Recent articles by Wimmer & colleagues, and Pitchford & colleagues

In Hawelka and Wimmer (2008, Vision Research), young adult dyslexics and controls performed letter search on 5-letter strings. The target letter appeared prior to the string, and remained visible when the string appeared. Dependent variable was reaction time for detecting a present target. The authors found that the dyslexics were actually faster than the controls (with the same high accuracy) and concluded that "the slow reading speed of German dyslexic readers cannot be traced to inefficient visual processing of letter strings".

However, I would suggest that this conclusion is unwarranted. The task of detecting a letter within a string differs from the visual processing required for reading, where automatic encoding of all of the letters' positions within the string is necessary. Just because dyslexics are as fast as controls at detecting a letter target does not mean that they encode letter order in a normal manner. In fact, when processing requires fast automatic encoding of letter position across the entire string, dyslexics are notably impaired, as found by Hawelka et al. (2005; 2006, Vision Research), Enns, Bryson & Roes (1995, Can Jour of Exp Psych.) and various studies by Valdois and colleagues.

It is of interest to look at the RT patterns of the dyslexics vs controls in this letter search paradigm. Pitchway, Ledgeway and Masterson (in press, QJEP) did so for adult English dyslexias. They found an LVF advantage for controls, but not dyslexics. A similar pattern is also evident in Hawelka and Wimmer's (2008) data - numerically, controls were faster on position 2 than 4, but dyslexics were not. These patterns are consistent with my idea that normal readers perform rapid serial processing of letters of sub-parts of a single object (the string), whereas dyslexics process letters in parallel as individual objects.

The length of the string in these experiments (5 letters) is near the limit (~4) for the number of visual objects that can be processed in parallel. Thus dyslexics do not show increased RTs in the letter search task because they can process the five letters of the string mostly in parallel, but they do show a different RT pattern due to this parallel processing. For longer strings, the difference between the two styles of processing has stronger implications, because the rapid serial processing (at 10-15 ms/letter) allows ~10 letters to be processed per fixation, whereas parallel processing in highly-compensated adult dyslexics is probably restricted to 5 letters max, due to innate limitations on the visual systems' ability to process multiple objects in parallel. This accounts for the slow reading that is characteristic of dyslexic readers in transparent orthographies. (In English, dyslexics would have the same visual limit. Due to the irregularity, they may adopt the approach of processing only the salient letters, and guessing at the word. This yields faster, less accurate reading.)

Bergmann and Wimmer (in press, Cog. Neuropsychology) then examined performance of German dyslexics versus controls on lexical decision (LD) versus pseudohomophone decision (PD). (In PD, the answer is "yes" if the pronunciation of a pseudoword is a word, e.g., yes for "taksi", no for "tazi"). Looking at accuracy, they found that dyslexics were only slightly impaired (with respect to controls) for PD, but were highly impaired on LD. In fact, controls were better at LD than PD, while dyslexics were better at PD than LD. For RT, dyslexics were considerably slower than controls on both tasks.

This provides yet more evidence that, universally, the characteristic pattern of dyslexia is a limitation in the uptake of orthographic information, rather than a phonological deficit. However, predicated on their presumption that there is no deficit in the dyslexics' visual processing of strings, the authors place the dyslexics' deficits in three places: poor representations of orthographic word forms, slow connections between orthographic word forms and phonological word forms and slow connections between graphemes and phonemes.

But the data are explained more compactly via the proposal of abnormal, parallel encoding of letters as individual objects. This limits the number of letters that can be processed within a fixation. Furthermore, parallel processing probably also slows down grapheme-phoneme mapping within a fixation, as such translation likely functions more automatically under seriality. Both of these factors yield increased RTs. The parallel processing also prohibits the encoding of a string as a single object, which precludes normal representation of orthographic word forms, yielding poor LD performance.

SSSR meeting

Recently got back from the Society for the Scientific Study of Reading conference, where I chaired a symposium, with Nikki Pitchford and Daisy Powell, on orthographic learning. We were heartened that there seems to be an increasing openness to the importance of visual/orthographic processing in reading, and felt that the symposium was well received.

Nikki gave a talk on RT patterns for letter search in 5-letters arrays in English, English dyslexic, and Greek readers. Her Greek data have caused me to reconsider my explanation of the final-letter effect somewhat, which I'll address in a subsequent post.

