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Record W4402862972 · doi:10.1037/neu0000973

Getting oriented: Redefining attention deficits in Parkinson’s disease.

2024· article· en· W4402862972 on OpenAlexaff
O. Peleg, Rébaï Soret, Pom Charras, Vsevolod Peysakhovich, Anat Mirelman, Inbal Maidan, Daniel A. Levy

Bibliographic record

VenueNeuropsychology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsCentre for Movement Disorders
FundersMinistry of Health, State of IsraelIsrael Science Foundation
KeywordsParkinson's diseaseNeuroscienceDiseaseCognitive psychologyPsychologyMedicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: Parkinson's disease (PD) may affect not only motor functions, but also cognitive processes such as attention. While past research has examined PD impact on spatial attention, it has not addressed how the key functions of attentional orienting and alerting in PD are mediated by cueing format, an ecologically relevant parameter. We assessed how exogenous and endogenous orienting cue modes affect PD patients' visuospatial attention expressed as dorsal attention network orienting benefits, ventral attention network reorienting costs, and alerting abilities. METHOD: Ninety PD patients and 72 healthy comparison participants performed a spatial attention task in an engaging game format which required selection of a target location without prior cueing, or with temporal, valid spatial, or invalid spatial exogenous or endogenous cueing. RESULTS: PD patients differed from healthy participants only in response time benefits in orienting under endogenous probabilistically predictive cue processing. They did not exhibit greater reorienting costs, differences in inhibition of return, or alerting deficits, irrespective of modes of cueing. CONCLUSION: These results suggest that fundamental orienting and alerting functions might be intact in PD, with challenges emerging only if additional cognitive processes, including those related to motor preparation, are required to utilize cue information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations1
Published2024
Admission routes1
Has abstractyes

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