Evidence map›Paper›PMID 39514144›Full record

ArticleCognitive processing2025

Specificity ratings for English data.

Andrea Amelio Ravelli, Marianna Marcella Bolognesi, Tommaso Caselli

Abstract read
In one paragraph

Article in Cognitive processing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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.

Andrea Amelio RavelliABSTRACTION Research Group, Department of Modern Languages, Literatures and Cultures, University of Bologna, Via Cartoleria 5, 40124, Bologna, BO, Italy.
Marianna Marcella BolognesiABSTRACTION Research Group, Department of Modern Languages, Literatures and Cultures, University of Bologna, Via Cartoleria 5, 40124, Bologna, BO, Italy.ORCID http://orcid.org/0000-0002-3292-8968
Tommaso CaselliCenter for Language and Cognition, Faculty of Arts, University of Groeningen, Oude Kijk in 't Jatstraat 26, 9712 EK, Groningen, The Netherlands. t.caselli@rug.nl.

Funding

HORIZON EUROPE European Research Council 101039777
6 · The paper itself

Abstract

A dataset of specificity ratings for English words is hereby presented, analyzed and discussed in relation with other collections of speaker-generated ratings, including concreteness. Both, specificity and concreteness are analyzed in their ability to explain decision latencies in lexical and semantic tasks, showing important individual contributions. Specificity ratings are collected through best-worst scaling method on the words included in the ANEW dataset (Bradley and Lang in Affective norms for English words (ANEW): instruction manual and affective ratings (Tech. Rep.). Technical report C-1, the center for research in psychophysiology, 1999), chosen for its compatibility with many other collections of rating resources, and for its comparability with Italian specificity data (Bolognesi and Caselli in Behav Res Methods 55(7):3531-3548, 2023), allowing for cross-linguistic comparisons. Results suggest that specificity plays an important role in word processing and the importance of taking specificity into consideration when investigating concreteness effects.

Indexed as

LanguagePsycholinguisticsSemanticsVocabularyFemaleHumansMaleAbstractionBest–worst-scalingCategorizationConcretenessCross-language comparisonHuman ratingsSpecificity

Identifiers

PMID39514144
PMCPMC12055903

What OpenQuestion holds

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LicenceCC BY
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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.