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Cracking the orthographic code: An introduction Jonathan Grainger a a CNRS & Aix-Marseille University, Marseille, France

First Published on: 04 September 2007 To cite this Article: Grainger, Jonathan (2007) 'Cracking the orthographic code: An introduction', Language and Cognitive Processes, 23:1, 1 - 35 To link to this article: DOI: 10.1080/01690960701578013 URL: http://dx.doi.org/10.1080/01690960701578013

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LANGUAGE AND COGNITIVE PROCESSES 2008, 23 (1), 135

Cracking the orthographic code: An introduction Jonathan Grainger CNRS & Aix-Marseille University, Marseille, France

In this introduction to the special issue, I will first briefly summarise past research on orthographic processing, describing some of the central areas of empirical investigation and the major theories guiding that work. Next, I will describe the more recent lines of empirical and theoretical research that have emerged over the last decade or so, and that serve as the focus of the current special issue. I will attempt to summarise the key evidence that has emerged from this research, and examine how this evidence can guide model selection. Finally, I will discuss some possible avenues for future research on orthographic processing in the hope of finally ‘cracking the orthographic code’.

Under typical reading conditions, our eyes fixate the majority of words in the text, and the visual information highlighted by such fixations then enables subsequent orthographic, phonological, and semantic processing. Individual words are undeniably the building blocks of the reading process, and a printed word is primarily an orthographic object a set of individual letters in languages that use alphabetical orthographies. Therefore, there is a large consensus today that the constituent letters of individual words represent the first ‘language-specific’ stage of the reading process following the work done by oculomotor control mechanisms (enabling fixation on the word) and early visual processing (enabling visual feature extraction). The term ‘orthographic processing’ minimally refers to the processing of the identities and positions of the constituent letters of a word. Correspondence should be addressed to Jonathan Grainger, Laboratoire de Psychologie Cognitive, Universite´ de Provence, 3 pl. Victor Hugo, 13331 Marseille, France. E-mail: [email protected] Many thanks to Kathy Rastle as the instigator of this special issue, and to Lolly Tyler for her support. Thanks to all the authors of papers in this issue for having responded to my call, and to have (almost) respected the deadlines. This introductory article benefited from constructive criticism from Marc Brysbaert, Colin Davis, Ken Forster, Steve Lupker, Manolo Perea, Carol Whitney, and one anonymous reviewer. # 2007 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business http://www.psypress.com/lcp

DOI: 10.1080/01690960701578013


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In spite of the consensus concerning the role of individual letters in visual word recognition, there has been surprising little research directly aimed at elucidating the nature of orthographic representations and their role in perceiving printed words. One reason for this might be that McClelland and Rumelhart’s (1981) interactive-activation model provided, for a significant number of researchers in the field, a response to many of the questions concerning orthographic processing that had been guiding research in the two decades prior to that publication. Visual word recognition research in the 1980s therefore became more focused on issues of phonological, morphological, and semantic processing. In order to understand this situation first some history.

HISTORICAL BACKGROUND This brief historical overview of research on orthographic processing is by no means intended to be exhaustive. After all, the first experiments on orthographic processing were published over 100 years ago. I have opted to describe a subset of this research that has, in my opinion, had the strongest influence on current approaches to understanding orthographic processes in reading. Furthermore, little reference is made to the large number of studies examining the process of reading aloud.

The word superiority effect One of the central questions guiding early research on visual word recognition was to what extent the recognition process proceeds via the constituent letters of the word. Are printed words recognised via their constituent letters? Cattell’s (1886) work on the ‘word superiority effect’ appeared to impose a negative response to that question. Indeed, Cattell had demonstrated that with identical (brief) tachistoscopic exposures, real words (e.g., silence) were identified more accurately than random combinations of their constituent letters (e.g., lesinec). Reicher (1969) and Wheeler (1970) provided an improved methodology to answer that same question. These authors demonstrated that even when participants only have to identity a single letter, performance is superior with real words compared to nonsense strings of letters. So, how can a word be recognised via its constituent letters if these letters are harder to identify than the word itself ? This conclusion led to the ‘word shape’ hypothesis, according to which some form of supra-letter or holistic information about a printed word (e.g., the word envelope generated by a specific combination of ascending, descending, and neutral letters; Bouma, 1973) is used to identify the word. This solution ignores two major problems: one empirical, the other computational. First, at an empirical level, a word shape theory of word


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recognition cannot account for the standard finding that there is no observable advantage for reading lower case compared with upper case text (e.g., Paap, Newsome, & Noel, 1984; but see Perea & Rosa, 2002, for a recent exception). Second, from a computational point of view, the word shape theory has to resolve the problem of shape invariance for each word possibly with multiple representations of words in lower case, upper case, and ‘weirdo’ font (see Chauncey, Holcomb, & Grainger, 2008 this issue). Given that the average skilled reader knows somewhere between 30,000 and 50,000 words, the computation involved in solving shape invariance for each word is going to be a lot more costly than solving it for each letter of the alphabet.

The Interactive-Activation model (IAM) With their two papers published in 1981 and 1982 in Psychological Review, Jay McClelland and Dave Rumelhart put an end to one century of debate on this issue (McClelland & Rumelhart, 1981; Rumelhart & McClelland, 1982). There are two important ingredients in the IAM, and either one of them alone can account for the word superiority effect. These are cascaded processing and interactive processing. Cascaded processing can account for the word superiority effect by having activation build up faster in word representations than letter representations, even if the latter are the first to receive any bottom-up activation input. This can occur due to the greater amount of activation input at the word level through the convergence of inputs that are separated at the letter level. Grainger and Jacob’s (1994) dual read-out model (DROM) provided an account of the word superiority effect within the framework of a noninteractive version of the IAM. Superior identification of letters in words compared with letters in pseudowords is due to identification of the word enabling correct identification of its constituent letters via read-out from a whole-word orthographic representation in long-term memory. According to this non-interactive account of the word superiority effect, the pseudoword superiority effect (superior identification of letters in orthographically regular, pronounceable nonwords compared with irregular unpronounceable nonwords) is due to misperceiving the pseudoword as a real word (see Grainger & Jacobs, 2005, for a detailed discussion of this possibility). McClelland and Rumelhart’s preferred interpretation of the word superiority effect was, however, couched in terms of interactive processing topdown feedback from word representations to letter representations. In this way, letters presented in a word stimulus benefit from an additional activation input (top-down word-letter activation) compared with letters presented in a nonword context. Similarly, pseudoword stimuli partially activate the representations of similar real words, which in turn send topdown feedback to the letters shared with the pseudoword stimulus. Grainger


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and Jacobs (2005) describe some experiments designed to test the relative contributions of top-down processing and word misperception to the pseudoword superiority effect. McClelland and Rumelhart might not have provided a complete account of the word superiority effect and the pseudoword superiority effect (see Estes & Brunn, 1987; Grainger & Jacobs, 1994, 2005; Hooper & Paap, 1997; Jordan & Bevan, 1996; Paap, Johansen, Chun, & Vonnahme, 2000, for further discussions on various issues), but they did provide an extremely powerful theoretical framework for understanding visual word recognition in particular and familiar object recognition in general. Although McClelland and Rumelhart only applied the IAM to data obtained with the Reicher Wheeler task, the model was adopted by others as a generic theoretical framework for visual word recognition. The enduring success of this approach can be seen in Coltheart, Rastle, Perry, Langdon, and Ziegler’s (2001) DRC model, that uses the IAM as the direct, orthographic route (see also Perry, Ziegler, & Zorzi, 2007), and in Grainger and Jacobs’ (1996) application of the model to the lexical decision task. At the time of writing this paper, McClelland and Rumelhart (1981) had over 1600 referenced citations.

