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On the basis of a transparent neurocognitive computational model rooted in the principles of the predictive coding framework, we postulated that readers optimize their percept by removing redundant visual signals, allowing them to focus on the informative aspects of the sensory input (i.e., the oPE). Here, we explore alternative oPE implementations, testing whether increased precision by assuming all-or-nothing signaling and more realistic word lexicons results in adequate representations underlying efficient word recognition. We used behavioral and electrophysiological data (i.e., EEG) for model evaluation. More precise oPE representations (i.e., implementing a binary signaling and a frequency-sorted lexicon with the 500 most common five-letter words) explained variance in behavioral responses and electrophysiological data 300 msec after stimulus onset best. The original less-precise oPE representation still best explains early brain activation. This pattern suggests a dynamic adaption of represented visual-orthographic information, where initial graded prediction errors convert into binary representations, allowing accurate retrieval of word meaning. These results offer a neurocognitive plausible account of efficient word recognition, emphasizing visual-orthographic information in the form of prediction error representations central to the transition from perceptual processing to the access of word meaning.<\/jats:p>","DOI":"10.1162\/jocn_a_02301","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T11:23:35Z","timestamp":1737977015000},"page":"1349-1363","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["Specifying Precision in Visual-orthographic Prediction Error Representations for a Better Understanding of Efficient Reading"],"prefix":"10.1162","volume":"37","author":[{"given":"Wanlu","family":"Fu","sequence":"first","affiliation":[{"name":"University of Cologne"}]},{"given":"Benjamin","family":"Gagl","sequence":"additional","affiliation":[{"name":"University of Cologne"}]}],"member":"281","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"2025062017074399600_bib1","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1093\/biomet\/60.2.255","article-title":"Maximum likelihood identification of Gaussian autoregressive moving average models","volume":"60","author":"Akaike","year":"1973","journal-title":"Biometrika"},{"key":"2025062017074399600_bib2","doi-asserted-by":"publisher","first-page":"2960","DOI":"10.1523\/JNEUROSCI.3730-10.2010","article-title":"Stimulus predictability reduces responses in primary visual cortex","volume":"30","author":"Alink","year":"2010","journal-title":"Journal of Neuroscience"},{"key":"2025062017074399600_bib3","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.brainresrev.2006.07.002","article-title":"Interplay between computational models and cognitive electrophysiology in visual word recognition","volume":"53","author":"Barber","year":"2007","journal-title":"Brain Research Reviews"},{"key":"2025062017074399600_bib4","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1506.04967","article-title":"Parsimonious mixed models","author":"Bates","year":"2015","journal-title":"arXiv"},{"key":"2025062017074399600_bib5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v067.i01","article-title":"Fitting linear mixed-effects models using lme4","volume":"67","author":"Bates","year":"2015","journal-title":"Journal of Statistical Software"},{"key":"2025062017074399600_bib6","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.cognition.2014.10.017","article-title":"Effects of prediction and contextual support on lexical processing: Prediction takes precedence","volume":"136","author":"Brothers","year":"2015","journal-title":"Cognition"},{"key":"2025062017074399600_bib7","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1007\/978-3-030-33676-9_29","article-title":"Not just a matter of semantics: The relationship between visual and semantic similarity","volume-title":"Pattern recognition: 41st DAGM German Conference","author":"Brust","year":"2019"},{"key":"2025062017074399600_bib8","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1027\/1618-3169\/a000123","article-title":"The word frequency effect: A review of recent developments and implications for the choice of frequency estimates in German","volume":"58","author":"Brysbaert","year":"2011","journal-title":"Experimental Psychology"},{"key":"2025062017074399600_bib9","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.3389\/fpsyg.2016.01116","article-title":"How many words do we know? 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