Evidence map›Paper›PMID 41975152›Full record

ArticleBehavior research methods2026

Child's play: A computational method for generating child-specific valence norms.

Katharina Gloria Hugentobler, Astrid Haase, Jana Lüdtke, Sascha Schroeder

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Article in Behavior research methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Katharina Gloria HugentoblerEducational Psychology, Georg-August-Universität Göttingen, Waldweg 26, Göttingen, 37073, Germany. hugentobler@zedat.fu-berlin.de.ORCID https://orcid.org/0000-0002-3551-3135
Astrid HaaseEducational Psychology, Georg-August-Universität Göttingen, Waldweg 26, Göttingen, 37073, Germany.ORCID https://orcid.org/0000-0003-2434-5858
Jana LüdtkeExperimental and Cognitive Neuropsychology, Freie Universität Berlin, Halbelschwerdter Allee 45, Berlin, 14195, Germany.ORCID https://orcid.org/0000-0002-1581-6120
Sascha SchroederEducational Psychology, Georg-August-Universität Göttingen, Waldweg 26, Göttingen, 37073, Germany.ORCID https://orcid.org/0000-0001-7001-4588

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on affective processing depends on the availability of word norms. Norms for adults are widely available and can be computed for whole-language vocabularies by mapping semantic similarities from a semantic vector space to a list of label words. While such methods are abundantly studied for adults, comparable methodologies for children are few and far between. In this article, we explored whether computational methods can be used to estimate word norms for children using valence as an example. We systematically investigated the impact of two main methodological choices relevant for the computation: the source of the semantic space used to measure semantic distance and the selection of the words in the label list. In Study 1, we correlated the computational estimates from all combinations with valence ratings from adults and children. Our results indicate that norms derived from adult vector space models and label lists outperform other combinations. Norms computed based on these parameters correlate with children's ratings at

Indexed as

VocabularyAdultChildFemaleHumansLanguageMaleSemanticsAffective normsLexical decision taskValence ratingsVector space models

Identifiers

PMID41975152
PMCPMC13076402

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