Daisy discussed experiments with poor readers without phonological deficits but with Rapid Automatized Naming deficits. These subjects had poorer orthographic knowledge than controls in general, but actually out-performed controls on orthographic learning in Share's self-teaching task. (In this task, pseudowords are included in passages read by subjects, and the subjects are later tested on the spelling of the pseudowords.) These were four-letter pseudowords. It would be interesting to try the experiment with longer pseudowords, as I think that four letters can be processed in parallel by dyslexics, and whereas processing should particularly break down on longer words. So the poor readers may depend on visual information more than the controls, and be capable of remembering this visual information better than controls for strings up to four letters.

Sylviane presented longitudinal data showing that her Visual Attention Span measure (the number of letters than can be reported following brief presentation of five letters) is predictive of reading achievement. Sylviane and I are both interested in gaining a better understanding of whether this task measures a general deficit in the ability to distribute visual attention across multiple objects, or is more specific to learned orthographic processing. As I mentioned in a previous post, it may well measure both, and a deficit may arise at different levels in different subjects.

Piers wowed everyone with MEG data showing early (~100 ms post-target) phonological priming in IFG and precentral gyrus.

I harped on my favorite subject - perceptual patterns for identification of briefly presented strings, and suggested that the trigram identification task could be used to measure whether normal visual/orthographic processing has been learned. In particular, the SERIOL model predicts that, at a given eccentricity, increased letter position within a string should have a much larger detrimental effect on accuracy in the LVF than the RVF for normal readers. Thus they should show an VF asymmetry on the effect of string position. If normal string processing has not been learned, the pattern should be symmetric, with little effect of string position in either VF. Data from 1 seventh-grade dyslexic and 7 age-matched controls, from Dubois et al. (2007), support this proposal, as discussed in this post. Clearly "more research is required".

Dubois et al. (2007) in Cognitive Psychology

The authors look at perceptual patterns for a seventh-grade French dyslexic, MT, who has no phonological deficit. In particular, they look at trigram identification across a range of retinal locations (centered from -7 to 7), for MT versus seven age-matched controls. The authors fit curves to the trigram data, and did not find any difference between MT and the controls.

However, the SERIOL model makes quite specific predictions of how perceptual patterns should differ between dyslexics and controls, which the authors did not evaluate. The model predicts that a letter's position within the string should have a much stronger influence in the LVF than the RVF. This is due to the proposal of learned left-to-right inhibition in the LVF/RH. For younger readers, this effect should be strongest near fixation, where perceptual learning is the strongest. For example, accuracy for a letter at retinal location -2 should be much better when it is the 1st letter in the string than when it is the 3rd letter. In contrast, accuracy for a letter at retinal location 2 should be minimally affected by its position within the string. This asymmetry should be a signature of normal visual/orthographic processing, and it should be absent for dyslexics, under the assumption that they are not performing normal visual processing.


Indeed, inspection of the data in Figure 6, shown above, supports this prediction. In this figure, a filled circle represents the 1st letter in the LVF and the 3rd letter in the RVF. Conversely, an open circle represents the 3rd letter in the LVF and 1st letter in the RVF. For controls for eccentricities of 1 to 3 letter widths, it is evident that string position had a strong effect in the LVF, but not the RVF, while the pattern was symmetric for MT. Examination of the individual data shows that the asymmetric pattern held at the individual level.

Of course, this is a very small sample size. I would suggest that it is important to try this experiment on a large group of school-age controls and dyslexics to see how diagnostic this asymmetric vs. symmetric pattern truly is. If it is highly diagnostic, this would be quite informative as to the nature of core deficits in developmental dyslexia.

Martin et al. (2007) in Brain Research

In this study, the subjects performed the Reicher-Wheeler task on five-letter words versus unpronounceable nonwords, for exposure durations of 50 and 66 ms. The stimuli were presented so that the target letter always occured at fixation. So for example, when the target was the second letter, the second letter appeared at fixation, putting the first letter in the LVF and the third to fifth letters in the RVF. Thus the retinal location and visual acuity of the target letter did not vary with its position within the string. The task was performed by adult unimpaired readers and dyslexics.

