# On the Relationship between Reading Abilities and Word Properties Involved in Word Recognition

## TL;DR

This study explores how reading skills and word features like frequency and length influence word recognition using a lexical decision task.

## Contribution

The study introduces a data-driven approach to model how reading expertise interacts with word properties in word recognition.

## Key findings

- More expert readers are less affected by word length and frequency in word recognition tasks.
- The best model excluded interactions between semantic neighborhood density and reading skills.
- Both lexical properties and individual reading proficiency are important in understanding word recognition.

## Abstract

Word recognition is a complex cognitive process that has been often investigated via lexical decision task (LDT). LDT can indeed provide insight into how individuals access and process linguistic information, and how (and if) specific word- and/or individual-level characteristics affect participants’ behavior. Here, we aimed to provide a systematic investigation of the interaction between individual-level reading skills and word-level factors (e.g., frequency, length). Participants were asked to perform a LDT and complete neuropsychological tests assessing their reading-related skills. By using completely data-driven approaches, participants’ performance in the LDT was predicted by word- and individual-level predictors, and the best-fitting model was selected. The best-fitting model dropped all the interactions among deeper-level predictors (e.g., density of the semantic neighborhood) and reading-related skills. The interactions involving word length or word frequency indicated that more expert readers are less sensitive to this kind of factors. These results underscore the importance of considering both lexical properties and individual reading proficiency when investigating the cognitive mechanisms underlying word recognition.