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. 2014 Feb:129:7-13.
doi: 10.1016/j.bandl.2013.11.005. Epub 2014 Jan 24.

Reading faces: investigating the use of a novel face-based orthography in acquired alexia

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Reading faces: investigating the use of a novel face-based orthography in acquired alexia

Michelle W Moore et al. Brain Lang. 2014 Feb.

Abstract

Skilled visual word recognition is thought to rely upon a particular region within the left fusiform gyrus, the visual word form area (VWFA). We investigated whether an individual (AA1) with pure alexia resulting from acquired damage to the VWFA territory could learn an alphabetic "FaceFont" orthography, in which faces rather than typical letter-like units are used to represent phonemes. FaceFont was designed to distinguish between perceptual versus phonological influences on the VWFA. AA1 was unable to learn more than five face-phoneme mappings, performing well below that of controls. AA1 succeeded, however, in learning and using a proto-syllabary comprising 15 face-syllable mappings. These results suggest that the VWFA provides a "linguistic bridge" into left hemisphere speech and language regions, irrespective of the perceptual characteristics of a written language. They also suggest that some individuals may be able to acquire a non-alphabetic writing system more readily than an alphabetic writing system.

Keywords: Acquired alexia; Dyslexia; Orthography; Phonology; Reading; VWFA; Word identification.

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Figures

Fig. 1
Fig. 1
Examples of FaceFont phonograms and presentation of pure alexia in AA1. (a) Left: A sample of five FaceFont phonograms, their corresponding English phonemes (represented using the International Phonetic Alphabet), and examples of the sounds in English words. Right: An example of the word `gate' shown in FaceFont. (b) Typical location of the VWFA (red sphere) on a reference brain (left image). The structural image from AA1 (acquired during her initial hospital admission) reveals damage to the left occipotemporal cortex that includes the typical VWFA territory (right image). (c) Letter-by-letter reading observed in AA1, as demonstrated by a steep, linear increase in reading latencies as words increase in length, as compared to the average reading latencies of four controls. Error bars (representing standard deviations) reflect relatively little variance amongst the control participants when contrasted with AA1's reading latencies. Note: 4.81% of all trials were excluded in which the microphone did not register the response latencies. Latencies were reported for correct pronunciations only. See Table S3 for accuracy results.
Fig. 2
Fig. 2
FaceFont and FaceSyllable phonogram learning. AA1 was able to learn the initial set of five FaceFont phonograms (Set A) but not the second set (Set B). (a1) AA1's average accuracy across all FaceFontA recall attempts was only 0.22 SDs below the control mean, but her average accuracy across all FaceFontB recall attempts was 5.3 SDs below the control mean. (a2) In the FaceFont protocol, AA1 reached the early advancement criterion for FaceFontA (100% accuracy on two consecutive recall attempts). In contrast, for FaceFontB she reached the maximum recall attempts allowed without scoring above an 80.00%. AA1 was able to learn all three sets of five FaceSyllable phonograms. (b1) AA1's average accuracy across all recall attempts for each FaceSyllable set was 0.46 SDs below (FaceSyllableA), 0.02 SDs above (FaceSyllableB), and 6.51 SDs below (FaceSyllableC) the control mean. (b2) In contrast with the FaceFont protocol, AA1 reached 100.00% accuracy on all three FaceSyllable sets after nine or fewer recall attempts. Error bars in (a1, b1) represent standard deviations.
Fig. 3
Fig. 3
FaceFont and FaceSyllable decoding. (a) Performance on FaceFont decoding of nonwords and real words; AA1 performed 2.13 and 3.44 SDs below the control mean, respectively. (b1) AA1 had improved performance on FaceSyllable decoding; she scored identically to the control mean on FaceSyllable nonwords and 0.71 SDs above the control mean on real word items. (b2) FaceSyllable decoding in the final training stage with all 15 phonograms and only real words presented. AA1 continued to have improved performance compared to FaceFont decoding. She scored 1.15 SDs below the control mean on both imprecise and precise words. Additional scoring details are provided in Table S4.

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