Overlap model - Gomez, Ratcliff & Perea (in press) in Psychological Review

In this paper, the authors introduce the Overlap Model of letter-position encoding, which is essentially a sloppy slot-based model. For example, an L in the third position would activate an encoding for L in the third position, as well L in the second and fourth positions, to a lesser degree. The authors present a series of experiments to determine parameter settings for the model, which govern the amount of spread for each letter position. In the experiments, a five-letter string was presented for 60ms, followed by a mask and 2AFC. The choices were strings, and the way in which the distractor differed from the target was systematically varied. Note also, that the choices were presented well below where the string occurred, so they did not act as an additional backward mask.

From my point of view, the most interesting aspect of this paper is the finding that the final letter was the least well recognized and localized. In contrast, at longer exposures (>= 100 ms), the usual final-letter advantage can be observed. This contrast is consistent with serial processing and the resulting account of the final-letter advantage, and is difficult to explain otherwise.

However, as a model of letter-position encoding, the Overlap Model faces some difficulties.

1. It is not a full model, as it does not explain how the positions are computed. How is the retinotopic representation transformed into a string-centered positional representation?
2. It cannot explain the finding that, for nine-letter words, the prime 6789 provides facilitation (Grainger et al., 2006 in JEP:HPP). Even with a sloppy position encoding, there would be too much difference between the letter's positions in the prime and target to provide any overlap. Nor could the model be modified to include a position encoding anchored at the final letter; their experiments do not support the existence of such an encoding, as the final letter was the least well anchored/localized (in contrast to the initial letter, which was the best anchored/localized).
   

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.