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Related Experiment Video

Updated: Jul 19, 2026

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Published on: April 1, 2016

Regularity effects in word naming: what are they?

M J Cortese¹, G B Simpson

¹Department of Psychology, Morehead State University, 601 Ginger Hall, Morehead, KY 40351, USA. m.cortese@morehead-st.edu

Memory & Cognition

|February 28, 2001

Summary

This study on word recognition found that word-body consistency significantly impacts naming speed and accuracy. Human data best aligns with the parallel-distributed-processing model over other computational models.

Area of Science:

Cognitive Psychology
Computational Linguistics
Neuroscience

Background:

Current models of word recognition aim to explain how humans process written language.
Understanding the interplay between word form and sound representation is crucial for refining these models.

Purpose of the Study:

To investigate the roles of word-body consistency and grapheme-to-phoneme regularity in word recognition.
To compare the predictive accuracy of three prominent computational models of word recognition against human behavioral data.

Main Methods:

A word-naming experiment was conducted, manipulating word-body consistency and grapheme-to-phoneme regularity.
Behavioral data (latency and error rates) were collected from human participants.
Simulations were run using three computational models (parallel-distributed-processing, dual-process, dual-route-cascaded) with the same experimental stimuli.

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Main Results:

Human word recognition was significantly influenced by word-body consistency, with a weaker effect from grapheme-to-phoneme regularity.
Simulation results indicated that human response latencies were most consistent with the parallel-distributed-processing model.
The dual-process and dual-route-cascaded models showed less consistency with the observed human data.

Conclusions:

Word-body consistency plays a more dominant role than grapheme-to-phoneme regularity in human word recognition.
The parallel-distributed-processing model provides a superior account of human word recognition processes compared to the dual-process and dual-route-cascaded models.