Quantifying the Effects of Word Length, Frequency, and Predictability on Dyslexia

TL;DR

This study analyzes how word length, frequency, and predictability affect reading times in dyslexic and typical readers, revealing stronger sensitivities in dyslexics and suggesting targeted intervention strategies.

Contribution

It provides a large-scale quantitative analysis of how specific lexical features influence dyslexic reading costs, highlighting the importance of predictability, length, and frequency.

Findings

All three features affect reading times in both groups.

Dyslexic readers are more sensitive to these features.

Manipulating features reduces the dyslexic-control reading gap by about one third.

Abstract

We ask where, and under what conditions, dyslexic reading costs arise in a large-scale naturalistic reading dataset. Using eye-tracking aligned to word-level features (word length, frequency, and predictability), we model how each feature influences dyslexic time costs. We find that all three features robustly change reading times in both typical and dyslexic readers, and that dyslexic readers show stronger sensitivities to each, especially predictability. Counterfactual manipulations of these features substantially narrow the dyslexic-control gap by about one third, with predictability showing the strongest effect, followed by length and frequency. These patterns align with dyslexia theories that posit heightened demands on linguistic working memory and phonological encoding, and they motivate further work on lexical complexity and parafoveal preview benefits to explain the remaining gap. In short, we quantify when extra dyslexic costs arise, how large they are, and offer actionable guidance for interventions and computational models for dyslexics.