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Effects of personality on assessments of anxiety and cognition

2022· review· en· W4291110150 on OpenAlexaff
Lucía Améndola, Daniel M. Weary, Gosia Zobel

Bibliographic record

VenueNeuroscience & Biobehavioral Reviews · 2022
Typereview
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnxietyPersonalityPsychologyVariation (astronomy)Big Five personality traitsCognitionCognitive psychologyClinical psychologyDevelopmental psychologyPersonality Assessment InventorySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Individual variation in responses to commonly used tests of anxiety and spatial memory is often reported. While this variation is frequently considered to be 'noise', evidence suggests that it is, at least partially, related to consistent individual differences in behavioral responses (i.e., personality). The same tests used to assess anxiety are often used to profile personality traits, but personality differences are rarely considered when testing treatment differences in anxiety. Focusing on the rat literature, we describe fundamental principles involved in anxiety and spatial memory tests and we discuss how personality differences and housing conditions can influence behavioral responses in these tests. We propose that an opportunity exists to increase stress resiliency in environmentally sensitive individuals by providing environmental enrichment. We conclude by discussing different approaches to incorporating personality measures into the design and analysis of future studies; given the potential that variation masks research outcomes, we suggest that a strategy which considers the individual and its housing can contribute to improving research reproducibility.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.228
GPT teacher head0.447
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2022
Admission routes1
Has abstractyes

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