Evidence map›Paper›PMID 36002626›Full record

ArticleBehavior research methods2023

Memory effects of semantic attributes: A method of controlling attribute contamination.

C J Brainerd, D M Bialer, M Chang

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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 1 paper.

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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

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

C J BrainerdDepartment of Psychology and Human Neuroscience Institute, Cornell University, G331 MVR Hall, Ithaca, NY, 14853, USA. cb299@cornell.edu.ORCID 0000-0002-8358-7497
D M BialerDepartment of Psychology and Human Neuroscience Institute, Cornell University, G331 MVR Hall, Ithaca, NY, 14853, USA.
M ChangDepartment of Psychology and Human Neuroscience Institute, Cornell University, G331 MVR Hall, Ithaca, NY, 14853, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rating norms for semantic attributes (e.g., concreteness, familiarity, valence) are widely used to study the content that people process as they encode meaningful material. Intensity ratings of individual attributes have been manipulated in numerous experiments with a range of memory paradigms, but those manipulations are contaminated by substantial correlations with the intensity ratings of other attributes. A method of controlling such contamination is needed, which requires a determination of how many distinct attributes there are among the large collection of attributes for which published norms are available. Identification of overlapping words in multiple rating projects yielded a data base containing normed values for each word's perceived intensity (M rating) and ambiguity (rating SD) on 20 different attributes. Principal component analyses then revealed that the intensity space was spanned by just three latent semantic attributes, and the ambiguity space was spanned by five. Psychologically, the big three intensity factors (emotional valence, size, age) were highly interpretable, as were the big five ambiguity factors (discrete emotion, emotional valence, age, meaningfulness, and verbatim memory). We provide a data base of intensity and ambiguity factor scores that can be used to conduct uncontaminated studies of the memory effects of the intensity and ambiguity of latent semantic attributes.

Indexed as

LanguageSemanticsDatabases, FactualEmotionsHumansRecognition, PsychologyAttribute ambiguityAttribute intensityLatent semantic structureSemantic attributesSemantic word norms

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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.