Analogical transfer from interaction with a simulated physical system

Analogical Transfer from Interaction with a Simulated Physical System Robert L. Goldstone (rgoldsto@indiana.edu) Samuel B. Day (day9@indiana.edu) Department of Psychology, 1101 East 10 th Street Bloomington, IN 47405 USA Abstract In two studies, we find that participants are able to transfer strategies learned while interacting with a simulated physical system to a dissimilar and less perceptually-concrete domain. Interestingly, performance on the transfer task was completely unrelated to explicit knowledge of the structural correspondences between the systems. We suggest that direct interaction with a concrete system may lead to a kind of procedural knowledge that provides a good basis for analogical transfer. Introduction There is no question that analogical reasoning is a powerful tool for learning. It allows us to look past the simple surface details of a situation, and to focus instead on underlying structure—how the components of a system fit together and relate to one another. In so doing, it allows us to make structurally-sound inferences about new situations, and provides the opportunity to draw on our wealth of existing knowledge. These processes may occur in any kind of situation. Practice problems from mathematics and physics classes are often solved by seeking out prior examples that share the same principles, even if the specific objects and situations described are concretely very different. On a larger scale, there are many stories of important scientific progress relying on apt analogies, such as Rutherford’s model of the atom developing from an analogy with the structure of the solar system. However, research has repeatedly shown that people have a very difficult time taking advantage of this tool. Unless the structural commonalities are somehow pointed out to them, people generally fail to notice that two situations from different domains are analogous. For example, Gick & Holyoak (1980, 1983) provided participants with a concrete example of a problem being solved with a convergence strategy, in which several small forces converged at a single location, and summed to produce a large effect. When the participants were subsequently asked to solve an analogous problem from a different domain, however, they were very unlikely to spontaneously recognize the relevance of the prior example, and therefore failed to transfer the solution strategy. The problem was not with the soundness of the analogy itself—when given a hint to think about the prior example, participants were quite good at making use of the relevant strategy. Rather, the issue seemed to be their ability to spontaneously see the connection between the episodes. This general pattern has been shown repeatedly across a wide range of materials. What underlies this difficulty? One factor that is often cited is the concrete content of the episodes themselves. Although the terminology may vary somewhat, most research on analogical transfer has distinguished between the “deep,” abstract, structural aspects of an episode, which are directly relevant for transfer, and the superficial “surface” content, which includes the concrete, domain- specific details of a particular example. For instance, in Rutherford’s model of the atom, the abstract structure of multiple entities that revolve around a more massive core is relevant for analogical mapping, while details such as the color and the temperature of the sun are considered irrelevant “surface” features. One way in which concrete information might impair analogical transfer is simply through competition with the relevant abstract structure. A consistent finding in the literature is that people are very likely to be reminded of a prior episode if it shares concrete features with a current situation, while remindings solely due to shared abstract structure are much more rare (e.g., Gentner, Rattermann & Forbus, 1993; Ross, 1984). Furthermore, even when an appropriate analog has been retrieved from memory, its application to a current problem can be impaired if the concrete features of the entities involved mismatch. For instance, Ross (1987, 1989) reported that superficial similarity between objects in two mathematical problems could reduce transfer performance if those objects played different roles in the two problems. There is also evidence suggesting that reducing concreteness may facilitate abstract understanding and improve reminding and transfer, at least in some situations. For example, Clement, Mawby & Giles (1994) found that analogical retrieval was improved substantially when the situations were described with very abstract, domain-general terms, rather than more concrete and specific terminology. Goldstone & Sakamoto (2003) found that for participants who performed more poorly in general, the use of a less concrete training task significantly increased transfer. And one of the most robust methods for improving analogical transfer—asking participants to compare two potential analogs before solving a new problem—is presumed to succeed due to the creation of a more abstract, less concrete representation of their common structure (e.g., Gick & Holyoak, 1983; Gentner, Loewenstein & Thompson, 2003). Together, these findings seem to suggest that concrete information represents a clear impediment to transfer between dissimilar situations, largely by overshadowing the relevant abstract structures. On the other hand, many researchers have suggested an important relationship between low-level perceptual processes and high-level, abstract representations. For example, Goldstone & Barsalou (1998) argue that most of our abstract conceptual abilities are ultimately grounded in perceptual processes (see Barsalou, 1999, for a more extreme version of this view). Others have suggested that many specific abstract