ArticlePloS one2021
Comparing predictors of sentence self-paced reading times: Syntactic complexity versus transitional probability metrics.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Global measures of syntactic and lexical complexity are not strong predictors of eye-movement patterns in sentence and passage reading.Quarterly journal of experimental psychology (2006) · 2025Article
- Whole-Brain Dimensions of Intrinsic Connectivity Capture Modality-Specific and Heteromodal Language Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- What's Surprising About Surprisal.Computational brain & behavior · 2025Article
- IDLaS-NL - A platform for running customized studies on individual differences in Dutch language skills via the Internet.Behavior research methods · 2024Article
- Individual word representations dissociate from linguistic context along a cortical unimodal to heteromodal gradient.Human brain mapping · 2024Article
- Naturalistic Spoken Language Comprehension Is Supported by Alpha and Beta Oscillations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Individual Differences in Holistic and Compositional Language Processing.Journal of cognition · 2023Article
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2 authors.
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Abstract
When estimating the influence of sentence complexity on reading, researchers typically opt for one of two main approaches: Measuring syntactic complexity (SC) or transitional probability (TP). Comparisons of the predictive power of both approaches have yielded mixed results. To address this inconsistency, we conducted a self-paced reading experiment. Participants read sentences of varying syntactic complexity. From two alternatives, we selected the set of SC and TP measures, respectively, that provided the best fit to the self-paced reading data. We then compared the contributions of the SC and TP measures to self-paced reading times when entered into the same model. Our results showed that while both measures explained significant portions of variance in reading times (over and above control variables: word/sentence length, word frequency and word position) when included in independent models, their contributions changed drastically when SC and TP were entered into the same model. Specifically, we only observed significant effects of TP. We conclude that in our experiment the control variables explained the bulk of variance. When comparing the small effects of SC and TP, the effects of TP appear to be more robust.
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