So far, we have discussed empirical findings indicating that phonology is involved in visual word recognition and reading. Psycholinguistic research is more than documenting empirical effects, however. Researchers want to integrate the findings in a coherent theory.
Computational models are the most informative to understand the mechanisms involved in word processing as implementation requires a detailed description of the operations involved (see Seidenberg et al., this volume). This is in contrast to verbal models which often resort to analogies with human intuition (Ward, 1998).
We limit our discussion to three computational models that include phonology. There are other models of visual word processing without assembled phonology, not discussed here (see Davis, 2010; Norris & Kinoshita, 2012; Whitney, 2001). So far, no computational model has been proposed for the strong phonological theory. The models we discuss are limited to English, and to the processing of monosyllabic words (some 6 000 in total).
The Dual Route Cascaded (DRC) model
The DRC model of visual word naming (Coltheart et al., 2001) was the culmination of decades of behavioral experiments with skilled readers and detailed analysis of reading in patients with various types of acquired dyslexia following brain damage (see Woollams et al., this volume). The model is summarized in Figure 4.2. It assumes two main routes for naming printed words (actually three, as we will see in the following text). The first route is based on addressed phonology; the second, on assembled phonology. Both routes start from an orthographic analysis stage in which the visual stimulus is translated into abstract letter identities. This means that the input is independent of the letter font used. Evidence for this stage comes from the observation that changes in font do not impact the magnitude of priming in masked priming: The target word television is processed equally fast after the primes television, TELEVISION, or TeLeViSiOn (Brysbaert et al., 2009; Forster, 1998; Rayner et al., 1980; see Grainger, this volume).
Figure 4.2 The DRC model of visual word naming
(Coltheart et al. 2001/With permission of American Psychological Association)
includes separate routes for addressed phonology (left part) and assembled phonology (right part).
The addressed phonology route within the DRC model assumes that the abstract letter identities activate entries in an orthographic input lexicon. A lexicon is a dictionary of known word forms. In many models of word processing it is separate from word meanings (called the semantic system) because the mappings between words and meanings are not one‐to‐one (e.g., synonyms refer to similar meanings although they have different forms; spoken and written words have different forms for the same meaning, and multilinguals know even more word forms referring to the same meaning).
The orthographic input lexicon in the DRC model contains word nodes that receive activation when the orthographic input includes overlapping letter identities at the right place. So, the orthographic input work activates all orthographic word nodes with the letter w at the beginning (want, went, why, work,…), all word nodes with the letter o in second position (soak, body, work,…), all word nodes with the letter r in third position (air, germs, work,…), and all nodes with the letter k in fourth position (bank, drake, work, …). There is competition between the activated word nodes until one wins. Usually this is the one corresponding to the input, as it receives the most activation.
Importantly, the word nodes in the orthographic input lexicon interact with corresponding word nodes in the phonological output lexicon (the mental dictionary of spoken word forms). As a result, the phonological node of the word is activated in parallel with the orthographic node. The lexicon‐mediated route of the DRC model makes it possible to name the words the model knows correctly, regardless of their orthographic transparency.
The authors of the DRC model argued for the existence of direct interactions between the orthographic input lexicon and the phonological output lexicon. This followed the observation that some patients with advanced dementia sometimes remain able to name words with irregular grapheme‐phoneme correspondences correctly, even though they no longer understand the words (e.g., Blazely et al., 2005). So, semantic involvement does not seem to be needed for addressed phonology to operate. At the same time, some patients with acquired deep dyslexia produce synonym errors in reading, saying tree to the target word bush (Coltheart et al., 1987). Because synonyms have no form overlap, the DRC maintained an extra route between the orthographic input lexicon and the phonological output lexicon via the semantic system. Word nodes in the orthographic lexicon activate meanings in the semantic system, which in turn activate word nodes in the phonological output lexicon. In the absence of direct interactions between the orthographic and phonological lexicons, this will result in synonym naming errors (but see Seidenberg et al., this volume, for an alternative account).
The second route of the DRC model implements assembled phonology. This is done by means of grapheme‐phoneme conversion rules. The abstract letter identities are translated one by one into phonemes. The phonemes are sent to a response buffer, which makes it possible for the model to name new words or pseudowords (e.g., briss, gert, tuise). Grapheme‐phoneme conversion is governed by a set of rules (e.g., b ‐> /b/, ea ‐> /i/). In case of multiletter graphemes, a wrong phoneme may be initially activated to some extent, which has to be corrected afterward (e.g., the letters ph will first activate /p/ before they activate /f/). This involves a time cost, an effect that can be seen in pseudoword naming latencies. Phonemes activated in the response buffer also activate word nodes in the phonological lexicon in a way similar to the activation of word nodes in the orthographic lexicon by orthographic letter identities. This is particularly interesting for regular words because the activation from the phonological lexicon is in line with the activation from the visual input.
In the DRC model, addressed phonology is activated more rapidly than assembled phonology, and therefore dominates the response. Inconsistencies between phonemes in the response buffer activated by the addressed route and the assembled route typically result in a time cost to name the word but not in a naming error. Addressed phonology is particularly fast for high‐frequency words because these word nodes are easier to activate in the lexicon. In contrast, the time needed for assembled phonology depends on the length of the letter string: longer words and the presence of multilettered graphemes with naming confusions (cf. the pronunciation of ph) result in slower responses.
The DRC model simulates many effects observed in word naming (Coltheart et al., 2001; but see Seidenberg et al., this volume, for a critique). However, there are three issues with the assembled phonology route. First, there is an element of arbitrariness in the grapheme‐phoneme conversion rules used, as indicated by Glushko (1979). Second, the model cannot account for the finding that graphemes with ambiguous pronunciations activate more than one phoneme. So, the model cannot explain why it takes longer to name the regular word wave than wade (Glushko, 1979), or why the word bead can be misread as bed (Lesch & Pollatsek, 1993). The DRC model cannot explain either why bilinguals activate phonology in both their languages when reading words in one language.
The CDP+ model
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