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To address the shortcomings of DRC, Perry et al. (2007) developed an alternative model: CDP+ (the Connectionist Dual Process model). The CDP+ model built on the DRC model and included the same route(s) for addressed phonology. As a result, the new model accounted for all effects previously simulated by the addressed route in the DRC model (e.g., the word frequency effect).

The route for assembled phonology is different, however. Instead of using a set of conversion rules, CDP+ includes a neural network to translate graphemes into phonemes (Figure 4.3). A neural network has two or more layers of nodes connected to each other. In CDP+, the input layer consists of graphemes and the output layer of phonemes. The model is trained in such a way that it learns to activate the most likely phonological output when presented with the graphemic input of a monosyllabic English word.

This neural network solves the problems with the assembled phonology route in the DRC model. First, there is no need to define default conversions (set of rules). Instead, the network learns to activate the best fitting output on the basis of the input units activated. It does so by changing the weights of the connections between the units. When there is a consistent correspondence between input patterns and output patterns, the model rapidly catches the correspondence and returns the correct output when given the input. Thus, the neural network rapidly learns to activate the output pronunciation /‐id/ for input words ending in –eed, as all monosyllabic words ending in these letters have the same pronunciation.

Second, when there are inconsistencies in the stimulus set (like the pronunciation of the end letters –ead), the network learns to activate the different pronunciations in line with the input. So, both the phonological forms /‐id/ and /‐εd/ are activated when the model is presented with a visual English word ending–ead. The degree of activation depends on the frequencies of the words pronounced as /‐id/ (read, bead, lead) relative to the frequencies of the words pronounced as /‐εd/ (head, lead, bread, spread).

Figure 4.3 The CDP+ model of visual word naming

(Perry et al., 2007/With permission of American Psychological Association).

The route for addressed phonology is the same as in DRC, but a neural network replaces the rule system in the assembled route.

The CDP+ model has two more advantages over the DRC model. First, it more naturally accounts for the fact that not all people name pseudowords the same (Pritchard et al., 2012). Indeed, not everyone pronounces the pseudowords nead as /nid/, which they should if they follow strict grapheme‐phoneme correspondence rules as in the DRC. Some people pronounce nead as /nεd/, or give different pronunciations on different occasions. Such differences can be understood as individual differences in the learning of the neural network, or in the activation dynamics of the network. Second, the CDP+ model has more scope for interactions between the graphemes and phonemes in a word. Because the conversion occurs in parallel across the entire word, the model may learn that the pronunciation of the vowel in subtle ways depends on the consonants before and/or after the vowel. So, the model may end up naming the pseudoword glive as rhyming with give and the pseudoword brive as rhyming with drive because of the larger overlap between glive and give and brive and drive. Or the model may pronounce glive as rhyming with drive if it has shortly before been presented with the word drive. Similar dynamics are seen in people (Pritchard et al., 2012).