Beyond Interactive-Activation Despite its success, certain limitations of McClelland and Rumelhart’s model soon became obvious after its publication. However, the limitation that attracted most attention the model’s inability to learn or to adapt led research away from the study of basic mechanisms underlying orthographic processing. Instead, the vast majority of models appearing in the late 1980s and 90s focused on the issue of how irregularities in language, such as irregularities in the mapping of spelling-to-sound, are learned during the process of language acquisition (e.g., Plaut, McClelland, Seidenberg, & Patterson, 1996; Seidenberg & McClelland, 1989). This gave rise to a clear dominance of research investigating the process of reading aloud using the naming task, while empirical work on silent reading focused more on the role of phonology, morphology, and semantics (see Frost, 1998; Frost, Grainger, & Rastle, 2005, for reviews). Research on orthographic processing continued during that period, with a rise in interest on how orthographic similarities between words affects the word recognition process. As an aside, it is interesting to note that the definition proposed by Coltheart, Davelaar, Jonasson, and Besner (1977) of what constitutes maximal orthographic similarity (excluding the identity condition, i.e., an orthographic neighbour) and widely adopted since, is exactly that of the IAM. In 1989, two papers investigating effects of orthographic neighbourhood were published. Andrews (1989) reported



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facilitatory effects of number of orthographic neighbours (N) in lowfrequency words. Grainger, O’Regan, Jacobs, and Segui (1989) reported inhibitory effects related to the presence of at least one high frequency word in the orthographic neighbourhood. Since these publications there have been several reports of null effects, and even opposite effects obtained with similar manipulations (e.g., Forster & Shen, 1996; Sears, Hino, & Lupker, 1995). There have been several attempts to reconcile the contradictory patterns (e.g., Andrews, 1997; Grainger & Jacobs, 1996; Pollatsek, Perea, & Binder, 1999; Ziegler & Perry, 1998), but one might be tempted to conclude that there has been a failure to reach a consensus on how a word’s orthographic neighbourhood affects its recognition. Some of the empirical discrepancies surrounding effects of orthographic neighbourhood might be due to the operational definition of orthographic neighbour applied in this research (Coltheart et al.’s, 1977, N metric).1 Grainger and Segui (1990) provided some informal evidence that this might be the case. Using the progressive demasking paradigm (see Dufau, Stevens, & Grainger, in press), Grainger and Segui reported identification errors that were not standard orthographic neighbours of target words, and notably differed in length (e.g., target ‘vote’, response ‘votre’). More recently, Davis and Taft (2005) found that nonwords formed by adding a letter to a real word (e.g., ‘scome’ from ‘come’) were harder to reject in a lexical decision task compared with control nonwords, and responses were slower and less accurate to real words that formed a high-frequency word when one letter was removed (e.g., drown-down). Therefore, it was gradually becoming clear that a good understanding of how orthographic similarity between words can influence the recognition process must be based on a good understanding of how orthographic information is processed in the first place (orthographic input coding). The solution proposed in the IAM and in Coltheart et al.’s N metric, although a good first approximation, was clearly only a first approximation (see Perry, Lupker, & Davis, 2008 this issue, for a more detailed discussion). For the IAM, the major culprit appeared to be the rigid slot-based letter position coding scheme that was used in the model (see the section on models of letter position coding below). Further evidence in this direction was obtained from the study of illusory word perception via letter migrations (e.g., McClelland & Mozer, 1986). In this paradigm, two words are briefly presented next to each other (e.g., SAND-LANE) and participants are asked to report the identity of the two words. With appropriate stimuli, errors (e.g., LAND-SANE) can be induced via letter migrations. Davis and Bowers 1

There is also evidence that some of these discrepancies might be due to the influence of phonological neighbourhoods and the interaction between orthographic and phonological neighborhood (Grainger, Muneaux, Farioli, & Ziegler, 2005; Yates, Locker, & Simpson, 2004).


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(2004) showed that illusory identifications generated by letter migration do not always respect position. Given a stimulus pair like STEP-SOAP, participants also made errors of the type STOP where the O at position two has migrated to position three. The present special issue describes the rapidly developing empirical evidence in favour of a more flexible letter position coding mechanism, and describes several proposals for such a mechanism. Finally, empirical research performed prior to and posterior to publication of the IAM had clearly suggested that the letter and word representations of this model are likely not to be the only functional orthographic unit for word recognition. I now turn to briefly examine this particular line of research.

In search of the access code Much of this line of research was motivated by a theoretical approach that is radically different from interactive-activation. The foundations of this approach were described in Forster’s (1976) serial search model of visual word recognition. The guiding principle is that the rapidity and efficiency of word recognition results from the lexical processor only ever examining a small part of the total number of lexical representations stored in long-term memory. Lexical memory is divided into domains (orthography, phonology, and semantics), and each domain is organised into ‘bins’, each of which has a unique access code. Understanding word recognition was therefore reduced to specifying the nature of this access code, the code that allows the lexical processor to direct incoming orthographic information to the appropriate bin. It should be noted that most of the research associated with this particular theoretical stance investigated the recognition of relatively long, polysyllabic, often polymorphemic words, compared with the short, monosyllabic, simplex words that were the object of much of the research discussed above. Much of the empirical research engaged in this direction used one of three types of manipulation: Priming (masked and unmasked) where the target word is preceded by the hypothetical access code or not; Divided Stimulus where the target word is artificially divided into sub-structures that either respect or do not respect the hypothetical access code, either by a change in case (tarGET), by introducing a space (tar get), or by colour (‘tar’ in red ink, ‘get’ in blue ink); Frequency where the frequency of occurrence of a specific sublexical structure is manipulated, and the frequency of alternative structures held constant. Search for the access code using these techniques resulted in several proposals. One prominent example is Taft’s (1979) basic orthographic syllabic structure (BOSS). Taft defined the BOSS as all letters forming the first syllable of a word’s stem (once all prefixes are removed) plus



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all following consonants that can be added without violating orthotactic rules (i.e., the end letters must form a legal sequence of letters for a word ending the BOSS of ‘uncle’ is ‘unc’ since ‘ncl’ cannot occur at the end of a word in English). However, research examining the role of the BOSS in visual word recognition has failed to unambiguously designate this as a critical functional unit over and above effects of phonological syllables, effects of morphological structure, or effects of letter cluster frequency (e.g., Lima & Pollatsek, 1983; Seidenberg, 1987). The role of phonologically defined syllables has been investigated by manipulating the frequency of a word’s component syllables (Carreiras, Alvarez, & De Vega, 1993). An inhibitory effect of syllable frequency (analogous to the inhibitory effects of neighbourhood frequency reported by Grainger, 1990; Grainger et al., 1989) was reported by Perea and Carreiras (1998). Words with a frequently occurring initial syllable generated longer response latencies than words with a low-frequency initial syllable. Recent research in French has shown that these syllable frequency effects are indeed driven by phonologically and not orthographically defined syllables, and do reflect syllable frequency and not segmental frequency (Conrad, Grainger, & Jacobs, in press). An influence of syllable structure has also been found using the illusory conjunction paradigm an extension of the divided stimulus paradigm mentioned above. In one version of this paradigm, a printed word is divided into two parts using two colours, and participants are asked to report the ink colour of a pre-specified letter target. Prinzmetal, Treiman, and Rho (1986) found that when the colour boundary did not correspond to the syllable boundary, then participants were influenced by the colour of the other letters in the syllable (e.g., VO in red, DKA in blue for the word VODKA participants incorrectly report the letter D as being printed in red ink). Finally, the possible role of morphemes as functional sublexical orthographic representations (see Taft & Forster, 1976, for an early proposal) has been recently revived by the discovery that pseudo-complex words such as ‘corner’ (where the ‘er’ does not function as a suffix as in ‘farmer’) may well be decomposed into their ‘morphological’ constituents during visual word recognition (Longtin, Segui, & Halle´, 2003; Rastle, Davis, & New, 2004). There is also a major line of on-going research investigating the role of sub-syllabic structures in reading (onsets, rimes, graphemes). Although the majority of this research has focused on the process of reading aloud, some research has examined the role of such units in silent word reading. For example, Treiman and Chafetz (1987) showed an advantage for respecting the onset-rime boundary in the divided word paradigm combined with the lexical decision task. More recent research using the letter search paradigm (Rey, Ziegler, & Jacobs, 2000) points to the role of graphemes as functional orthographic units. Rey et al. found that letter detection was harder when the


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target letter was embedded in a complex grapheme (e.g., target A in stimulus ‘bread’) compared with a single letter grapheme (see also, Rey & Schiller, 2005). However, to cut a long story short (and to get back to the main thrust of this introductory article), the take-home message from this line of research is that the ‘letters and words’ approach of interactive-activation is likely not to be the whole story.

