Story of Psychology - Morton Hunt [361]
Analogical reasoning is acquired in the later stages of childhood mental development. Dedre Gentner, a cognitive psychologist, asked five-year-olds and adults in what way a cloud is like a sponge. The children replied in terms of similar attributes (“They’re both round and fluffy”), adults in terms of relational similarities (“They both store water and give it back to you”).88
Gentner interprets analogical reasoning as a “mapping” of high-level relations from one domain to another; she and two colleagues even wrote a computer program, the “Structure-Mapping Engine,” that simulates the process. When it was run on a computer and provided with limited data about both the atom and the solar system, the program, like the great physicist Lord Rutherford, recognized that they are analogous and drew appropriate conclusions.89
With difficult or unfamiliar problems, people generally do not use analogical reasoning because they only rarely spot a distant analogy, even when it would provide the solution to their problem. But if they consciously make the effort to look for an analogy, they are far more apt to see one that is not at all obvious. M. L. Gick and Keith Holyoak used Duncker’s classic problem, of which we read earlier, about how one can use X-rays to destroy a stomach tumor without harming the surrounding healthy tissue. Most of their subjects did not spontaneously discover the solution; Gick and Holyoak then provided them with a story that, they hinted, might prove helpful. It told of an army unable to capture a fortress by a single frontal attack but successful when its general divided it into separate bands that attacked from all sides. Having read this and consciously sought an analogy to the X-ray problem, most subjects saw that many sources of weak X-rays placed all around the body and converging on the tumor would solve the problem.90
Expert reasoning: Many cognitive psychologists, intrigued by Newell and Simon’s work, assumed that their theory would apply to problem solving by experts in fields of special knowledge, but found, to their surprise, that it did not. In a knowledge-rich domain, experts do more forward searching than backward searching or means-end analysis, and their thinking often proceeds not step by step but in leaps. Rather than starting with details, they perceive overall relationships; they know which category or principle is involved and work top-down. Novices, in contrast, lack perspective and work bottom-up, starting with details and trying to gather enough data to gain an overview.91
Since the 1980s, a number of cognitive psychologists have been exploring the characteristics of expert reasoning in different fields. They have asked experts in cardiology, commodity trading, law, and many other areas to solve problems; again and again they have found that experts, rather than pursuing a logical, step-by-step search (as a newly trained novice or an artificial intelligence program would do), often leap from a few facts to a correct assessment of the nature of the problem and the probable solution. A cardiologist, for instance, might from only two or three fragments of information correctly diagnose a specific heart disorder, while a newly graduated doctor, presented with the same case, would ask a great many questions and slowly narrow down the range of possibilities. The explanation: Unlike novices, experts have their knowledge organized and arranged in schemas that are full of special shortcuts based on experience.92
Is the Mind a Computer? Is a Computer a Mind?
Even in the first flush of enthusiasm for IP theory and computer simulations of reasoning, some psychologists, of a more humanistic than computer-technical bent, had reservations about the comparability of mind and machine. There are, indeed, major dissimilarities. For one, the computer searches for and retrieves items as needed—at blinding speed, nowadays—but human beings retrieve many items of information without any search: our own name, for instance, and most of the words