Development of Visual Expertise for Reading: Rapid Emergence of Visual Familiarity for an Artificial Script

Abstract

Adults produce left-lateralized N170 responses to visual words relative to control stimuli, even within tasks that do not require active reading. This specialization begins in preschoolers as a right-lateralized N170 effect. We investigated whether this developmental shift reflects an early learning phenomenon, such as attaining visual familiarity with a script, by training adults in an artificial script and measuring N170 responses before and afterward. Training enhanced the N170 response, especially over the right hemisphere. This suggests N170 sensitivity to visual familiarity with a script emerges before reading becomes sufficiently automatic to drive left-lateralized effects in a shallow encoding task.

Notes

This work was supported by grants to BDM from the National Science Foundation (REC-0337715) and National Institutes of Health (NIDCD-R01-DC007694) and the Swiss National Science Foundation (Fellowship for Prospective Researchers: UM).

1. The N170 component typically peaks between 150 and 200 msec and shows a topography of a bifocal posterior negativity and of a central positivity. In some ERP studies the N170 is also labeled N1 or N150, or even P150, if referred to the positive pole of the topographic distribution.

2. Including the instruction as between-subject factor (grapheme-phoneme vs. whole-word) in the analyses on ERP training effects yielded no significant modulation of instruction (neither main effects nor interactions in the MANOVAs), as expected considering the implicit nature of the one-back task. Thus, the data were collapsed across the groups in this article to facilitate a more straightforward presentation.

3. It is also possible that some learning accrued at the level of two-letter combinations (bigrams) which would be intermediate to the level of entire word characters and the letter level and below. As the stimulus sets were not designed to exclude this possibility, a post-hoc analysis of bigram overlap between trained and transfer items was conducted. Of the unique bigrams appearing in the training set, 51% also appeared in the transfer set (as opposed to 100% overlap for the letters). Considering each bigram repetition as a unique token, the overlap was 39% (as opposed to 92% for the letters).