjunction fallacy and the inductive argument strength phenomena were not retrieved from memory. The comparison process took place on-line.