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Record W2953888619

Effect of attentional focus on manual tracking performance: Central vs. ambient input

2013· article· en· W2953888619 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFocus (optics)Computer scienceComputer visionTracking (education)Offset (computer science)Artificial intelligencePsychologyOptics
DOInot available

Abstract

fetched live from OpenAlex

The present study examined the manual tracking performance of participants under both external and internal focus instructions while engaging either central or ambient vision. Participants were instructed to reproduce both spatial and temporal characteristics of two vertically oscillating targets (0 and 90 degree phasing relationship) displayed on a monitor in front of them, which required monitoring via their central (< 15 degrees from central fixation point) or ambient (>30 degrees from central fixation point) visual systems. Manual tracking performance was recorded while participants focused attention on either their hands (internal focus) or the handles they were grasping (external focus). Tracking accuracy (root mean square: RMS) and temporal accuracy (lag) results revealed that when internal focus instructions were paired with ambient visual conditions, temporal performance was comparable to external focus results, suggesting that internal focus deficits can be offset by engaging ambient vision. For RMS results, performance accuracy was superior only when ambient vision was used under the more difficult 90 degree tracking trials.

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.

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 categoriesInsufficient 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 score0.619
Threshold uncertainty score0.999

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)0.0090.002

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 head0.015
GPT teacher head0.323
Teacher spread0.308 · 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

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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