Читать книгу The Science of Reading - Группа авторов - Страница 52

Computational modeling is a powerful tool for studying reading and other complex behaviors. Modeling has addressed two major concerns about earlier box‐and‐arrow models of reading and other phenomena. First, it was unclear whether informally stated verbal models would work in the intended ways. Discussing schematic diagrams of the dual‐route model, Seidenberg (1988) observed:

My own view is that these [models] can only be sustained because explicit representations and processing mechanisms are not provided. Conversely, providing this information would yield a very different functional architecture than the received view.

Implementations of dual‐route models validated these concerns: providing the necessary computational details revealed the limitations of the approach. Recognition of these limitations (initially by Rumelhart & McClelland, 1986, in the context of learning the past tense; later by Seidenberg & McClelland, 1989) led to the development of connectionist models with a very different character.

The second concern was the post‐hoc character of box‐and‐arrow modeling. As new behavioral phenomena were discovered, components were added or adjusted to fit the data. Case reports of patients with highly selective impairments were considered especially informative (Patterson & Lambon Ralph, 1999). The framework was unconstrained and could be modified in numerous ways. Elaborations of these informal models increased the number of phenomena that could be accommodated, but the explanations were shallow because they were devised to fit the data rather than independently justified (see Seidenberg, 1988; Seidenberg & Plaut, 2006).

Looking at the research we have reviewed, it becomes apparent that the DRC model and later models it inspired are computational versions of the box‐and‐arrow approach. As before, the goal is to account for as many phenomena as possible. The “gold standard” studies function like the brain‐injured patients with highly selective impairments. The parameter settings that allowed an effect to be reproduced had no independent justification, nor do the detailed assumptions about the sequence of events in generating a pronunciation or making a lexical decision. The goal of the connectionist approach, in contrast, was to identify general properties of knowledge representation, learning, and processing which, when applied in domains such as reading aloud or generating the past tense, would yield behavior that aligned well with people’s. The properties that were identified have turned out to have lasting value, yielding novel insights about old questions and opening new ones for scientific study.

At the outset we observed that “Visual word recognition is one of the great success stories in modern cognitive science and neuroscience.” Is the claim valid? All of the models we have discussed address a limited range of reading phenomena. The goal of implementing models that simulate the results of individual studies may well have been over‐ambitious; behavior is affected by factors outside the scope of such models (e.g., measurement and sampling error). Many issues, such as the roles of different types of learning, remain to be addressed. Pursuit of the two types of models has nonetheless deepened understanding of many aspects of reading, including ones discussed elsewhere in this volume. The high level of convergence between theories that has occurred is itself indicative of progress in this vigorous area of research.