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.
Highlights
The comprehension of written sentences consists of a multitude of low-level and high-level cognitive processes
Previous research has led to two main approaches for quantifying complexity: in terms of syntactic complexity (SC), which refers to a set of measures based on hierarchical dependency structures (e.g., [1,2]), and in terms of transitional probability (TP), which refers to a class of information-theoretical metrics concerning probabilistic patterns of co-occurrence of linguistic units (e.g., [3,4])
The results showed that reading individual words in the electrophysiological study elicited N400 components that were strongly correlated with levels of surprisal
Summary
The comprehension of written sentences consists of a multitude of low-level and high-level cognitive processes. The complexity of a sentence influences the speed with which it is read: Complex sentences are read more slowly than less complex sentences. An important topic in reading research has been the operationalization of sentential complexity. Previous research has led to two main approaches for quantifying complexity: in terms of syntactic complexity (SC), which refers to a set of measures based on hierarchical dependency structures (e.g., [1,2]), and in terms of transitional probability (TP), which refers to a class of information-theoretical metrics concerning probabilistic patterns of co-occurrence of linguistic units (e.g., [3,4]). Previous empirical reports have provided mixed evidence with regard to the importance of SC and TP in predicting sentence reading speed
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