Cohen et al. (2008) in Neuroimage

In this fMRI experiment, the authors presented words that were progressively degraded, under three manipulations:

• Shifting the word into the LVF
• Increasing the spacing between letters
• Rotating the string

Within each manipulation, there were 5 levels of degradation, where level 1 was normal presentation, and level 5 was maximally degraded. For levels 4 and 5, the authors found a behavioral length effect under all three degradations. Parietal activity increased from level 1 to level 5. The authors conclude that words are normally processed in parallel, while degradation causes attention-driven, serial processing.

I wrote a commentary on this article, but Neuroimage would not publish it. Briefly, the article points out that there are 3 main problems with their analysis.

• If there's an abrupt shift in processing mode at the onset of the length effect (between levels 3 and 4), parietal activity should show a large jump between levels 3 and 4. However within each manipulation, parietal activity was similar for levels 3 and 4.
  
• Attention-driven processing cannot explain the time scale of the length effect, which was ~20 ms/letter, as it's been shown that serial covert shifts of attention take at least 300 ms per shift.
• The authors cannot explain the results of Whitney & Lavidor (2004), who showed that the LVF length effect can be abolished via a contrast manipulation.
  

Furthermore, it is straightforward to explain their results under the SERIOL model. As I previously proposed in email to Andy Ellis, degradation would interfere with automatic bottom-up formation of the locational gradient. Therefore, top-down attention is recruited to form the activation gradient. This yields a less finely tuned (steeper) gradient than normal, and a length effect emerges from the usual serial processing. See the commentary for details.

Vinckier et al. (2007) in Neuron

In this fMRI study, the authors varied the word-likeness of string stimuli at six levels - false fonts, rare letters forming rare (contiguous) bigrams, frequent letters forming rare bigrams, frequent letters forming frequent bigrams but rare quadrigrams, frequent bigrams forming frequent quadrigrams, words - and investigated the sensitivity of brain regions around the VWFA to this manipulation. In their Discussion, the authors state:

"Our results demonstrate effects of letter and quadrigram frequency above and beyond those of bigram frequency, suggesting that all of these levels (Dehaene et al., 2005), not just bigrams (Grainger & Whitney, 2004; Whitney, 2001), may be useful subcomponents of visual word recognition."

However the claim that their data provides evidence for quadrigram detectors is tenuous, at best. The claim comes from the comparison of frequent bigrams forming rare quadrigrams vs. frequent bigrams forming frequent quadrigrams. However, this comparison is confounded with the pronounceability of the stimuli. The rare quadrigram stimuli were not pronounceable, whereas the frequent quadrigram stimuli were pronounceable. Thus frequent-quadrigram stimuli were much more likely to yield partial activation of lexical representations, and therefore any difference between the two may reflect different levels of lexical activation, rather than quadrigram activation. This is supported by their finding that only words and frequent quadrigrams yielded significant activation of posterior middle temporal gyrus, a region associated with lexico-semantic processing.

On the other hand, the contrast between frequent letters forming rare bigrams vs frequent letters forming frequent bigrams provides does not suffer this confound, as both types of strings were not pronounceable. Differences between between these types of stimuli were found in middle/anterior left fusiform. Binder et al. (2006, Neuroimage) also found sensitivity to bigram frequency in this area in another fMRI study. These results support the claim of multi-letter units, such as open-bigrams, and are difficult to explain under models that do not include them, such as Davis's SOLAR model and Gomez et al.'s Overlap model.

LCD model - Dehaene et al. (2005) in TICS

Following Whitney's and Grainger's proposal that the highest pre-lexical orthographic encoding on the lexical (ventral) route is comprised of non-contiguous bigrams (dubbed open-bigrams by Grainger), Dehaene and company got into the act with their Local Combination Detector (LCD) model. While the model is somewhat vague, they do make two specific claims:

1. Open-bigram representations do not provide sufficient accuracy in encoding letter order. Therefore, the highest level of the LCD model includes quadrigram detectors to provide a more precise encoding of letter order.
2. Open-bigram-like representations occur as a result of retinotopic bigram detectors operating over noisy retinotopic representations of individual letters.

Claim (1) has some problematic aspects:

• The model does not include a location-invariant encoding, as the quadigram detectors are retinotopic.
• Quadrigrams do not provide a realistic similarity metric. For example, LAME and LIME would activate different quadrigrams, making them completely different from each other at the lexical level.
• The authors only considered on/off open-bigrams with no encoding of edges. The addition of graded activations and edge bigrams, as in the SERIOL model, allows more accurate encoding of order information.
• However, there is evidence that letter-order encoding on the ventral route is indeed somewhat imprecise, whereas the encoding is more accurate on the dorsal phonological route. Occipito-parietal lesions lead to a selective deficit in encoding letter order (Friedmann & Gvion, 2001; Shalev, Mevorach & Humphryes, in press) . See also Frankish & Turner (2007) . So experimental results indicate that open-bigrams do not need to be "fixed" with quadrigrams.

In contrast, claim (2) above offers a reasonable account of how open-bigrams could be activated within a parallel framework. Grainger et al. (2006) incorporated this suggestion into their Overlap Open-Bigram (OOB) model. Within the abstract open-bigram layer, the OOB model is quite similar to the SERIOL model, in that it employs open-bigrams with graded activation levels. The only difference is that the OOB model includes activation of transpositions (e.g., BIRD would activate bigram RI to a low level). Of course, the two models radically differ on how the open-bigrams become activated, as the SERIOL model proposes that open-bigrams are activated serially. This serial mechanism explains perceptual patterns for consonant strings, while parallel accounts do not. For a detailed comparison of serial versus parallel activation of open-bigrams, see Whitney & Cornelissen (2008).