Abstract
Previously reported simulations using the E-Z Reader model of eye-movement control suggest that the patterns of eye movements observed with children versus adult readers reflect differences in lexical processing proficiency. However, these simulations fail to specify precisely what aspect(s) of lexical processing (e.g., orthographic processing) account for the concurrent changes in eye movements and reading skill. To examine this issue, the E-Z Reader model was first used to simulate the aggregate eye-movement data from 15 adults and 75 children to replicate the finding that gross differences in reading skill can be accounted for by differences in lexical processing proficiency. The model was then used to simulate the eye-movement data of individual children so that the best-fitting lexical processing parameters could be correlated to measures of orthographic knowledge, phonological processing skill, sentence comprehension, and general intelligence. These analyses suggest that orthographic knowledge accounts for variance in the eye-movement measures that is observed with between-individual differences in reading skill. The theoretical implications of this conclusion will be discussed in relation to computational models of reading and our understanding of reading skill development.
The work reported in this article was completed while the first author was a visiting member of the Center for Vision and Cognition at the University of Southampton, UK, and was supported by Region Rhône-Alpes, France, ARC 6, and by Laboratoire de Psychologie et NeuroCognition, Université Pierre Mendes, France. We also thank the University of Southampton's IRIDIS High Performance Computing Facility for the computational resources used in completing the simulations reported in this article and Gelu Ionescu for developing the fixation-realignment software that was used to analyse the data from our experiment.
No potential conflict of interest was reported by the authors.
The work was also supported by a National Institute of Health grant [grant number HD075800] awarded to the second author and by a grant from the Agence Nationale de la Recherche [ANR-12-BSH2-0013-01] awarded to Sylviane Valdois.
Notes
1 These tests were developed as part of the Ortholearn project, which was funded by the French Research National Agency [ANR-12-BSHS2-0013]; for more information about the tests, contact Sylviane Valdois ([email protected]).
2 This fixation realignment software was developed by Gelu Ionescu ([email protected]).
3 Because of the fixations preceding and following return sweeps from one line of text to the next are not representative of other fixations (and thus outside of the theoretical scope of the E-Z Reader model), these words were excluded from our analyses and simulations.
4 The durations of all processes except the eye–brain lag (V in ) and saccade execution (S in ) are random deviates that are sampled from gamma distributions having means specified by the model's equations and standard deviations of 0.22 of the mean.
5 This normative study used eight adults and eight children who did not participate in our experiment (but who were selected from the same population) to determine the mean cloze-predictability values for each word in our text.
6 Analyses using the full set of children (i.e., N = 75) produced a similar pattern of results, with the only notable differences being that phonological processing skill was also related to the best-fitting values of α1 (r = –.36; p < .001) and the two fixation-duration measures (FFDs: r = –.32; p < .05; GDs: r = –.40; p < .001).
7 See foot note 3.
8 The Über-Reader model that is currently being developed provides one concrete example of such a model; however, even a brief description of this model is beyond the scope of what can be addressed in this article (see Reichle, Citationin press).