Understanding Representativeness Heuristic
According to Tversky and Kahneman (1974), many of the probabilistic questions that people are confronted with can be characterized by “what is the probability that object A belongs to class B” or “what is the probability that event A originates from process B?” To answer questions like these, people make use of the representative heuristic, in which probabilities are evaluated by the degree to which A resembles B.
Representative heuristic is the tendency to judge the likelihood that an object belongs to a certain category based on how similar the object is to the typical features of the category (or how representative it is of the category).
For instance, deciding that the dog you saw last night in the park was probably a poodle because it had curly hair that was cut in a poodle style illustrates the representativeness heuristic. This heuristic, which relies on the extent to which a target resembles the typical case, is akin to categorization Opens in new window—the most basic of our cognitive processes.
Categorizing an object by assessing the overlap between its features and the defining features of a category is perfectly reasonable—this is precisely how we use schemas Opens in new window to categorize an object so we can predict its behavior (Fiske & Taylor, 1991). As an example, a small animal that looks like a cat and meows like a cat probably is a cat. Similarly, when A is highly representative of B, the probability that A originates from category B is judged to be high.
People’s assessment of the likelihood that an event will occur is influenced by how similar the occurrence is to their mental representation (stereotype) of similar experiences. The representativeness heuristic is related to the base rate fallacy.
The representative heuristic usually serves us well in evaluating the probabilities dealing with objects or processes. The problem, however, is that heavy reliance on representativeness (similarity) leads people to ignore other factors that help shape events, such as rules of chance, independence, and base rate information. Consider the following example:
Tom is a 41-year-old who reads nonfiction books, listens to National Public Radio, and plays tennis in his spare time. Which is more likely?
- (a) Tom is an Ivy League professor.
- (b) Tom is a truck driver.
Most people answer (a) because Tom seems like a typical Ivy League professor. People fail to consider, however, that there are a lot more truck drivers than there are Ivy League professors. Thus, in making that judgment, people rely on one kind of information (representativeness, which means how well Tom resembles the category of professors) instead of another (how many people are in the category).
Two factors that leave us more and more hostage to the representativeness heuristic, is that,
- The representativeness heuristic is a built-in feature of the brain for producing rapid probability judgments, rather than a consciously adopted procedure.
- As humans, we are not aware of substituting judgment of representativeness for judgment of probability.