What Makes Theory Useful

So far, this discussion has emphasized accuracy and flexibility as the principal bases for model evaluation. We want our theories to predict events that happen and not to predict things that don't; if our theory does so with a reasonable degree of success, then we covet it and attempt to defend it against outside claims of inadequacy.

I want to propose a slight amendment to such a system, however. I believe that models can also be tremendously useful when they fail to provide an account for certain data. Models—particularly well-specified mathematical ones—are useful in part because they are putative isomorphisms for the system under investigation. Consider, for example, the question of how to compare the weights of objects. Masses of objects can only be directly compared with an accurate balance. Yet if I want to know whether this APA-produced tome outweighs other recent books in this domain, I don't need to truck my library over to a chemistry lab to use their balancing scales. Rather, the mass of each object is represented as a real number, and 1 know that the set of ordinal operators in mathematics (including > and <) correspond to "weighing more than" and "weighing less than." To return from this tortured analogy back to the original diatribe, models are useful in part because they provide a different representational system with which to talk about the components of the theory. As discussed early in this chapter, cognitive components are notably vague; grounding a theory in a more formal representational system, such as mathematics, allows us to use the sophistication of that system to derive relationships beyond what our intuitions would have provided us with—even when that formal system is not a fully accurate representation.

One excellent example of how model accuracy and model utility occasionally diverge is provided by the Rescorla-Wagner model of learning (e.g., Rescorla & Wagner, 1972). That theory was itself an attempt to address shortcomings of previous views of associative learning that postulated that contingency of events in time and space was a sufficient (and necessary) precondition for the learning of an association between the events (e.g., Bush & Mosteller, 1951). A number of important results were obtained in the late 1960s that demonstrated the inadequacy of this view by demonstrating conditions in which animals apparently did not learn an association between two stimuli despite highly contingent presentations of the stimuli. One illustrative and fundamental phenomenon is that of blocking, in which an organism first learns that A predicts B and later that the compound AC also predicts B. Blocking is revealed by the fact that the organism does not engage in typical behaviors preparatory for the onset of B when exposed to C alone (Kamin, 1969). The Rescorla-Wagner model explains this result by assuming that an organism learns about the relationships between events only to the degree that outcomes are unpredictable: When an event is expected on the basis of alternative cues (e.g., A predicts B), then nothing is learned about the relationship between additional cues and that outcome (e.g., C and B). Formally, the model can be stated in a reduced form as

in which AAj represents the change in the strength of the learned association between two stimuli on Trial i, ¡3 represents a learning parameter related to the intensity and associability of the two stimuli, X represents an asymptotic learning parameter related to the outcome event, and most importantly, XA( represents the summed associative strength between all available stimuli and the outcome event in question. When this value is close to X, the term inside the parentheses approaches 0; thus learning is weak or nil.

It would be no exaggeration to state that this model has been the single most influential theory of learning since its publication. It has been imported into (or coevolved with) many other domains, including human contingency learning and causality judgments (e.g., Chapman & Robbins, 1990; cf. Cheng, 1997) and artificial learning in neural networks (as the influential delta rule; Rumelhart, Hinton, & Williams, 1986; Widrow & Hoff, 1960). It can account for a huge number of basic phenomena in associative learning (Dickenson & Macintosh, 1978; Miller, Barnet, & Grahame, 1995; Walkenbach & Haddid, 1980) and consequently has been the primary vehicle for the discussion of phenomena in animal learning in introductory textbooks.

These successes notwithstanding, there are numerous examples of how the model fails to account for behavior in the very paradigms it was designed for. To draw again on the example of blocking, as described earlier, remember that the model explains blocking as a deficit in learning— the animal fails to respond to the blocked stimulus because nothing was learned about the relationship of that stimulus to the outcome. Certain phenomena indicate that this assumption is almost certainly false. For example, additional training following the traditional blocking procedure that presents the blocking stimulus (A, in the preceding example) paired with the absence of the outcome stimulus (C) can lead to retroactive unblocking, in which responding increases to the B stimulus, even though there were no additional presentations of that B stimulus (Arcediano, Escobar, & Matute, 2001; Blaisdell, Gunther, & Miller, 1999).

From a model-evaluation perspective, such data should lead us to cast out the Rescorla-Wagner Model as outdated and unsatisfactory. However, this approach ignores critical aspects of the scientific process; namely, the discovery of phenomena like retroactive unblocking was motivated in large part by the strong (and ultimately incorrect) predictions of the model. In other words, widespread understanding of the model led researchers to devise paradigms that tested its limits. In addition, certain generalities among the phenomena that contradict the model are only apparent in context of how the model deals with them inadequately (Miller et al., 1995). Thus we see that models serve not only as isomorphisms for the systems we study, but also as motivating and organizational tools that enhance our progress toward understanding the mechanisms they purport to represent—even when they do so incorrectly. This approach to model-based psychological science is well reflected in the quip that models should be your friends, not your lovers (Dell, 2004). You maintain them because of what they offer you, but you keep many of them and don't demand too much of any single one.

Was this article helpful?

0 0

Post a comment