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Related Experiment Video

Updated: Jun 23, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

Item performance in visual word recognition.

Arnaud Rey¹, Pierre Courrieu, Florian Schmidt-Weigand

¹CNRS and Université de Provence, Marseille, France. arnaud.rey@univ-provence.fr

Psychonomic Bulletin & Review

|May 20, 2009

Summary

This summary is machine-generated.

This study introduces a statistical method to quantify reproducible variance in psycholinguistic data, essential for computational models. The findings reveal that increasing reproducible variance helps detect new sources of variation.

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Area of Science:

Psycholinguistics
Computational Linguistics
Statistical Modeling

Background:

Factorial designs in psycholinguistics are increasingly supplemented by large-scale databases.
Computational models aim to predict item-level performance, but error variance is a concern.
Quantifying reproducible variance is crucial for evaluating model accuracy.

Purpose of the Study:

To provide a statistical analysis for quantifying error variance in item performance.
To develop practical solutions for estimating reproducible variance in databases.
To test these solutions using a new empirical dataset.

Main Methods:

Developed a simple and tractable statistical analysis for variance decomposition.
Proposed practical solutions for estimating reproducible variance.
Collected and analyzed word identification times from 140 participants on 120 words.

Main Results:

Demonstrated a method to estimate reproducible variance in psycholinguistic data.
Empirically validated the proposed solutions with a novel dataset.
Showed that increased reproducible variance correlates with the identification of new variance sources.

Conclusions:

The proposed statistical approach effectively quantifies reproducible variance.
Accurate estimation of reproducible variance enhances the evaluation of computational models.
Identifying new sources of variance is facilitated by focusing on reproducible variance.