Evidence map›Paper›PMID 41523094›Full record

ArticleJournal of cognition2026

Updating the German Psycholinguistic Word Toolbox with AI-Generated Estimates of Concreteness, Valence, Arousal, Age of Acquisition, and Familiarity.

Javier Conde, Gonzalo Martínez, María Grandury, Carlos Arriaga, Juan Haro, Sascha Schroeder, Florian Hintz, Pedro Reviriego, Marc Brysbaert

Abstract read
In one paragraph

Article in Journal of cognition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Javier CondeInformation Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, Spain.ORCID https://orcid.org/0000-0002-5304-0626
Gonzalo MartínezInformation Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, Spain.ORCID https://orcid.org/0000-0002-9125-6225
María GranduryInformation Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, Spain.ORCID https://orcid.org/0009-0009-4703-3348
Carlos ArriagaInformation Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, Spain.ORCID https://orcid.org/0000-0002-0513-2550
Juan HaroUniversitat Rovira I Virgili. Departament of Psychology and CRAMC, Spain.ORCID https://orcid.org/0000-0002-3456-4731
Sascha SchroederInstitute of Psychology, University of Göttingen, Germany.ORCID https://orcid.org/0000-0001-7001-4588
Florian HintzResearch Center Deutscher Sprachatlas, Marburg University, Germany.ORCID https://orcid.org/0000-0002-2444-3303
Pedro ReviriegoInformation Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, Spain.ORCID https://orcid.org/0000-0003-2540-5234
Marc BrysbaertDepartment of Experimental Psychology, Ghent University, Belgium.ORCID https://orcid.org/0000-0002-3645-3189

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents AI-generated estimates for five characteristics of German words: concreteness, valence, arousal, age of acquisition (AoA), and word familiarity. The estimates were generated using GPT-4o-mini, which was selected due to its good performance in previous studies. Validation studies were conducted comparing the AI-generated estimates with both human ratings and previously generated AI data to ensure their usefulness for research applications. The main results are as follows. The GPT estimates of word concreteness, valence, and arousal show a strong correlation with human ratings but are not better than the best available AI-generated estimates based on semantic vectors. The GPT estimates of AoA are good approximations of human ratings and outperform other available alternatives (except for human ratings), especially after the model was fine-tuned based on 2,000 human ratings. Fine-tuned AI-generated estimates of word familiarity have better predictive value than word frequency for word recognition in lexical decision tasks and vocabulary tests. Estimates for concreteness, valence, arousal, and AoA are available for 167,000 words, which are likely to be known to more than 90% of participants in typical adult studies. Word familiarity estimates are presented for 928,000 word forms. All data and codes, including newly collected human familiarity ratings for 11,000 words, are publicly available at https://osf.io/ghjd2/. The data may be freely used for research purposes, but not for commercial purposes.

Indexed as

age of acquisitionAI-generated word normsarousalconcretenessfamiliarityGerman languagevalence

Identifiers

PMID41523094
PMCPMC12785658

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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.