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Record W4384664491 · doi:10.1177/00315125231189091

Young Basketball Players’ Multiple Object Tracking Skills Were Unaffected by Stroop-Induced Mental Fatigue

2023· article· en· W4384664491 on OpenAlexaff
Heloiana Faro, Douglas Cavalcante Silva, Bruno Teixeira Barbosa, Yago Pessoa da Costa, Carlos Gilberto de Freitas-Junior, Dalton de Lima‐Júnior, Jocelyn Faubert, Leonardo de Sousa Fortes

Bibliographic record

VenuePerceptual and Motor Skills · 2023
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsStroop effectBasketballMental fatiguePsychologyTask (project management)Set (abstract data type)Mental healthPhysical therapyPhysical medicine and rehabilitationCognitionClinical psychologyMedicinePsychiatryComputer science

Abstract

fetched live from OpenAlex

We aimed to examine the acute effect of mental fatigue on young basketball players’ three-dimensional multiple object tracking (3D-MOT) skills. Our participants were 12 adolescent basketball players ( M age = 16.66, SD = 1.87 years; M years of practice = 2.66, SD = 1.07 years). In nine lab visits, we used visits 1 to 7 to familiarize participants with 3D-MOT, a subjective scale of mental fatigue, and a Stroop task involving mental set shifting. In the last two visits, participants performed in both experimental (EXP) and control (CON) conditions that were presented in randomized order. In the EXP condition, participants performed 3D-MOT pre- and post-60 minutes of induced mental fatigue; in the CON condition, they watched a documentary. After each condition, B participants performed the National Aeronautics and Space Administration Task Load Index (NASA-TLX). 3D-MOT performance measures were the “score” and “fastest trial score success.” The response time on the Stroop tasks increased throughout the mental fatigue inducement in the experimental condition ( p = .0037). The NASA-TLX responses were higher following the EXP condition than following the CON condition for mental demand, temporal demand, and performance (all ps < .05). Still, there were no significant EXP versus CON differences on the 3D-MOT performance indicators.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0100.002

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 head0.031
GPT teacher head0.339
Teacher spread0.307 · 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; both teacher heads agree on what is shown here.

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 routes1
Has abstractyes

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