# Data from the Paper Entitled “Application of a Bayesian Approach for Exploring the Impact of Syllable Frequency in Handwritten Picture Naming”

## TL;DR

This paper provides data from an experiment where participants handwrote labels for images, measuring response times and errors.

## Contribution

The study introduces a Bayesian approach to analyze the impact of syllable frequency in handwritten picture naming.

## Key findings

- Response times and production errors were recorded for 150 black-and-white drawings.
- DmDx scripts and data are publicly available for further research and pre-testing.
- The dataset can aid in estimating sample sizes and Bayesian priors.

## Abstract

The data presented here comes from the Perret and Solier (2022) study. 30 participants handwrote labels for 150 black-and-white drawings. The experiment was carried out using the DmDx program. Response times and production errors were the two behavioral reported measures. DmDx scripts and data are available on the OSF platform (DOI: https://doi.org/10.17605/OSF.IO/GAZF3). These data should be useful for pre-testing to explore new hypotheses, as well as for methodological elements (e.g., sample size estimation, estimation of a priori distributions for Bayesian analyses).