Distinctive patterns of Multiple Object-Tracking performance trajectories in youth with deficits in attention, learning, and intelligence
Bibliographic record
Abstract
There is a significant overlap in symptomology between individuals with deficits in attention and learning, which is explained by the co-dependent dynamic between the two cognitive constructs. Within this dynamic, attentional resources are allocated to salient stimuli while learning mechanisms distinguish relevant from irrelevant information. Moreover, individuals with deficits in higher-order cognition (i.e., intelligence) can demonstrate difficulties in attention and learning. The Multiple Object-Tracking (MOT) task is a sensitive and versatile measure of attention that has characterized individual differences in attention as a function of higher-order cognition. Exploiting the traditional MOT task's ability to characterize the allocation of attentional resources to task demands, the current study compared learning exhibited on an attention-based task across neurodevelopmental conditions defined by deficits in attention (attention-deficit/hyperactivity disorder; ADHD), learning (specific learning disorder; SLD), and intelligence (intellectual developmental disorder; IDD). Children and adolescents (N = 101) completed 15 sessions on a Multiple Object-Tracking (MOT) task where performance trajectories were analyzed using latent growth curve modeling and conditioned by the presence of ADHD, SLD, or IDD while controlling for performance on a separate measure of attention, age, and sex. The sample, characterized by below-average IQ and problematic levels of attention, exhibited an effect of learning on MOT. However, individuals with an IDD diagnosis demonstrated decreased baseline MOT capability while ADHD and SLD profiles exhibited decreased slopes, relative to other neurodevelopmental conditions. Taken together, the results demonstrate distinct linear performance trajectories between neurodevelopmental conditions defined by deficits in attention, learning, and intelligence. The current study provides additional evidence to repurpose the traditional MOT task as a descriptor of attention and discusses alternative uses for the paradigm. Overall, these results suggest an eclectic approach that considers attention, learning, and higher-order cognition when diagnosing ADHD, SLD, or IDD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".