Abstract
The efficiency of reading, to a crucial extent, results from the fact that visual word recognition is faster in predictive contexts (like sentences or texts). This observation is consistent with predictive coding models, but it is so far not sufficiently understoo d which aspects of the rich set of linguistic representations that is activated during reading contribute to this context-dependent facilitation. Candidate representations are visual, orthographic, phonological, and/or lexical-semantic in nature. Our study investigates which representations contribute to efficient word recognition in predictive context with a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), that was combined with magnetoencephalography (MEG) measurements in human participants (both sexes). We used high-powered linear mixed modeling to test the hypothesis that context-dependent facilitation relies on the pre-activation of linguistic representations prior to perceiving the expected stimulus. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical -semantic, but not visual or phonological processes. MEG data from 38 participants show sustained activation of orthographic and lexical-semantic codes in the interval between prime and target, i.e., before processing the predicted stimulus. Also, we found a positive correlation between these pre-activation effects and brain responses elicited when processing the expected letter string. Pre-activation was more stable across time in words than in pseudowords. These results suggest that readers use orthographic and lexical-semantic representations to actively predict upcoming words and that this predictive proces s is modulated by the presence of prior knowledge (in words as opposed to pseudowords).
Significance Statement Visual word recognition requires the efficient transformation of visual input, via orthographic and phonological representations, into meaning. During reading, visual word recognition benefits strongly from predictive contexts like sentences or texts, presumably due to active prediction-based processes. Combining magnetoencephalography with high-powered linear mixed model regression analysis, we provide evidence that orthographic and lexical-semantic (but not visual or phonological) linguistic representations are pre-activated prior to actually perceiving expected words. More sustained pre-activation is observed for words than for pseudowords, i.e., when prior knowledge about the expected stimulus is available. Our findings clarify the neuronal mechanisms underlying active prediction of expected words in predictive context, which describes an important facet of efficient reading.
Competing Interest Statement
The authors have declared no competing interest.