The Science of Reading. Группа авторов
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Название: The Science of Reading

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Языкознание

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isbn: 9781119705130

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СКАЧАТЬ and exception words are processed equally rapidly by the lexical route, but for lower frequency exception words, the outputs of the two routes conflict, yielding longer latencies than for regular words.

      The DRC model also erroneously produces regularity effects for both high‐ and low‐frequency words when tested on materials from other studies (e.g., Taraban & McClelland, 1987; Seidenberg et al., 1984; Seidenberg, 1985). Simulations of these studies were not reported, however. Including results for the simulation of one “benchmark” study said to produce a desired outcome but not the missimulations of other studies of the same phenomena is similar to the “file‐drawer problem” in other types of research.

       Consistency effects

      In the dual‐route approach, words are either regular (rule‐governed) or exceptions (rule‐violating). In connectionist models, words differ in their degree of spelling‐sound consistency. This is a fundamental difference between the theories and it is important to establish which claim about the English writing system is correct.

      The theoretically critical comparisons were identified in Glushko’s original study. In the dual‐route theory, wade is regular and have is an exception. What is wave? In the dual‐route approach it is rule‐governed, allowing generalization to nonwords such as mave. However, wave has a close, high‐frequency, irregular neighbor have. ‐ave is therefore less consistent than ‐ade. Glushko found that, for skilled college‐student readers, “regular but inconsistent” words such as wave yielded longer naming latencies than regular and consistent words such as wade. This is a crucial finding. Both types of words are regular/rule‐governed according to DRC and should therefore behave alike, but they do not. Accounting for the unexpected impact of words such as have on rule‐governed words such as wave therefore presented a significant challenge. Later studies showed that consistency effects are modulated by word frequency and reading skill: for younger and weaker readers they occur for higher frequency words such as gave, but with increases in reading skill, the effects are limited to less common words (Backman et al., 1984; Seidenberg, 1985; Waters et al., 1984). Consistency effects also occur for nonwords (Glushko, 1979). The SM89 model accurately simulated several studies of consistency effects, including the effects of frequency and reading skill, and nonword consistency. The effects arise because the weights on connections in the network reflect the aggregate impact of exposure to words. Learning about have shifts the weights away from values that are optimal for pronouncing wave. That yields slower pronunciation times than for words and nonwords with highly consistent spelling patterns such as wade and nade. The effects are graded, varying in degree, rather than categorical, as in the rule‐governed versus exception distinction.

      Coltheart et al. (2001) counted consistency effects as one of the phenomena the DRC model correctly simulated, based on the results for a “benchmark” study by Jared (1997) that manipulated frequency and consistency factorially. A consistency effect was found for both the high‐ and low‐frequency words (the atypical effect for “higher frequency” words was because they were moderate in frequency with higher frequency enemies). The DRC model reproduced these results, as did SM89. Coltheart et al. concluded:

      [We] have shown that the DRC model can simulate the Jared (1997) results and have discovered why; hence we have shown that there is no conflict between her results and the DRC model. Even more generally, for these reasons we believe that the body of experiments showing effects of consistency on reading aloud are compatible with the DRC model despite the fact that this model contains no level of representation specific to orthographic bodies. (p. 233)

      According to Coltheart et al., the “consistency” effect in the Jared study was due to other properties of the stimuli. In their view, the inconsistent words were a mix of regular and exception words. For example, Jared categorized doll as inconsistent because of the alternative pronunciations of –oll (as in poll and doll). DRC 2001 treats doll as an exception. A set of “inconsistent” words containing a significant proportion of exceptions will yield poorer performance than a set of entirely regular words. The “inconsistency effect” is therefore seen as an artifact of averaging the latencies for regular and exception words used in this condition.

      If this account is correct, the consistency effect should be eliminated in both human and DRC data if exception words are excluded because the remaining words are regular (according to DRC). If the effect is due to consistency, it should still be obtained because the remaining words are inconsistent (according to the connectionist account). Seidenberg and Plaut (2006) tested this hypothesis by recomputing the condition means in Jared’s (1997) study, removing words that are considered exceptions in DRC and their matched control words (9 pairs of stimuli). Removing the exceptions eliminates the consistency effect in the DRC simulation; in the behavioral data, however, a statistically reliable consistency effect remains. Thus, Coltheart et al.’s analysis is a correct account of consistency effects in their model but not in people.

      Recognizing the importance of consistency effects in evaluating models of word reading, Coltheart et al. (2001) observed:

      [One] outcome that would refute the model would be to find that when body consistency, as defined by Glushko (1979), is taken into account, regularity has no effect on reading aloud. (p. 247)

      Following this reasoning, Jared’s (2002) study refutes the DRC. More importantly, this is one of many experiments documenting the theoretically decisive consistency effect and DRC’s СКАЧАТЬ