This limited ability to process information has some important effects on how humans organize material (and think about research). To manage the overload of information around us, humans evolved to chunk or categorize information into groupings or clusters. This kind of organization leads us to form overarching categories; there are words that designate those categories, like vegetable or sports or furniture. A term that is often used to describe such mental representations of knowledge is a schema. If you have a schema for something, you understand its gist or essence; a schema serves as a generalized description of the core characteristics of a given role, object, or event. You might have a schema for a role (e.g., father), for an object (e.g., a chair), or for an event (e.g., going to a restaurant). The benefit of having a schema is that it provides a condensed version of the information that is available about an entity in the world and it helps you make predictions.
Schema: Mental representation of a category that can be a role, an object, or an event (e.g., parent, table, or going to the dentist, respectively).
The ability to compartmentalize by categories minimizes the cognitive load and leaves the brain available to respond to incoming information that may have implications for survival (a car speeding toward a pedestrian; a loud noise). That’s the upside. The downside is that such compartmentalization leads to stereotypes and overgeneralizations, which can interfere with thinking objectively about research. Redheads are tempestuous, people who live in Detroit drive American-made cars, New Yorkers like to wear black, and so on. The propensity for categorization may lead humans to minimize the differences across dimensions and to categorize stimuli as similar when, in fact, there may be important differences.
Heuristics and the Work of Kahneman and Tversky
This chapter has presented some advantages and disadvantages to the formation of schemas. Let’s talk about some other cognitive characteristics of humans and how they interact with the research process. In particular, the focus will be on what are known as cognitive heuristics or mental shortcuts and how they both shape research questions and the answers participants provide.
The researchers Daniel Kahneman and Amos Tversky (see, for example, Kahneman & Tversky, 1972, 1973, 1979; Tversky & Kahneman, 1971, 1973, 1974) studied these predictive tendencies (heuristics) or shortcuts in thinking. Kahneman received the Nobel Prize in Economics (psychologists like to claim him as one of their own) for the work he and Tversky did on these cognitive biases. (Nobel prizes are awarded only to living recipients, and Tversky had died by the time the work was honored.)
There is evolutionary value in being an animal that operates on incomplete information and the ability to use schemas for prediction. The work of Kahneman and Tversky focuses on these heuristics or shortcuts and illustrates how these shortcuts may lead humans to incorrect decisions. Before you become discouraged about human capabilities, it’s useful to remember that the work of Kahneman and Tversky applies to particular kinds of decision-making problems, not to all problems. A good deal of their work focuses on the idea of representativeness (e.g., Kahneman & Tversky, 1972) and availability (e.g., Tversky & Kahneman, 1973), both of which have applications to the research process. Here the idea of representativeness is its frequency of occurrence in a population. It can also mean the extent to which an array of events or objects or people reflects the characteristics of its parent population (discussed in terms of sampling). Availability involves using examples that come easily to mind (e.g., because you just read an article on that topic).
Availability: One of the heuristics talked about by Kahneman and Tversky (1972) in which humans use examples that easily come to mind.
The Representativeness Heuristic in Research
In one of Kahneman and Tversky’s classic examples, participants were presented with the following: “All families of six children in a city were surveyed. In 72 families the exact order of births of boys and girls was GBGBBG. What is your estimate of the number of families surveyed in which the exact order of births was BGBBBB?” (Kahneman & Tversky, 1972, p. 432). Not surprisingly, a significant number of the respondents (75 of 92) said the second option was less likely to occur because, as Kahneman and Tversky argued, it seems less representative of the population. When the question is posed in terms of the frequency with which two birth sequences occur (BBBGGG vs. GBBGBG), the same participants pick the second sequence. The first looks “fixed” or nonrandom to us (and them). How representative something looks is one heuristic or bias that may influence the research process. A researcher might select a stimulus (e.g., photograph) as representative of a population of interest (e.g., urban parks) without knowing the full range of existing sites (compare Figure 1.1 and Figure 1.2). In this instance, both of these photos come from the same park (Brooklyn Bridge Park in Brooklyn, New York) but would communicate very different aspects of the park depending on which photograph was used. If you try to generalize from a single picture or even a limited range of pictures to say something definitive about people’s evaluations of such settings, the results might be overstated.
Figure 1.1 Pier 2 at Brooklyn Bridge Park
©Ann Sloan Devlin
Figure 1.2 View of the Brooklyn Bridge From the Brooklyn Bridge Park Greenway
©Ann Sloan Devlin
Although the work of Kahneman and Tversky focuses on cognitive decision-making processes (e.g., the decisions we make about stimuli), the idea of representativeness emerges in other ways in research. You may be familiar with such phrases as “a representative sample” or “a randomly selected sample” (the example from The Big Short earlier in this chapter raised the issue of sampling; see Chapter 11 for a fuller discussion of sampling).
One central question in every research project is who the participants are and to what extent the results of the study are therefore “representative” of the population of interest. If research uses a participant pool that consists of students enrolled in an introductory psychology course, there are several questions to ask about who participates, starting with the degree to which people who take an introductory course in psychology are representative of that student body as a whole (by gender, race, income, and many other qualities). Every decision about securing participants (e.g., the time of day the study is offered) is likely to influence the representativeness of the sample and, in turn, of the results.
The Availability Heuristic in Research
Let’s now turn to the availability heuristic, the second heuristic from Kahneman and Tversky to be discussed. The availability heuristic suggests that humans make decisions to some extent based on how easy it is to think of examples from that domain. One well-known example of Kahneman and Tversky’s work on availability involves the judgment of word frequency (Tversky & Kahneman, 1973). Take the letter K. Question: In words with three or more letters in English text, does the letter K appear more frequently in the first or third position?
When we hear this question about the letter K, what happens? We start to generate words that begin with the letter K because it is available to us. That seems easier to do than to think of words with K in the third position. But, after you’ve run out of key, knife, knight,