Reverse wrap-up effects by reading scenario, boundary salience, and word position: Novel evidence from eye movements in natural Chinese reading
Year: 2026
Type: article
Abstract: This study investigates how wrap-up effects-sentence-final words incur heavier processing loads than sentence-internal words-manifest in natural reading of unspaced, logographic Chinese scripts. We leveraged a large-scale naturalistic reading corpus (subjects: 98; word tokens: more than 1 million by 300 individual sentences and seven passages), employed multifactorial analyses by (generalized) linear mixed-effects models, and compared four eye-movement differences (i.e., gaze duration, total reading time, skipping probability and regression-in probability) between sentence-internal and sentence-final words. The results demonstrated robust reversal of traditional wrap-up effects: sentence-final words required significantly less processing effort than sentence-internal words, such as shorter durations, higher skipping rates and lower regression-in probabilities. Further, this reversal was modulated by reading scenarios (sentence vs. passage), boundary salience (period- vs. comma-bounded), and wrap-up positions (pre-critical, critical, and spill-over). Notably, sentence-final words were processed more rapidly when associated with characteristics such as fewer stroke counts, shorter length, higher frequency, function-word status, or progress further into page at the late processing stage. Challenging classic models that attribute inflated time at clause/sentence boundaries to semantic integration, we postulate punctuation's dual role in unspaced language processing: visual cues for word segmentation/recognition and semantic cues for integration jointly optimize language comprehension.
Source: Psychonomic Bulletin & Review
Authors: Yushu Wu, Chunyu Kit
Institution: City University of Hong Kong
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,790
Awards: G2423877823, G3281206726, G4166641879
OpenAlex ID: W7130316678
