The Science of Reading. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ al., 2020).

The hybrid models are also being used to study reading acquisition. The similarities we have noted between these models and the PDP‐connectionist models also carry over to acquisition. Perry et al. (2019) also based their account on Share’s “self‐teaching” idea: when a word is correctly read aloud, its accuracy can be determined by matching to an entry in the phonological lexicon. If the word is mispronounced, the correct pronunciation must be provided from some other source for learning to occur. How pronouncing nonwords could yield learning in this framework requires additional assumptions. Identifying the sources of the error signals that drive learning in a realistic manner is an important issue that merits additional research. Progress might be accelerated by incorporating research in two areas.

      One is studies of machine learning algorithms for neural networks. Share’s self‐teaching hypothesis was an important, original contribution, and for many years the only mechanistic account of learning to read at least some words. The mechanism he described can be understood as an example of learning via a forward model (Plaut & Kello, 1999). Whether a word has been pronounced correctly can be determined by attempting to comprehend it as spoken language (i.e., via phonology➔semantics). If the pronunciation is correct, the semantic pattern that is computed on this reverse pass should match the meaning of the word that was pronounced. A discrepancy between the two yields an error signal that can be used to adjust the weights. Processing one’s own language production on a backward pass through the speech comprehension system is used in contexts ranging from infant babbling (Werker & Tees, 1999) to making grammaticality judgments (Allen & Seidenberg, 1999). Machine learning researchers have also studied “semi‐supervised” learning algorithms in which the specificity of the feedback can vary across learning trials (Gibson et al., 2013). This is getting much closer to the varied conditions under which children learn, which include explicit correction (full feedback), partial “cues” about pronunciation, broad indications that a pronunciation was correct or incorrect, and children learning from their own output, correct or not.

      The second relevant area of research is on the brain bases of learning. Humans engage in at least two types of learning: explicit and implicit, also known as declarative and procedural (Ellis, 2005; Kumaran et al., 2016), which are subserved by subcortical and cortical neural structures, respectively. The explicit system is associated with conscious awareness and intention, and knowledge that can be described using language, such as the rules for chess. It is slow and effortful (cf. Kahneman’s System 2 thinking, a related notion; Kahneman, 2011). The implicit system operates without conscious awareness, occurs automatically rather than by intent, and involves unlabeled statistical patterns (cf. System 1 thinking; Kahneman, 2011). Traditional instruction is explicit, as in teaching explicit rules for pronouncing or spelling words. Aside from the lack of agreement about the rules, these mappings are too complex to be wholly taught this way. Although learners benefit from explicit instruction (e.g., Foorman et al., 1991), most of this knowledge is acquired via implicit learning. Excessive emphasis on explicit instruction may make acquiring this material more difficult. Finding the balance between explicit and implicit learning experiences that is most effective is an important challenge for future research. Incorporating both learning systems in computational models of reading seems an obvious and necessary next step, with potentially important implications for education.

Conclusions

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.

Acknowledgments

      This work was supported by The Vilas Trust and Deinlein Language and Literacy Fund (University of Wisconsin‐Madison). We have greatly benefited from discussions of these issues with many people over the years, especially David Plaut, Anna Woollams, Karalyn Patterson, Jay McClelland, Matt Lambon Ralph, and Maryellen MacDonald. Details concerning the modeling results mentioned in the article are available on the Open Science Framework website, https://osf.io/ay7h6/.

References

      1 Allen, J., & Seidenberg, M.S. (1999). Grammaticality judgment and aphasia: A connectionist account. In B. MacWhinney (Ed.), The Emergence of Language (pp. 115–151). Erlbaum.Anderson, J. R. (1983). The architecture of cognition. Lawrence Erlbaum Associates, Inc.

      2 Andrews, S., & Scarratt, D. R. (1998). Rule and analogy mechanisms in reading nonwords: Hough dou peapel rede gnew wirds? Journal of Experimental Psychology: Human Perception and Performance, 24(4), 1052–1086. doi: 10.1037/0096‐1523.24.4.1052.

      3 Backman, J., Bruck, M., Hebert, M., & Seidenberg, M. S. (1984). Acquisition and use of spelling‐sound information in reading. Journal of Experimental СКАЧАТЬ