We present administration details and normative data for a new version of the word and nonword reading test originally developed by Castles and Coltheart. The new test contains an expanded set of items, with 40 each of regular words, irregular words and nonwords, rather than the original 30 items of each type. The new items extend the upper-end of the difficulty range of the test, making it less susceptible to ceiling effects than the original version. The test also incorporates a stopping-rule, which makes administration of the test less time-consuming and removes the stress on children who can only read a few items.
Woollams, Lambon Ralph, Plaut, and Patterson reported detailed data on reading in 51 cases of semantic dementia. They simulated some aspects of these data using a connectionist parallel distributed processing (PDP) triangle model of reading. We argue here that a different model of reading, the dual route cascaded (DRC) model of Coltheart, Rastle, Perry, Langdon, and Ziegler (2001), not only provides a more accurate simulation of these aspects of reading in semantic dementia than does the PDP model but also provides highly accurate simulations of other aspects of reading in this disorder that the PDP approach has not simulated. We conclude that our findings add to evidence both from simulations of normal skilled reading and from simulations of other kinds of acquired dyslexia that the nonconnectionist DRC model of reading offers a better account of normal and disordered reading than the connectionist PDP models of reading.
The masked onset priming effect (MOPE) refers to the empirical finding that target naming is faster when the target (SIB) is preceded by a briefly presented masked prime that starts with the same letter/phoneme (suf) than when it does not (mof; Kinoshita, 2000, Experiment 1). The dual-route cascaded (DRC) computational model of reading (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001) has offered an explanation for how the MOPE might occur in humans. However, there has been some empirical discrepancy regarding whether for nonword items the effect is limited to the first-letter/phoneme overlap between primes and targets or whether orthographic/phonological priming effects occur beyond the first letter/phoneme. Experiment 1 tested these two possibilities. The human results, which were successfully simulated by the DRC model, showed priming beyond the first letter/phoneme. Nevertheless, two recent versions of the DRC model made different predictions regarding the nature of these priming effects. Experiment 2 examined whether it is facilitatory, inhibitory, or both, in order to adjudicate between the two versions of the model. The human results showed that primes exert both facilitatory and inhibitory effects.
Rogers, Lambon Ralph, Hodges, and Patterson (2004) studied two-alternative forced-choice visual lexical decision performance in patients with semantic dementia. With item pairs where the target word was more "typical" (i.e., higher in bigram and trigram frequency) than the foil (all foils were pseudohomophones), lexical decision performance was good and was unaffected by word frequency. With item pairs where the target word was less "typical" (i.e., lower in bigram and trigram frequency) than the foil, lexical decision performance was worse and was affected by word frequency, being particularly inaccurate when the word targets were low in frequency. We show (using as materials all the monosyllabic items used by Rogers and colleagues) that the same pattern of results occurs in the lexical decision performance of the DRC (dual-route cascaded) computational model of reading when the model is lesioned by probabilistic deletion of low-frequency words from its orthographic lexicon. We consider that the PDP (parallel distributed processing) computational model of reading used by Woollams, Plaut, Lambon Ralph, and Patterson (2007) to simulate reading in semantic dementia is not capable of simulating this lexical decision result. We take this, in conjunction with previous work on computational modelling of reading aloud in surface dyslexia, phonological dyslexia, and semantic dementia using the DRC and PDP reading models, to indicate that the DRC model does a better job than the PDP model in accounting for what is known about the various forms of acquired dyslexia.
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