Evidence map›Paper›PMID 36443580›Full record

ArticleBehavior research methods2023

Multi-LEX: A database of multi-word frequencies for French and English.

Marjorie Armando, Jonathan Grainger, Stephane Dufau

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Article in Behavior research methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Marjorie ArmandoLaboratoire de Psychologie Cognitive (UMR7290), CNRS & Aix-Marseille Université Case D, 3, place Victor HUGO, 13331, Marseille Cedex 3, France.
Jonathan GraingerLaboratoire de Psychologie Cognitive (UMR7290), CNRS & Aix-Marseille Université Case D, 3, place Victor HUGO, 13331, Marseille Cedex 3, France.ORCID 0000-0002-6737-5646
Stephane DufauLaboratoire de Psychologie Cognitive (UMR7290), CNRS & Aix-Marseille Université Case D, 3, place Victor HUGO, 13331, Marseille Cedex 3, France. stephane.dufau@univ-amu.fr.ORCID 0000-0002-2692-2084

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single-word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with part-of-speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.

Indexed as

LanguagePsycholinguisticsHumansSpeechEnglishFrenchfrequencyGoogle Ngram datasetsPart-Of-Speechvisual multi-word recognition

Identifiers

PMID36443580

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.