Masked priming This historical overview of research on orthographic processing would not be complete without mention of the major methodological innovation of the last two decades. Much of the empirical work investigating orthographic processing over the years has been plagued with the age-old problem of confounding variables. Many researchers have turned to masked priming as a methodology that helps, at least partly, overcome some of the problems associated with direct comparisons of different sets of stimuli (Forster, 1998). Indeed, the vast majority of current research on orthographic processing, as exemplified by several papers in this special issue, uses the masked priming paradigm. Masked priming was first introduced as a paradigm for the investigation of subliminal processing. In one seminal study, Marcel (1983) described an experiment showing semantic priming effects without awareness of prime stimuli. This research sparked off the on-going debate as to whether or not the meaning of words can be processed unconsciously (see Holender, 1986, for a methodological appraisal of the early research). However, this hotly debated issue does not concern orthographic processing, since there is a general consensus that such processing can proceed without awareness. Evett and Humphreys (1981) provided one of the first applications of the paradigm to the study of orthographic processing. However, it was Forster and Davis (1984) who were to become the inspiration of a whole generation by combining masked priming with the lexical decision task rather than the perceptual identification task that had been used in prior studies. It is also important to note the earlier development of a parallel technique in the study of eye movements and reading. The boundary technique, introduced by Rayner (1975), allows a change in stimulus during an eye movement such that at the moment the eye crosses a given (invisible) boundary, the to-be-fixated stimulus is modified. In this way, while fixating word n, the following word (n1) that appears in the parafovea can initially appear in modified form (the equivalent of a prime stimulus) and switched to its form as a target word during an eye movement (in this case the change is not noticeable). Many studies using this parafoveal preview paradigm have shown that orthographically and phonologically related previews influence the subsequent pattern of eye movements on target words, while semantically



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related previews do not (e.g., Pollatsek, Lesch, Morris, & Rayner, 1992; see Rayner, 1998, for a review). Up to now there have only been minor discrepancies between the results obtained with the masked priming paradigm and those obtained with the parafoveal preview technique. These discrepancies are likely due to the fact that prime stimuli are foveally presented in one paradigm and appear in the parafovea in the other, generating differences in the visibility of letters across the prime stimulus in the two paradigms. In the last two decades, masked priming has become a key tool for studying all aspects of visual word recognition, using both behavioural measures of performance and also more direct measures of brain activity (e.g., Dehaene et al., 2004; Holcomb & Grainger, 2006). The technique has been used to study not only the processing of orthographic information, but also the role of phonology, morphology, and meaning in the word recognition process, both within and across modalities (e.g., Grainger, Diependaele, Spinelli, Ferrand, & Farioli, 2003). However, perhaps the most stable, replicable, and therefore uncontroversial results obtained with the masked priming paradigm concern purely orthographic manipulations. I will now summarise these findings.

KEY EVIDENCE In this section, I will summarise the relatively recent but rapidly developing empirical research using masked priming (and related paradigms) to investigate orthographic processing. Three principal kinds of priming manipulation will be highlighted: substitution priming, transposed-letter priming, and relative-position priming.

Substitution priming Substitution priming refers to the manipulation whereby a letter in a given target is replaced by another letter (respecting position) typically not present in the target stimulus. Most research up to now has investigated single letter substitution priming, and mainly with nonword primes. This type of priming was referred to as ‘form priming’ in early studies using the manipulation (e.g., Forster, Davis, Schoknecht, & Carter, 1987). One key finding is that the size of substitution priming effects varies as a function of the target word’s neighbourhood density the so-called ‘density constraint’ on form priming (Forster & Davis, 1991). Facilitatory priming relative to an all-different letter prime condition arises only in targets with small numbers of orthographic neighbours (i.e., targets with low neighbourhood density). However, van Heuven, Dijkstra, Grainger, and Schriefers (2001) have pointed out that target word neighbourhood density is generally confounded with the number


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of orthographic neighbours that are shared by prime and target (the ‘shared neighbourhood’, see also Davis, 2003; Grainger & Jacobs, 1999; Hinton, Liversedge, & Underwood, 1998). High N targets will tend to share several orthographic neighbours with the prime (e.g., the prime DEAK for the target DEAL have ‘dear’ and ‘deaf’ as shared neighbours), while low N targets will tend to be the only orthographic neighbour of the prime (e.g., zind-ZINC). The experiments of van Heuven et al. suggested that it might be the shared neighborhood of prime and target that is critical, thus confirming a key prediction of the IAM. Perry, Lupker, and Davis (2008 this issue) pick up on this important issue. These authors manipulated the ambiguity of substitution primes with letters substituted by a symbol (referred to as partial-word priming by Grainger and Jacobs, 1993). Ambiguous primes have at least one other neighbour in common with the corresponding target, whereas unambiguous primes do not. Perry et al. report larger priming effects with unambiguous primes compared with ambiguous primes. Target word neighbourhood density was also found to affect the size of priming effects with ambiguous primes larger effects being obtained with low N targets. This effect could, however, reflect the number of shared neighbours. Perry et al. also demonstrated an absence of a density constraint (low N versus high N targets) with unambiguous primes. However, Perry et al. did find a larger priming effect for hermit target words (words with no orthographic neighbours), a result that was not predicted by the IAM. Nevertheless, the authors present a fairly convincing case that a more flexible letter position coding scheme might help the model account for the pattern of priming effects they observe. The basic argument is that a more flexible letter position coding mechanism allows for a greater number of words to be activated during target word processing (i.e., there is an increase in the number of ‘neighbours’). Hence, words that are hermits according to the classic N metric are no longer hermits when a more flexible input coding scheme is applied. The simple letter frequency model described by Grainger and Jacobs (1993) is another example of how increasing the number of potential competitor words can improve predictions concerning substitution priming effects. Grainger and Jacobs found that the best predictor of the size of their priming effects was a ratio of the summed frequency of the letters shared by prime and target and the summed frequency of all target letters. Basically, they found that priming effects were stronger when the shared-letter frequency was minimised relative to the total letter frequency of targets. Grainger and Jacobs argue that it is the ability of the prime letters to constrain the identity of the target word that is critical. Low-frequency letters are more constraining than high-frequency letters, since, by definition, they occur in fewer words. In terms of defining orthographic similarity among words, positional letter frequency is situated at one extreme of a continuum



