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Related Experiment Video

Updated: May 10, 2026

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese
08:08

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

Published on: April 1, 2016

Phi-square Lexical Competition Database (Phi-Lex): an online tool for quantifying auditory and visual lexical

Julia F Strand1

  • 1Department of Psychology, Carleton College, Northfield, MN, 55057, USA, jstrand@carleton.edu.

Behavior Research Methods
|June 12, 2013
PubMed
Summary

Understanding spoken word recognition requires quantifying lexical competition. The new Phi-square Lexical Competition Database (Phi-Lex) provides accessible metrics for auditory and visual word recognition.

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Last Updated: May 10, 2026

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Area of Science:

  • Cognitive Psychology
  • Speech and Hearing Sciences
  • Computational Linguistics

Background:

  • Spoken word recognition involves parallel activation and competition among multiple lexical candidates.
  • Quantifying lexical competition is crucial for understanding recognition processes.
  • Continuous metrics predict recognition better than categorical ones but are computationally intensive.

Purpose of the Study:

  • To introduce the Phi-square Lexical Competition Database (Phi-Lex).
  • To provide accessible, continuous metrics for lexical competition in spoken word recognition.
  • To facilitate research on auditory and visual (lipread) word recognition.

Main Methods:

  • Development of an online, searchable database.
  • Inclusion of multiple metrics for lexical competition.
  • Focus on English words for both auditory and visual recognition.

Main Results:

  • The Phi-Lex database offers readily available metrics for lexical competition.
  • It addresses the computational burden and data access limitations of previous continuous metrics.
  • Provides metrics for both auditory and visual (lipread) spoken word recognition.

Conclusions:

  • The Phi-Lex database simplifies the quantification of lexical competition.
  • It supports further research into the dynamics of spoken word recognition.
  • Enhances accessibility to crucial data for speech processing and cognitive science.