Three-Dimensional MOT task as an assessment tool for attention and working memory: a comparison with traditional measures
Bibliographic record
Abstract
Performance on a three-dimensional multiple object tracking (3D-MOT) task is considered to be an accurate measure of real-world dynamic attention. Working memory (WM) is an important component of 3D-MOT task completion since target items are tracked amongst distractors over a set period of time. 3D-MOT performance is also consistent with developmental expectations, wherein improvements are observed with increasing age in concordance with developing WM capabilities. This study aimed to assess whether 3D-MOT can be used to characterize WM ability at different periods of development by comparing it to that of traditional neuropsychological assessment methods. Sixty-four participants, placed in child(n=9), adolescent (n=22), adult (n=33) groups, were assessed on a 3D-MOT tasks comprised of four conditions with increasing WM load (3 target items out of 8 distractor items were tracked for 5, 8 12 and 15 seconds). All participants also completed the Paced Auditory Serial Addition Test (PASAT) WM task; attention (Connors CPT-3 & CATA) and WASI-2 IQ measures also collected. Results indicated that all groups showed a reduction in 3D-MOT performance (defined as the average speed at which target spheres were successfully tracked) with increasing WM load. Importantly, performance on the 3D-MOT and the PASAT WM task declined in a similar rate with increasing WM load for adolescents and adults, but not for children, consistent with developing WM capacity. These group differences seem to reflect the differential ability typically observed on traditional attention and WM tasks, thus suggesting that dynamic 3D-MOT tasks are sensitive enough to characterize WM ability across developmental stages. Meeting abstract presented at VSS 2017
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".