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of metrics on which the N metric is situated at the opposite extreme. This is because the average positional letter frequency of a given word reflects the number of words that share at least one letter in a given position with that word. In all the above-mentioned substitution priming experiments, the position of letters shared by prime and target was respected. In a direct test of the importance of respecting letter positions in substitution priming, Davis and Bowers (2006) presented a series of experiments where the substituted letter could change position (e.g., tasin-train) compared to the standard substitution priming (e.g., tsain-train). They found significant priming in the position change condition, but these priming effects were smaller than the standard substitution priming effect with no change in position. There appeared to be a graded effect of position change, with priming effects decreasing in size as the distance of the position change increased. One would also expect a similar graded influence of the number of substituted letters on substitution priming. However, the evidence at present suggests that priming effects are practically absent as soon as two letters are substituted compared with an all-different letter baseline (e.g., Schoonbaert & Grainger, 2004). Future research will need to test parametric manipulations of number of substituted letters and the degree of displacement of substituted letters in more sensitive measurement conditions, perhaps using longer words (see Guerrera & Forster, 2008 this issue, for a similar manipulation with letter transpositions). Finally, substitution priming has been used to investigate the role of whole-word orthographic representations in visual word recognition. Such whole-word orthographic representations define the contents of the ‘orthographic input lexicon’ posited in many neuropsychological models (Ellis, 1984; Morton, 1969), and form the word-level representation of the IAM. One key characteristic of the IAM is the presence of lateral inhibitory connections between such whole-word representations. This provides a neurobiologically plausible implementation of a winner-take-all mechanism for lexical selection. Is there any behavioural evidence for lateral inhibition between whole-word orthographic representations? Some of the clearest evidence has been provided with the masked priming paradigm and substitution primes that are high frequency words (rather than nonwords, as in most substitution priming experiments). Segui and Grainger (1990) reported that performance to low-frequency target words in a lexical decision task was hindered by the prior presentation of a high-frequency neighbour prime compared with an unrelated high-frequency prime word. Jacobs and Grainger (1992) provided a simulation of this result in the IAM with exactly the same stimulus set as tested by Segui and Grainger. The model’s ability to simulate this finding was all the more convincing given the somewhat counterintuitive nature of the result. More recently, Davis and Lupker (2006)


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provided a replication of this inhibitory priming effect (see also Grainger & Ferrand, 1994) and showed that some relatively minor modifications of the IAM greatly improved its ability to simulate the complete pattern of results they reported (see Perry et al., 2008 this issue, for further details). Finally, De Moor and Brysbaert (2000) showed that high-frequency prime words that are orthographically similar to the target but not the same length (e.g., grootGOOT) interfere in the same way as length-matched neighbours. This result provides further evidence that letter position information is not coded in a strictly length-dependent manner, a conclusion that will become very obvious from the results to be presented in the following sections.

Transposed letter (TL) priming There is a long history of research investigating the effects of letter order on visual word recognition. A typical strategy is to compare performance to anagrams (e.g., bale-able; trial-trail) and matched non-anagram control words (e.g., Andrews, 1996; Chambers, 1979; O’Connor & Forster, 1981), but perhaps the clearest evidence was obtained from experiments using nonword anagrams formed by transposing two letters in a real word (e.g., mohtermother) and comparing performance with matched non-anagram nonwords (Andrews, 1996; Bruner & O’Dowd, 1958; Chambers, 1979; O’Connor & Forster, 1981; Perea, Rosa, & Go´mez, 2005). In the latter type of experiment, it is typically found that such anagram (transposed letter or TL) nonwords are more often misperceived as a real word (the base word from which they were formed) or misclassified as a real word in a lexical decision task than the non-anagram controls. There is also a history of research examining the role of letter order in the perceptual matching task in which participants have to classify two strings of letters as being the same or different. Results with this task have shown a graded sensitivity of ‘different’ response latencies to the number of shared letters and degree to which the shared letters match in position (e.g., Krueger, 1978; Proctor & Healy, 1985; Ratcliff, 1981). In a similar vein, Peressotti and Grainger (1995) reported TL-priming effects in a multiple alphabetic decision task (decide if all characters in a 3-character string are real letters). Once again, priming effects were governed by the number of letters shared across prime and target and the degree of positional match (see Whitney & Cornelissen, 2008 this issue, for a more detailed analysis of these data). Using masked priming with the lexical decision task and relatively long target words, Forster et al. (1987) found that effects of TL primes (e.g., salior-SAILOR) were practically as strong as identity primes. This TLpriming effect was replicated and extended by Perea and Lupker (2003, 2004) using double-substitution primes as controls, and TL priming effects have also been found more recently with the parafoveal preview technique



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(Johnson, Perea, & Rayner, 2007). Schoonbaert and Grainger (2004) reported evidence that the size of TL-priming effects might depend on word length, with larger priming effects for 7-letter compared with 5-letter words. Indeed, the evidence suggests that TL priming disappears with shorter (4-letter) words (Humphreys, Evett, & Quinlan, 1990). Using their 4-field masking procedure and perceptual identification responses to targets, Humphreys et al. found no significant difference between TL primes (e.g., snad-SAND) compared to 2-letter substitution control primes (e.g., smedSAND). On the other end of the continuum, Guerrera and Forster (2008 this issue) showed robust TL priming effects in long (8-letter) words with rather extreme TL manipulations (involving three transpositions, e.g., 13254768).2 Thus, target word length and/or target neighbourhood density might critically determine the size of TL priming effects. Concerning a possible influence of letter position (inner versus outer letters) in TL priming, prior research with nonword anagrams has shown that nonwords formed by transposing two inner letters are harder to respond to in a lexical decision task than nonwords formed by transposing the two first or the two last letters (Chambers, 1979). Schoonbaert and Grainger (2004) reported evidence that TL primes involving an outer letter (the first or the last letter of a word) were less effective than TL primes involving two inner letters. The results of Guerrera and Forster (2008 this issue) also suggest a privileged role of a word’s outer letters (see Jordan, 1995; Jordan, Thomas, Patching, & Scott-Brown, 2003, for further evidence). TL priming can also be obtained with the transposition of non-adjacent letters. Strong effects of non-adjacent TL primes were reported by Perea and Lupker (2004) with 610 letter long Spanish words. This result was replicated in English by Lupker, Perea, and Davis (2008 this issue) in order to rule out a possible influence of the syllabic structure of Spanish words. Interestingly, there has been no direct comparison of the size of TL priming effects as a function of the number of letter spaces that separate the transposed letters (adjacent, 1-letter apart, 2-letters apart, etc.), yet the models to be described in the following section all predict an effect of this variable on the size of TL priming effects. Also, Guerrera and Forster (2008 this issue) demonstrate that priming effects can be obtained when primes include multiple adjacent transpositions (e.g., 12436587). Clearly what we need now are parametric manipulations of the number of transpositions and the size of the transposition (number of intervening letters). 2

In providing examples of prime and target stimuli for some of the experiments to be described here, the following notational convenience will be adopted: for a target string ‘12345’ (where 1 denotes the first letter, 2 the second letter etc., of the target), a prime string ‘1d43d’ is formed of the first letter of the target, a letter that is not in the target (‘d’ refers to ‘different’), the fourth and third letter of the target, and another letter that is not in the target, in that order.


