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Record W4389056196 · doi:10.3390/brainsci13121638

Anxiety and Depressive Traits in the Healthy Population Does Not Affect Spatial Orientation and Navigation

2023· article· en· W4389056196 on OpenAlexafffund
Ford Burles, Lila Berger, Michael McLaren-Gradinaru, Adam Leonidas David, Inderpreet Dhillon, Giuseppe Iaria

Bibliographic record

VenueBrain Sciences · 2023
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnxietyPsychologyMoodOrientation (vector space)Depression (economics)CognitionAffect (linguistics)PopulationClinical psychologyDevelopmental psychologyPsychiatryMedicineCommunication

Abstract

fetched live from OpenAlex

The ability to navigate and orient in spatial surroundings is critical for effective daily functioning. Such ability is perturbed in clinically diagnosed mood and anxiety disorders, with patients exhibiting poor navigational skills. Here, we investigated the effects of depression and anxiety traits (not the clinical manifestation of the disorders) on the healthy population and hypothesized that greater levels of depression and anxiety traits would manifest in poorer spatial orientation skills and, in particular, with a poor ability to form mental representations of the environment, i.e., cognitive maps. We asked 1237 participants to perform a battery of spatial orientation tasks and complete two questionnaires assessing their anxiety and depression traits. Contrary to our hypothesis, we did not find any correlation between participants' anxiety and depression traits and their ability to form cognitive maps. These findings may imply a significant difference between the clinical and non-clinical manifestations of anxiety and depression as affecting spatial orientation and navigational abilities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes2
Has abstractyes

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