Repurposing the multiple object tracking task to assess individual differences in attention resource capacity
Bibliographic record
Abstract
The Multiple Object-Tracking task first debuted in the cognitive sciences 35 years ago. Since then, research has sourced this paradigm to depict the phenomena of tracking multiple items simultaneously by manipulating factors that stress the limits of visual perception. The current presentation outlines and proposes an alternative use for the traditional measure. Here, we test the accuracy of the evolved and enhanced 3D-MOT paradigm to test the reliability in repeated assessment as well as the predictive validity of the task on clinically validated measures of attention and higher-order cognitive capability, such as intelligence. Additionally, this work aims to characterize individual difference factors in attention resource capacity via 3D-MOT performance. Participants (N = 400) between the ages of 6 to 30 years of age, and individuals with and without a neurodevelopmental condition (e.g., attention deficit hyperactivity disorder, intellectual disability) were administered the MOT paradigm. Performance on a separate, clinically validated measure of attention was collected using the Conners Continuous Performance task – 3rd edition (CPT-3), and similarly, IQ was measured using the Wechsler Abbreviated Scale of Intelligence – 2nd Edition. A subset of the sample (n = 120) were assessed on the 3D-MOT paradigm for a second time on a separate day. The results demonstrate a robust relationship between time 1 and time 2 of 3D-MOT performance across age, for neurotypical and neurodivergent populations. Furthermore, 3D-MOT capability explained a significant proportion of the variance in CPT-3 and IQ, particularly at higher levels of attentional load. While the indication of a neurodevelopmental condition was associated with decreased tracking capability, this difference was better accounted for by IQ. Overall, the findings have implications for the alternative use of 3D-MOT as a characterization of attention resource capacity, which is accurate, appropriate, and accessible for a neurodiverse population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".