Evidence map›Paper›PMID 42591228›Full record

ArticleFrontiers in psychology2026

Quantifying spectral information and redundancy in high-dimensional personality taxonomies: an entropy-based approach.

Fernando Gutiérrez, Anton Aluja, José Ruiz-Rodríguez, Luis F García, Eva Baillès, Natalia Calvo, Claudia Rodríguez, Miguel Gárriz

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Article in Frontiers in psychology, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Fernando GutiérrezInstitute of Neuroscience, Hospital Clínic of Barcelona, Barcelona, Spain.
Anton AlujaLleida Institute for Biomedical Research Dr. Pifarré Foundation, Lleida, Spain.
José Ruiz-RodríguezDepartment of Clinical Psychology and Psychobiology, University of Barcelona (UB), Barcelona, Spain.
Luis F GarcíaLleida Institute for Biomedical Research Dr. Pifarré Foundation, Lleida, Spain.
Eva BaillèsConsultation Liaison Psychiatry Section, Department of Psychiatry, Vall d'Hebron University Hospital, Barcelona, Spain.
Natalia CalvoDepartment of Psychiatry, Vall d'Hebron University Hospital, CIBERSAM, Barcelona, Spain.
Claudia RodríguezInstitute of Neuroscience, Hospital Clínic of Barcelona, Barcelona, Spain.
Miguel GárrizInstitute of Neuroscience, Hospital Clínic of Barcelona, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Personality assessment involves the collection and organization of information into multidimensional taxonomies. However, no routinely used index directly quantifies the amount of information encoded in such taxonomies. Effective dimensionality (ED) is a Shannon entropy-based metric that uses eigenvalue distributions to quantify a system's spectral information as the effective number of non-redundant components. Although ED has been applied across fields ranging from physics to political science, it remains underutilized in psychological assessment. Methods: After establishing an interpretive framework through Monte Carlo simulation, we applied ED to quantify informational content and redundancy across five widely used taxonomies of normal and pathological personality in large community and clinical samples ( Results: On average, domains conveyed 21% less spectral information than their nominal dimensionality implies, and facets 45% less, reflecting substantial trait overlap. Across levels, items contained 9- to 31-fold greater spectral information than domains, reflecting both specific variance and measurement error. Although residualized facets and items contained unique variance that predicted clinically relevant life outcomes, gains did not reach significance under stringent out-of-sample comparisons. In factor analysis, ED quantified factor redundancy across rotation methods, with oblique rotations producing up to 25% less spectral information. ED also informed cutoff calibration to mitigate the curse of dimensionality-which can artifactually inflate disorder rates in high-dimensional systems-maintaining prevalence estimates within 1-15% of target levels under multivariate normality. Conclusion: ED provides a model-agnostic, continuous index for quantifying spectral information, structural redundancy, and complexity in multidimensional psychological systems. However, it does not necessarily coincide with psychologically meaningful information. ED may support the development of informationally parsimonious taxonomies and well-calibrated diagnostic thresholds in high-dimensional personality assessment.

Indexed as

curse of dimensionalityeffective dimensionalitypersonality assessmentpersonality taxonomyShannon entropyspectral information

Identifiers

PMID42591228
PMCPMC13461502

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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.