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Record W4402904609 · doi:10.1167/jov.24.10.620

Multiple object tracking as a measure of sustained attention and relation with fluid reasoning

2024· article· en· W4402904609 on OpenAlexaff
Taryn Perelmiter, Domenico Tullo, Jocelyn Faubert, Armando Bertone

Bibliographic record

VenueJournal of Vision · 2024
Typearticle
Languageen
FieldComputer Science
TopicIntelligent Tutoring Systems and Adaptive Learning
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsRelation (database)Measure (data warehouse)Object (grammar)Tracking (education)PsychologyArtificial intelligenceCognitive psychologyComputer visionComputer scienceData mining

Abstract

fetched live from OpenAlex

The Multiple Object Tracking (MOT) paradigm has been a staple psychometric tool in cognitive sciences for over three decades, primarily designed to assess the ability to track multiple items simultaneously and explore the limitations of select subcomponents of attention. The present study seeks to extend the paradigm’s application to real-world settings by investigating the paradigm's utility as a measure of sustained attention, while also considering individual differences in higher-order cognition, such as fluid reasoning intelligence. In this study, 61 typically developing adults completed the MOT task at varying demands of sustained attention (i.e., manipulations in trial durations consisting of 5, 8, 11, and 15 seconds). Participants also completed a measure of cognitive functioning via the Wechsler Abbreviated Scale of Intelligence – 2nd edition. The results demonstrated that fluid reasoning intelligence was a significant and robust predictor of MOT task performance across all trial lengths. Here, individuals with higher fluid reasoning scores outperformed those with lower scores, regardless of whether they also had high verbal intelligence, extending previous work examining the relationship between distributed attention and intelligence. Furthermore, performance on 8-second trials emerged as the best significant predictor of fluid reasoning intelligence, suggesting an optimal trial condition to characterize attention resource capacity for sustained attention. These findings contribute to the growing body of evidence affirming the strong link between fluid reasoning intelligence and sustained attention capabilities as measured by the MOT. Moreover, they support the paradigm's effectiveness in differentiating attention resource capacities among individuals. This study also uncovers the clinical significance of these insights, highlighting the optimal conditions for evaluating attentional capabilities in individuals with and without attention deficits. Taken together these results advocate for the MOT's broader application in both research and clinical settings, emphasizing its value in isolating specific subcomponents of attention.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.191

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 communication0.0000.001
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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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