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Having established the robustness of TL priming, several studies then proceeded to examine at which level of processing these TL effects arise. For a given level of processing (e.g., syllabic, morphemic), the typical manipulation is to select the pair of TL letters as either occurring within a given unit or straddling the boundary between two units. Research applying this strategy has found evidence that TL effects are insensitive to syllabic structure (Perea & Carreiras, 2006), but sensitive to the morphological structure of derived words (Christianson, Johnson, & Rayner, 2005; Dun˜abeitia, Perea, & Carreiras, in press). In the latter studies, no TL priming was found when the transposed letters crossed a morpheme boundary. This result suggests that bound morphemes may play a key role in early orthographic processing, as already suggested in recent studies of masked morphological priming (e.g., Diependaele, Sandra, & Grainger, 2005; Longtin et al., 2003; Rastle et al., 2004). An increasing number of recent studies investigating the locus of TL priming effects have focused on the possible role of phonology. Frankish and Turner (2007) manipulated the pronounceability of TL nonword targets in lexical decision and perceptual identification tasks with brief stimulus presentation. Their key finding concerned TL effects in nonwords where the letter transposition either created a pronounceable letter string (e.g., BARVE) or an unpronounceable string (e.g., PLCAE). Frankish and Turner found more incorrect false positive lexical decision responses and more incorrect report of the TL base word with the unpronounceable nonwords. Although pronounceability was confounded with orthotactics in this manipulation, Frankish and Turner opted for a phonological interpretation of their results on the basis of partial correlations between error rates and bigram frequency and pronounceability ratings (it should also be noted that orthotactic violations generally produce the opposite effect, in that orthotactically illegal strings tend to be easier to accept as nonwords). This interpretation was confirmed by the fact that dyslexic participants did not show any effect of pronounceability on error rate to TL nonwords. Perea and Carreiras (2008) report an analogous finding with masked priming. TL priming effects were found to be significantly greater when the TL primes formed an illegal letter string. Since orthotactics was again (and inevitably so) confounded with pronounceability in this study, it would appear premature to draw any firm conclusions for the time being. Perea and Carreiras (2008 this issue) argue against a possible influence of phonology on TL priming on the basis of other types of manipulation. Perea and Carreiras (2006) compared orthographic TL primes (relovucion-REVOLUCION) with phonological TL primes (e.g., relobucion-REVOLUTION, where ‘b’ and ‘v’ receive the same pronunciation in Spanish) and only found



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priming for the former. Perea and Carreiras (2008 this issue) further show that context-dependent changes in pronunciation (e.g., racidal-RADICAL, where the ‘c’ is pronounced differently in each string) does not affect TL priming. These results clearly suggest that most of the TL priming effect is driven by a mechanism that takes position-coded letter identities and maps them onto whole-word orthographic representations. TL priming is not likely to be due to a mechanism that takes position-coded phoneme representations and maps them onto whole-word phonological representations. However, this conclusion does not necessarily imply that phonology has no influence whatsoever on TL effects. As suggested by Frankish and Turner (2007), prelexical phonology (e.g., phonemes) could be used to clean-up orthographic processing via mutual interactions with letter-level processing. This hypothetical clean-up process is not in contradiction with the findings reported by Perea and Carreiras (2006, 2008 this issue). One finding that might pose a problem for a purely orthographic account of TL priming effects is the reported difference between vowels and consonants. Perea and Lupker (2004) found robust TL priming effects when the transposed letters were consonants but not when they were vowels. Lupker, Perea, and Davis (2008 this issue) replicated this finding and provide evidence that at least part of the vowel-consonant influence on TL priming might be due to the fact that vowels are more frequently occurring letters than consonants. Lupker et al. show that the size of TL priming obtained with non-adjacent consonant transpositions is larger for low-frequency than high-frequency consonants. These authors offer one possible interpretation of this finding in terms of the level of precision of position coding as a function of letter frequency. They also mention a possible role for differences in processing speed as a function of letter frequency. This latter interpretation would fit within the sublexical clean-up mechanism proposed by Frankish and Turner (2007), since high-frequency letters would benefit from such clean-up more quickly than low-frequency letters (assuming faster propagation of activation from high frequency letters). Finally, Lupker et al. (2008 this issue) make the important point there is little evidence that mixing consonant-vowel status has any influence on TL priming. The results show that mixed CV transpositions are just as effective as CC transpositions, and more effective than VV transpositions (Perea & Lupker, 2004). This counters the proposal that an abstract CVC structure is used in the early phases of orthographic processing (e.g., Buchwald & Rapp, 2006; Caramazza & Miceli, 1990). In all of the above-cited studies, the TL primes contained all of the target’s letters. When primes do not contain all of the target’s letters TL priming effects diminish and tend to disappear (Humphreys et al., 1990; Peressotti &


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Grainger, 1999).3 This manipulation is part of the research investigating relative-position priming effects.

Relative-position (RP) priming Relative-position priming is a variety of orthographic priming that involves a change in length across prime and target such that shared letters can have the same order without being matched in terms of absolute, length-dependent position. This can be achieved either by removing some of the target’s letters to form the prime stimulus (subset priming), or adding letters to the target (superset priming). In this situation, primes and targets differ in length so that absolute position information changes, while the relative order of letters in primes and targets is maintained. For example, for a 5-letter target (12345), a 5-letter substitution prime such as 12d45 contains letters that have the same absolute position in prime and target, while a 4-letter subset prime such as 1245 contains letters that preserve their relative order in prime and target but not their precise length-dependent position. In a seminal study, Humphreys et al. (1990, Experiment 4) found significant priming for primes sharing four out of five of the target’s letters in the same relative position (1245) compared to both a TL prime condition (1435) and an outer-letter only condition (1dd5). Further evidence for effects of relative-position priming was provided by Peressotti and Grainger (1999) using the Forster and Davis masked priming technique. With 6-letter target words, relative-position primes (1346) produced significant priming compared with unrelated primes (dddd). Inserting filler letters or characters (e.g., 1d34d6, 1-34-6) to provide absolute position information never led to significantly larger priming effects in this study. Violating the relative position of letters across prime and target (e.g., 1436, 6341) cancelled priming effects relative to all different letter primes (dddd). This critical result was replicated by Grainger, Granier, Farioli, Van Assche, and van Heuven (2006a) with 7-letter target words. Statistically equivalent priming was found for 13457, 1-345-7, 13-4-57 prime types, and no priming from 1-543-7 and 7-345-1 prime types, compared with all different letter primes. Grainger et al. also manipulated the position of overlap of RP primes and the contiguity of the prime letters in 7-letter and 9-letter target words with 4-letter and 5-letter primes. Small advantages for beginning-letter primes (1234/12345) compared with end-letter primes (4567/ 6789/34567/56789) was shown to be likely due to the greater phonological overlap between primes and targets in the former condition. Likewise, an 3

It is interesting to note that this might explain why TL effects are less evident in Hebrew (Velan & Frost, in press), since Hebrew words are written without the vowels in the concatenated written form adopted by Semitic languages.



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advantage for completely contiguous primes (the initial and final primes described above) compared with non-contiguous primes (1357/13457/1469/ 14569) was again shown to be due to the greater phonological overlap in the contiguous condition. No effects of position or contiguity were evident with reduced (30 ms) prime durations conditions in which RP priming effects were present and phonological priming effects absent. It is also interesting to note that RP priming effects vanished at longer (80 ms) prime durations conditions in which phonological priming was robust. This might be further evidence in favour of the phonological clean-up procedure invoked by Frankish and Turner (2007) to explain phonological influences on TL effects. It is interesting to note that in Grainger et al.’s (2006a) study, there was never an advantage for primes having the target’s two outer letters. However, some unpublished work from our laboratory does suggest that the initial letter of target words may play a privileged role in determining the size of RP subset priming effects. This is clearly an important point for further investigation. Another area that merits further exploration is the possible influence of letter repetition in RP priming. Schoonbaert and Grainger (2004) report a study in which 7-letter target words contained a non-adjacent repeated letter (e.g., balance), and prime stimuli were formed by removing either one of the repeated letters (e.g., balnce) or one non-repeated letter (e.g., balace). Priming effects were not influenced by the presence or absence of a letter repetition in the prime stimulus. On the other hand, performance to target stimuli independently of prime condition was adversely affected by the presence of a repeated letter, and this was true for both the word and the nonword targets. Finally, a novel form of relative-position priming (superset priming) has been examined in two recent studies (Van Assche & Grainger, 2006; Welvaert, Farioli, & Grainger, in press). Superset priming involves the addition of irrelevant letters to target stimuli such that primes are formed of the target letters in the correct order plus a certain number of other letters inserted at varying positions. The first major finding is that such superset primes generate strong priming effects (see Bowers, Davis, & Hanley, 2005, for a similar finding with real word supersets and a semantic categorisation task is ‘hatch’ an item of clothing?). For example, in both Van Assche and Grainger (2006) and Welvaert et al.’s (in press) study, 2-letter insertions generated priming effects of about 2025 ms. The results at present suggest that letter insertions generate a cost in processing of about 12 ms per letter (see Welvaert et al. for a meta-analysis). However, it is still not clear how much (if any) of this processing cost can be attributed to bottom-up letterword inhibition and how much attributed to perturbations in letter position coding (see Andrews & Davis, 1999, and Van Assche & Grainger, 2006, for a discussion of the role of letter-word inhibition). Future research should also investigate the role of contiguity of letter insertions (e.g., 12d3d4d56 vs.