This provides an opportunity to look at how accuracy interacts with string position and exposure duration. First we consider unimpaired readers. Under the assumption of serial processing, some letters may be read out before the mask occurs, and others will be read out after the mask occurs. The latter letters should be at a disadvantage. In general, the SERIOL model predicts that an increase in exposure duration should have the strongest effect at string positions in the transition zone (i.e., letters that were formerly read out after the mask occurred, but now are read out before the mask.) In this experiment, the change in exposure duration was 16ms, which is on the time scale proposed for per-letter processing. So at first glance, this suggests that early string positions should not be affected by an increase in exposure (because they are read out before the mask in any case) and later string positions should also not be affected (because they are read out after the mask in any case), while a transitional position should be affected. Here are the results from the experiment:

The nonword results for control subjects show an asymmetric effect of increased exposure, with the largest improvement for position 1, and no improvement at positions 4 and 5. This pattern is difficult to explain under parallel processing, but does not exactly match the SERIOL intuition that the improvement should be localized at the position that was not read out at 50 ms, but was read out at 66ms.

However, let's consider the mechanics in more detail. Due to strong bottom-up activation in the LVF/RH, an increase in string position at fixation will not necessarily cause that letter to be read out (at the letter level) a full "time slot" (~15 ms) later, because the additional LVF/RH letters in earlier positions can "fill in" earlier time periods. That is, at the feature level, an initial letter at -1 reaches a higher activation than an initial letter at 0 (fixation). Hence, for a letter at fixation, activations (at the feature level) are similar for position 1 versus position 2. Therefore the timing of activation at the letter level does not vary much either. This is illustrated in the following figure, which shows the proposed time that a letter starts firing at the letter level, based on its retinal location and string position. It shows how each increase in string position from 1 to 3 at fixation could delay firing by ~5 ms, rather than ~15 ms. There is a much larger difference going from position 3 to 4 under the assumption that an initial letter at -3 is too far from fixation to reach maximal activation at the feature level; the reduced activation level then percolates through the string, due to RH-left-to-right and cross-hemispheric lateral inhibition.


Under this account, for the 50 ms exposure, the letter at fixation does not fire before the mask appears. For a 67 ms exposure, the letter at fixation can start to fire before the mask when it is in positions 1, 2, or 3. This explains the observed interaction of increased exposure with string position. (However, this doesn't explain why having the third letter at fixation yields the poorest results overall. This may be due to greater positional uncertainty about the middle letter.)

It is also interesting that there was no or a very weak initial-letter advantage in the nonword data. This is consistent with the idea that the initial-letter advantage is essentially a LVF non-initial-letter disadvantage. That is, when a second letter falls in the LVF, it receives much more additional lateral inhibition (at the feature level) than when it is the first letter in the LVF. In contrast, when a second-letter falls at fixation, it only receives slightly more lateral inhibition than when it is the first letter. Thus the advantage for being the first letter is much reduced at fixation, compared to retinal locations in the LVF. In contrast, Tydat and Grainger (in press, JEP:HPP) claim that the initial-letter advantage is due to reduced receptive-field sizes for letters, such that a letter receives considerably less inhibition with 1 immediate flanker than with 2 flankers. This account incorrectly predicts that an initial-letter advantage should be present at fixation.

Note that the best overall firing patterns are obtained when fixation falls on the second or third letter. That is, these conditions allow the earliest completion of letter readout. This explains the OVP effect observed in the word conditions. Thus the word and nonword conditions yield different patterns, with fixation on the third letter yielding the poorest results for nonwords, but the best results for words. This is because accuracy in the word condition is influenced by the processing of the entire string (to yield lexical activation), which is best at positions 2 and 3, while accuracy in the nonword condition is influenced primarily by the processing and localization of the target letter.

It is also interesting to see that the dyslexics showed a different pattern. First, there was no position X exposure-duration interaction. This is consistent with my proposal that dyslexics process letters in parallel, while unimpaired readers process them serially. Secondly, there was no word-superiority effect, except when fixation fell on the third letter. This may indicate that these dyslexics use a retinotopic method to encode letter position, which is keyed to having two letters in the LVF. When the presentation condition matches this requirement, lexical representations are well activated; otherwise they are not. This is consistent with the case study of a single French dyslexic (Dubois et al., 2007, Cognitive Neuropsychology), which showed that lexical recognition was best when fixation fell on the third letter, independently of string length.