Читать книгу The Research Experience - Ann Sloan Devlin - Страница 29

Research is essentially about problem-solving, and humans are very good problem solvers. Relatedly, Kuhn (1962) describes normal science as puzzle-solving. What makes humans good at these kinds of activities? In addition to common sense, we can imagine objects used in a variety of ways. In essence, seeing potential or flexibility is a form of creativity. This kind of problem-solving creativity we have as humans was described by Hubert Dreyfus (1972) when he said that humans don’t necessarily see the function of an object as fixed. Consider using a turkey baster to fill a sports car running low on transmission fluid or a door as a desk surface. The artist Marcel Duchamp used found objects, called readymades, as art; his bicycle wheel mounted upside down on a stool from 1913 is a well-known example. Nevertheless, we shouldn’t take this flexibility for granted, for either objects or processes. For example, we may apply the same process (procedure) when it is no longer appropriate to solve a problem. This is essentially a problem-solving set effect, meaning that we approach a problem using an established (repeated) procedure. In other words, we don’t recognize that there might be a more efficient way of solving the problem. This repeated procedural approach is a problem for researchers because we might settle in on a particular approach to evaluate a hypothesis because that is what other researchers have done (the tradition). Until Sperling introduced the partial report technique, scientists’ estimate of the capacity of immediate visual memory was limited by the procedure used (the whole report) to measure it. We need to stop and ask ourselves how else we might go about investigating that particular issue. Can we improve on the tradition?

In the case of work on bias and discrimination, for example, researchers have been limited by using scales that directly ask questions about beliefs and attitudes. For example, an item from the Modern Sexism Scale is “It is rare to see women treated in a sexist manner on television” (Swim et al., 1995). Participants who see such scale items are likely to self-monitor and answer with social desirability, presenting themselves in a good light (see Chapter 5 on measures).

Social desirability: Responding to experimental stimuli and/or scales in a way that presents the respondent in a positive (socially appropriate) light.

A procedural breakthrough in addressing these kinds of problems with self-report measures has come in the form of Implicit Association Tests (IATs; Greenwald et al., 2003), which use reaction time to measure people’s associations to topics (e.g., race, sex, obesity, and age) where your explicit response might be different than your implicit response (see https://implicit.harvard.edu/implicit/takeatest.html if you want to try out an IAT for yourself). If the pairing of “fat people” with the adjective “good” takes longer to react to than the pairing of “thin people” with the adjective “good,” then we, as well as the individual taking the IAT, have learned about whether the individual’s biases are congruent with the explicit positions that person expresses about weight. In all likelihood, if we had only explicitly asked about people’s attitudes toward people who are thin versus heavy, we would not see differences. Generally, people do not want to appear biased, in this case, against those in a particular weight category.

The challenge of research is to appreciate what previous studies have shown us (tradition) without becoming limited by them in the questions we can ask (innovation). But with experience, our thought processes become routinized, regularized, and less likely to see the new in the old, to think outside the box. All too soon we are unwilling to break out of the box. Are you up to the challenge?!