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123ddd456), as a means of teasing apart the predictions of the different models to be presented below.

NEW MODELS The empirical data described in the preceding section and in the papers of this special issue have given rise to the general consensus that there exists a level of orthographic processing where some form of approximate, flexible coding of letter positions operates. The models differ mostly in terms of how this approximate letter position coding is achieved. There are basically three solutions, to be described below (and in the papers of this special issue), that are implemented within the three main classes of position coding mechanism: slot-based coding, context-sensitive coding, and spatial coding.

Noisy slot-based coding The most precise means of coding letter position information in computational models of visual word recognition is to use representations that code identity and position together. This is called conjunctive coding. Thus, a given letter is tagged to a specified location (slot) in the string. For example, in the IAM (McClelland & Rumelhart, 1981), letter strings are processed in parallel by a set of length-dependent, position-specific letter detectors. This means that, for example, there is a processing unit for the letter T as the first letter of a 4-letter word, a different unit for T as the second letter of a 4-letter word, and a different unit for the letter T as the first letter of a 5-letter word. This slot-based, position-specific, length-dependent scheme codes absolute letter position as opposed to relative position information. This is the most precise means of coding letter position information, but such precision is computationally quite costly, involving a large number of duplications of the alphabet and hence a serious risk of combinatorial explosion (i.e., when all these letter representations are connected up to tens of thousands of word representations). Furthermore, the empirical evidence described in the preceding section strongly suggests that this coding scheme is not as flexible as the one used by human readers. Noisy slot-based coding refers to the addition of Gaussian noise to the classic slot-based scheme used in the IAM, with an associated decrease in efficiency but increase in flexibility. Davis and Bowers (2004) used the term ‘slots plus slop’ to refer to this type of coding scheme. The most prominent example of this approach is the ‘overlap model’ of Gomez, Perea, and Ratcliff (2007). The origins of this model can be found in Ratcliff’s (1981) account of effects of letter position on same-different matching judgements



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of strings of letters. By adding noise to the mechanism that assigns a given letter identity to a given position (slot), evidence that a given letter is at position ‘p’ is also taken as evidence that the same letter is at positions p1, p1, etc. The probability of a given letter being at a given position diminishes as a function of the distance from the true location following a Gaussian distribution. This general approach provides a straightforward account of TL priming effects (and other TL phenomena) when the transposed letters are adjacent. However, it clearly predicts that effects of non-adjacent TL primes should be less effective, while the empirical evidence at present would suggest perhaps that this is not the case. It also has trouble accommodating the absence of priming with multiple adjacent transpositions, as shown by Guerrera and Forster (2008 this issue). Furthermore, it is not clear that this approach could handle the extreme cases of subset RP priming reported by Grainger et al. (2006a). In short, noisy slot-based coding is prone to many of the same pitfalls as standard slot-based coding. It would appear that one cannot do away with a level of orthographic processing that explicitly codes for relative position. Grainger et al. (2006a) suggest that the principle of noisy slot-based coding applied at the level of location-specific letter detectors could be usefully combined with a higher-level bigram coding mechanism (see also Dehaene, Cohen, Sigman, & Vinckier, 2005). Finally, the minimalist slot-based scheme (excluding a one-slot scheme which is tantamount to not coding for position at all) was proposed by Shillcock, Ellison, and Monaghan (2000) in their split-fovea model of visual word recognition. Letters are assigned to one of two possible positions: left and right of the point of eye fixation in the word. Letters falling to the left of fixation are sent to the right hemisphere and processed as an unordered set of letters in that position, while letters to the right of fixation are sent to the left hemisphere forming an unordered set of letters in that location. Shillcock et al. (2000) showed that 98.6% of all the words in the CELEX database are uniquely identified by the two sets of unordered letters generated by a central/centre-left split. However, for 4-letter words, this figure drops somewhat to 95.3% (i.e., 4.7% of these words are ambiguous). Shillcock et al. solve this problem by specifying the identity of the first and last letter, thus assigning a special role to exterior letters in orthographic processing. Thus, in the split-fovea model, letter identities are in fact assigned to one of four possible positions: first letter, last letter, inner letter left, inner letter right (see Peressotti & Grainger, 1999, for a similar proposal). Whether or not this provides enough flexibility to account for the effects of some of the extreme priming manipulations presented above remains to be seen with thorough tests of this approach.


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Non-contiguous context-sensitive coding The inspiration for letter position coding schemes that use local context comes from the work of Wickelgren (1969) who introduced the concept of a ‘wickelphone’ as a means for encoding phoneme positions in speech. This scheme was adapted by Seidenberg and McClelland (1989) in the form of ‘wickelgraphs’ or letter triples. Thus the word BLACK is coded as an unordered set of letter triples: #BL, BLA, LAC, CK, CK# (where # represents a space). Wickelgraphs code local context in that only information about the relative position of adjacent letters is directly computed. In general, however, a given set of letter triples has only one possible ordering for a given language (e.g., the 5 wickelgraphs for the word BLACK cannot be re-arranged to form another English word). Wickelgraph coding cannot, however, account for the TL-priming and RP-priming effects reported above. A more elaborate local-context scheme was proposed by Mozer (1987) in his BLIRNET model. Again, letter triples form the basis of local-context coding in this model, but the scheme is enhanced by coding of noncontiguous letter sequences as well as contiguous sequences. In Mozer’s model such non-contiguous combinations could be formed by inserting a letter between the first and the second, or between the second and the third letter of each letter triple. So the word BLACK contains the letter triple BLA (and other letter triples as above) plus open trigrams such as BL_C and B_AC (the underscore signifies that any letter can be inserted in this position). The idea of coding the relative position of non-adjacent letters has been used in two more recent accounts of letter position coding (Grainger & van Heuven, 2003; Whitney, 2001). The first illustration of how openbigrams can be used in orthographic processing can be found in Whitney’s (2001) SERIOL model. In the example provided by Whitney, the word CART is coded as the following set of bigrams: CA, CR, CT, AR, AT, RT. Thus, bigrams are formed across adjacent and non-adjacent letters in the correct order, the basis of what is now referred to as open-bigram coding (Grainger & Whitney, 2004). Activation of the appropriate open-bigram units is achieved by the sequential firing of individual letter representations, governed in turn by a beginning-to-end locational gradient at the level of feature representations. Open-bigrams act as an intermediate level of coding between individual letters and whole-word representations (see Whitney & Cornelissen, 2008 this issue, for further details). In Grainger and van Heuven’s model, open-bigrams also play a central role. The critical difference between this account and the one developed by Whitney (2001) concerns the presence or not of a serial, beginning-to-end, locational gradient across letter representations that is used to activate ordered pairs of letters in the SERIOL model. Grainger and van Heuven’s approach is more in the tradition of Mozer (1987) using a hierarchical,



