Evidence map›Paper›PMID 40167554›Full record

ArticleEmotion (Washington, D.C.)2025

Affective abstraction predicts variation in alexithymia, depression, and autism spectrum quotient.

Stephanie A Fiedler, Kent M Lee, Erik C Nook, Kristen A Lindquist, Maria Gendron, Ajay B Satpute

Abstract read
In one paragraph

Article in Emotion (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Stephanie A FiedlerDepartment of Psychology, Wayne State University.ORCID 0000-0002-6470-7422
Kent M LeeDepartment of Psychology, Northeastern University.
Erik C NookDepartment of Psychology, Princeton University.
Kristen A LindquistDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill.
Maria GendronDepartment of Psychology, Yale University.
Ajay B SatputeDepartment of Psychology, Northeastern University.

Funding

Advancing understanding of neural representations of threat perception through a novel predictive coding frameworkF32MH122062 · NIMH · NORTHEASTERN UNIVERSITY · PI LEE, KENT M · 2020 to 2022
$201k
National Institutes of Health; National Institute of Mental HealthNational Science Foundation; Division of Behavioral and Cognitive SciencesNIMH NIH HHS F32 MH122062
6 · The paper itself

Abstract

Affective abstraction refers to how people conceptualize affective states in terms of category-level representations that generalize across specific situations (e.g., "fear" as evoked by heights, predators, and haunted houses). Here, we develop a novel task for assessing affective abstraction and test its relations with trait alexithymia, depression, and autism spectrum quotient. In a preregistered online study, participants completed a set of tasks in which they matched a cue image with one of two probe images based on similarity of affective experience. In a discrete emotion version of the task, the cue and target probe matched on a discrete emotion category while controlling for valence. In a valence version of the task, the cue and target probe matched on valence (i.e., pleasantness or unpleasantness). We further varied the degree of abstraction such that some judgments crossed semantic categories (e.g., a house cue with animal probes). Accuracy, as indexed by the proportion of choices that accorded with norms, predicted trait measures of alexithymia, depression, and autism quotient with medium effect sizes. We conducted an integrative data analysis by including data from three other (nonpreregistered) samples (

Indexed as

AffectAffective SymptomsAutism Spectrum DisorderDepressionAdolescentAdultFemaleHumansMaleYoung Adult

Identifiers

PMID40167554
PMCPMC12353660

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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.