New et al. (2006, PB&R) and Yarkoni et al. (in press, Neuroimage)

A length effect (or lack thereof) has traditionally been used to distinguish between serial and parallel processing. For lexical decision under central presentation, most studies have shown that reaction times are independent of length for words of 4 to 6 letters, leading to the conclusion that letters are processed in parallel. However, examine of a large corpus of data across wide range of lengths (New et al., 2006) revealed a more interesting, complicated picture. When the effects of frequency and neighborhood size are partialled out, length has a facilitative effect for words of 3 to 5 letters (i.e., shorter RTs for longer words), no effect for words of 5 to 8 letters, and an inhibitory effect for words of 8 to 13 letters. These experimental results are shown in blue in the figure below, regraphed from Fig. 2 of New et al. (2006).

It is straightfoward to explain this pattern under the SERIOL model, under the assumption that RT is the sum of two components: (1) the total time that it takes for all of the letters to fire and (2) the time it takes for the lexical network to settle following firing of the last letter. The total firing time is given by Len * firing-time/letter, where the firing-time/letter is assumed to be on the order of 15-20 ms/letter, corresponding to a firing rate of around 60 Hz. It is assumed that the settling function has the shape shown in red above; settling time decreases across increasing word length, and then asymptotes. That is, more letters provide more information, so the lexical network can settle more quickly for longer words. However, there is a limit to how quickly the lexical network can settle, so beyond a certain length the settling function is flat.

The points in green show modeled RT; it is equal to the settling function + length * 20 ms. It is evident that this modeled RT gives an excellent fit to the data. Modeled RT is decreasing across lengths 3 to 5, flat across lengths 5 to 8, and increasing across lengths 8 - 13.

But is there any evidence for the above assumptions? I have previously pointed out that ERP studies by Hauk and Pulvermuller are consistent with these claims. They have shown that increased length initially (~ 100 - 200 ms post-stimulus), leads to increased amplitudes near occipital cortex. Interestingly, this effect is lateralized to the RH, indicating that it is not merely a result of increased visual angle, as such an effect would be symmetric. Rather it is consistent with a serial encoding driven by a retinotopic activation gradient that is strongest over the initial letters (i.e., in the RH). Later on (300+ ms ) increased length leads to decreased amplitude from left posterior cortex. This reduced signal is consistent with the claim of faster lexical settling for longer words. Thus the timing, direction, and location of these effects are consistent with the proposal that longer words cause increased processing time at the letter level, followed by decreased settling time at the lexical level.

A recent fMRI study (Yarkoni et al., in press), has yielded even stronger evidence for the proposed settling function. They had subjects perform text reading under RSVP, and used regression analyses to determine effects of different variables in different brain regions. For the VWFA, they found that the effect of length had a quadratic component. The fitted function decreased across lengths 2 to 7, was fairly flat across lengths 7 to 10, and increased across lengths 10 to 13 (as shown in Fig. 5 of Yarkoni et al.). Thus, the observed effect of length in the VWFA across lengths 2 to 10 is very similar to the proposed settling function above. For very long words (> 10 letters), there is a mismatch between the two functions (i.e., increasing for the quadratic fit vs. flat for the proposed settling function), but the experimental estimate may be unreliable due to the relatively small number of very long words, coupled with the requirement of a quadratic fit.

I'd like to make one other point about the Yarkoni et al. article. In another analysis, they seek to discover whether the encoding in the VWFA is purely orthographic or whether it includes phonological information. They find an effect of phonological neighborhood-size that cannot be reduced to an effect of orthographic neighborhood-size, and conclude that the encoding in the VWFA is partially phonological. However, I would suggest that more precision is required in the statement of the issues and the interpretation of the results.

Due to interactivity between brain regions, an area that initially encodes only orthographic information could later be affected by feedback from a phonological area. Thus the VWFA encoding could well be purely orthographic during initial feedforward processing, and then VWFA activity could be affected later by phonological attributes. Thus the real question is whether the encoding in the VWFA is initially purely orthographic, not whether VWFA activity is ever influenced by phonological variables. The real question cannot be answered by fMRI, due to lack of temporal precision.