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parallel activation mechanism. It also draws inspiration from Caramazza and Hillis’ (1990) distinction between retinotopic, location-specific, and word-centred, location-invariant representations of printed words. One key element in Grainger and van Heuven’s model is the alphabetic array, a hypothesised bank of letter detectors that perform parallel, independent letter identification. These letter detectors are assumed to be invariant to the physical characteristics of letters (both their size and their shape), but not invariant to position (see Chauncey et al., 2008 this issue, for a test of some of these assumptions). According to this model, featural information at a given location along the horizontal meridian is mapped onto abstract letter representations that code for the presence of a given letter identity at that particular location. These abstract letter representations are thought to be activated equally well by the same letter written in different case, in a different font, or a different size (within a given level of tolerance for variations in size). The next stage of processing, referred to as the relativeposition map, is thought to code for the relative (within-stimulus) position of letter identities independently of their shape and their size, and independently of the location of the stimulus word (location invariance). This is achieved by open-bigram units that receive activation from the alphabetic array such that a given letter order A_B that is realised at any of the possible combinations of location in the alphabetic array activates the open bigram for that sequence. Open-bigram coding, in its most general form, can handle most of the priming effects described above, and the fits with the data appear to be best in a graded version of this approach in which bigram activation is a function of the distance between the component letters (SERIOL model, overlap open-bigram model). However, such graded versions do have difficulty in capturing the extreme cases of non-contiguous subset priming reported by Grainger et al. (2006a). Furthermore, the absence of an influence of letter repetition on subset priming (Schoonbaert & Grainger, 2004) is problematical for open-bigram coding.

Spatial coding The notion of spatial coding was developed in the pioneering work of Steve Grossberg (e.g., Grossberg, 1978), and forms the basis of one recent account of letter position coding, the SOLAR model (Davis, 1999). The relative position of spatially distributed items is coded in terms of their relative activation level. This is best achieved when the items in the list form a monotonically increasing or decreasing set of activation values, referred to as an activation gradient. For the purposes of letter position coding, the activation gradient must form a monotonically decreasing activation


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function across letter position with the highest value for initial letters and the lowest value for the final letter of the string. In the SOLAR model, each word detector computes its input by calculating the match between two spatial codes: (a) the spatial code corresponding to the word represented by this detector (the ‘comparison word’), and (b) the spatial orthographic code corresponding to the input stimulus. This match calculation evaluates the degree of orthographic overlap between the two codes. To do this, signal-weight differences are computed for each letter in the comparison word, with each difference giving rise to a Gaussian function (reflecting letter position uncertainty, cf. the ‘overlap model’ described above). These functions are summed to compute a superposition function, the amplitude of which determines the match value. If the input stimulus contains all of the letters of the comparison word, in the same relative positions, each of the signal-weight differences will be identical, and the superposition function will have a large amplitude (tall narrow function), indicating a perfect match (this is true even if the letters are in different absolute positions). If the letters are closely, but not perfectly aligned (as with transpositions), the superposition function will have a smaller amplitude, but still tall enough to ensure a good match. However, if the common letters of the comparison word and the input stimulus are misaligned (e.g., as in anagrams like wolf and flow), the superposition function will have a low amplitude (low wide function), resulting in a small match value.4 Grainger et al. (2006a) highlight one possible limitation with the SOLAR model in that it might give too much weight to the number of letters shared by prime and target relative to the precise ordering of these letters, predicting stronger priming from 15437 primes for 7-letter targets compared with 1469 primes for 9-letter targets. This, of course, requires further experimentation before any definitive conclusions can be drawn.

CONCLUSIONS AND FUTURE DIRECTIONS The papers in this special issue provide a state-of-the-art sample of current research on orthographic processes in single word silent reading. The backbone of the special issue is the question of how letter position information is coded during the earliest phases of visual word recognition. The different contributions all assume that individual letters are the key elements for orthographic processing and that it is the mechanism used to 4 A program for computing match values in the SOLAR model can be downloaded from this website: http://www.pc.rhul.ac.uk/staff/c.davis/Utilities/



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code for the positions of these letters that critically determines the nature of the orthographic code. Different letter position coding schemes were examined (noisy slot-based coding, open-bigram coding, spatial coding), each of which is capable of accounting for most of the key evidence summarised here. A model can be judged superior for a specific data set (and there are examples of this in the contributions to the special issue), but it would be premature to definitively exclude any one approach at present. The relative success of the three main types of letter position coding scheme hinges on the flexibility that each one assigns to the process of coding for letter position. Some of the putative failures of these models to account for certain data patterns can often be attributed to intended oversimplifications of the models (for purposes of tractability) that can be easily corrected. On the other hand, credit for the successes of the different models can sometimes be assigned to ad-hoc extensions. Only multiple testing and refining of these different theoretical approaches will allow one single model to emerge as the dominant, consensual approach. The experiments reported in several of the papers of this special issue provide a sample of the kind of empirical work that needs to be continued. Indeed, letter position coding is an exemplary area of investigation in cognitive (neuro)science with a healthy, lively interaction between empirical and theoretical research. Apart from fine-grained manipulations designed to test specific predictions of a given model or models, I can see two major additional sources of constraint that should help further refine current accounts of letter position coding. One important constraint involves acknowledgment of the pervasive influence of phonology during written language comprehension. The other constraint involves accepting the fact that brain structure determines brain function.

Phonological constraints on letter position coding The papers in this special issue all assume that orthographic processing is only one of the ingredients of the general process of visual word recognition, and several papers examine how the orthographic processor connects up with the other components of such a general model. The critical question to be examined here is how prelexical orthographic information is mapped onto a prelexical phonological code, a mapping process that is deemed crucial for correct reading acquisition (Ziegler & Goswami, 2005), and how that might constrain the nature of the orthographic code (Goswami & Ziegler, 2006). There is a growing consensus that orthographic processing must connect with phonological processing quite early on during the process of visual word recognition, and that phonological representations constrain orthographic processing (e.g., Frost, 1998; Van Orden, 1987; Ziegler & Jacobs, 1995).


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There is now abundant evidence showing phonological priming in the masked priming paradigm (see Rastle & Brysbaert, 2006, for a review). The time-course of orthographic and phonological priming found in the behavioural work of Ferrand and Grainger (1992, 1993, 1994; see also Ziegler, Ferrand, Jacobs, Rey, & Grainger, 2000) was recently confirmed in an ERP study by Grainger, Kiyonaga, and Holcomb (2006b). In this study, effects of TL priming were found to appear earlier, by about 50 ms, in the ERP waveforms than pseudohomophone priming effects. Furthermore, there is recent evidence from masked priming that such phonological influences on visual word recognition operate sequentially (Carreiras, Ferrand, Grainger, & Perea, 2005). Finally, the results of Grainger et al. (2006a) showed RP priming effects at short (30 ms) prime durations, which disappeared at longer (80 ms) prime durations, while the opposite pattern was found for pseudohomophone primes (see Ferrand & Grainger, 1994, for a similar result). Within the framework of a generic dual-route model of visual word recognition, these results all suggest that the direct route from orthography to meaning implements a fast, parallel, coarse-grained orthographic code that provides an initial constraint on word identity, whereas the slower phonological route would require a more fine-grained, sequential orthographic code that is necessary for accurate grapheme-phoneme conversion. This fine-grained orthographic code for phonological recoding could be derived via an attentional sweep from the beginning to the end of the word, resulting in a monotonically decreasing activation gradient across letter representations. This tentative proposal fits with the recent claim by Perry et al. (2007) that focused attention is necessary for phonological recoding but not for direct orthographic access. One critical distinction between the above proposal and Whitney’s approach (see Whitney & Cornelissen, 2008 this issue) is that letter processing is completely serial in the SERIOL model, whereas it is both parallel (direct orthographic route) and serial (phonological route) in the account described above. Although one might prefer Whitney’s approach for reasons of parsimony, I would argue that the evidence is not yet clearly in favour of one or the other approach. Most importantly, it is always difficult to know when a ‘serial’ pattern of effects is due to serial orthographic processing or serial phonological processing, and to what extent the effect might be due to the serial nature of response read-out rather than the serial nature of perceptual processing. The more general question here is to what extent the temporal organisation of spoken language determines how we process written language. Sequential processing of graphemes would certainly facilitate the process of mapping graphemes onto phonemes as in the DRC model (Coltheart et al., 2001) and the CDP model (Perry et al., 2007), but does this influence spread further down to the level of individual letters, as suggested by Whitney (2001)?



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A generic ‘dual-route’ approach is adopted in several papers of the special issue (Perea & Carreiras, 2008 this issue; Whitney & Cornelissen, 2008 this issue), and is adopted (explicitly or implicitly) by many current models of visual word recognition and reading (e.g., Coltheart et al., 2001; Perry et al., 2007). According to one type of dual-route model, the bi-modal IAM (Grainger et al., 2003; Grainger, Muneaux, Farioli, & Ziegler, 2005; Grainger & Ziegler, in press), there is complete interactivity between letter-level processing and the sublexical interface between orthography and phonology (that implements grapheme-phoneme conversion). This would provide the mechanism for phonological clean-up as proposed by Frankish and Turner (2007) in order to account for the influence of pronounceability on transposed-letter effects. Finally, the generic dual-route theory described above suggests a possible dissociation in the type of algorithm involved in learning to map orthography onto meaning and onto phonology (Perry et al., 2007; Zorzi, Houghton, & Butterworth, 1998). Following Davis (1999), it seems likely that much of the orthographic code involved in the direct route develops through unsupervised self-organised learning. Following Perry et al. (2007) and Zorzi et al. (1998), we expect that the prelexical mapping of orthography-to-phonology is learned mainly via supervised learning. This distinction in terms of type of learning algorithm associated with the different processing routes fits with the proposal that focused attention is necessary for phonological recoding, but not for the mapping of orthography onto semantics (Perry et al., 2007).

Anatomical constraints on letter position coding One major step forward in the last decade has been the acknowledgement that no account of visual word recognition can be complete without taking into consideration the anatomical constraints of the organ that performs this function the brain. I therefore end this introductory article with a reflection on how the structure of the primate visual system, current knowledge concerning visual object recognition, and recent brain imaging studies of visual word recognition might constrain future models of orthographic processing. Two articles in this special issue propose that the structure of the mapping of retinal cells onto primary visual areas in each hemisphere is one major constraint on how written words are processed. This constraint is well described by Hunter and Brysbaert (2008 this issue) in terms of interhemispheric transfer costs. The assumption is that information falling to the right and left of fixation, even within the fovea, is sent to area V1 in the contralateral hemisphere. Since the brain regions involved in language processing are highly lateralised (in the left hemisphere for a majority of


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persons), this implies that information to the left of fixation, and processed initially by the right hemisphere, must be redirected to the left hemisphere (callosal transfer) in order for word recognition to proceed intact. Both Shillcock et al.’s (2000) split fovea model, and Whitney’s (2001) SERIOL model assume that orthographic processing makes use of the information provided by the split fovea. The other coding schemes assume that orthographic processing operates on representations that are activated following callosal transfer, and minimise the role of inter-hemispheric transfer. Hunter and Brysbaert (2008 this issue) discuss empirical evidence in favour of the role of inter-hemispheric transfer as implemented in the SERIOL model. The data show a modification of the function relating word reading efficiency and fixation position in the word (the optimal viewing position or OVP effect: O’Regan & Jacobs, 1992; Brysbaert & Nazir, 2005), as a function of the degree of lateralisation of language structures in the brain of individual participants (measured by fMRI). This suggests that at least part of the OVP effect is caused by differences in interhemispheric transfer costs as a function of how much of the word is initially sent to the non-dominant hemisphere. Hunter and Brysbaert also show that the empirical findings in question can also be accounted for by changes in the perceptibility of the most informative letters in words as a function of hemispheric dominance in combination with a cost for interhemispheric transfer (see Clark & O’Regan, 1999, and Stevens & Grainger, 2003, for analysis of the role of letter visibility in the OVP effect). Another more general constraint is related to the fact that written words are visual objects before attaining the status of linguistic object. The central idea here is that there is pre-emption of visual object processing mechanisms during the process of learning to read (McCandliss, Cohen, & Dehaene, 2003). Grainger and van Heuven’s (2003) alphabetic array is one such mechanism, described as a specialised system developed specifically for the processing of strings of alphanumeric stimuli. However, the principle of preemption was most fully illustrated in the Local Combination Detector (LCD) model of Dehaene et al. (2005). In this model, orthographic processing is described within the standard view of how the primate visual system achieves shape- and location-invariance in visual object processing. This is thought to occur via a hierarchy of increasingly invariant representations, such that complete invariance is derived from a retinotopic feature map in several processing steps. Thus, Dehaene et al.’s model of orthographic processing involves a hierarchy of increasingly complex, frequency-tuned representations features, letters, bigrams, trigrams, etc. (Dehaene et al., 2005). Perhaps the most important point made by Dehaene et al. (2005) is that the computational power required to derive shape- and locationinvariant orthographic representations from a retinotopically organised set



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of visual features is best achieved via a hierarchically organised multi-level processing system. The error with a more abstract computational approach, according to Dehaene et al., is to try to do too much in a single step. In this special issue, Whitney and Cornelissen (2008 this issue) provide a comparison of Dehaene et al.’s model with the SERIOL model and Grainger and van Heuven’s open-bigram model, describing how the different components of the different models map onto location-specific (retinotopic) or location-invariant representations. Independently of the specific assumptions of each model, the general idea that orthographic processing involves a gradual shift toward more invariant representations finds support in recent brain imaging studies of visual word recognition. This research suggests that orthographic processing is achieved by a series of computations performed by neurons in a small strip of left fusiform gyrus in the occipito-temporal cortex (referred to as the ‘visual word form area’ VWFA, by Cohen et al., 2000). Dehaene et al. (2004) provide evidence for a shift from locationspecific to location-invariant orthographic processing within this region. Once visual features have been extracted from the stimulus, orthographic processing would proceed in a posterior-anterior sweep through left fusiform (e.g., Binder, Medler, Westbury, Liebenthal, & Buchanan, 2006; Cohen et al., 2000; Dehaene et al., 2005). Other studies using imaging techniques with greater temporal resolution (MEG, EEG) suggest that orthographic processing in left fusiform is underway after about 150 ms post stimulus onset, and continues on for another 150 ms (e.g., Marinkovic et al., 2003; Pammer et al., 2004). In line with these estimates from MEG studies, Chauncey et al. (2008 this issue) describe results obtained by combining EEG recordings with masked priming, an approach that has been applied in several recent investigations of visual word recognition (e.g., Grainger & Holcomb, in press; Holcomb & Grainger, 2006; Kiyonaga, Midgley, Holcomb, & Grainger, 2007). Chauncey et al. (2008 this issue) analyse data concerning three ERP components modified by masked repetition priming: the N/P150, the N250, and the N400. These components are thought to reflect the mapping of visual features onto location-specific letter representations (N/ P150), prelexical orthographic processing (N250), and the mapping of word forms onto semantic representations (N400), with peak latencies at 150 ms, 250 ms, and 400 ms post-target onset, respectively. The combination of fine-grained orthographic manipulations (as illustrated in several papers of this special issue) using masked priming and brain-imaging techniques should allow future research to provide a more and more detailed plot of the progress of orthographic processing from visual features to increasingly invariant abstract orthographic representations, and to describe how this orthographic code connects up to phonology and semantics during visual word recognition. Within this general endeavour,


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solving the problem of flexible letter position coding will certainly be a major contribution, and a major step toward cracking the orthographic code.

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