Learning and visual attention across neurodevel-opmental conditions: Using Multiple Object-Tracking as a descriptor of visual attention
Bibliographic record
Abstract
While most studies in Multiple Object-Tracking (MOT) have focused on understanding the mechanisms of visual tracking, recent work has suggested that MOT can be used to characterize individual differences in attention resource capacity (Tullo, et al., 2018). In the current study, we investigated whether repeated practice on an adaptive MOT task could explain the interplay between attention resource capacity and learning in relation to individuals diagnosed with neurodevelopmental conditions. Specifically, we investigated whether MOT performance would differ across neurodevelopmental conditions that are either defined by deficits in attention (e.g., ADHD), or exhibit clinically significant difficulties in attention among other deficiencies (e.g., Autism). We asked whether intelligence, our proxy for cognitive capability, and/or diagnostic profile (i.e., autism, ADHD, Intellectual Disability [ID], and Learning Disability [LD]) predicted learning on daily MOT performance across 15 sessions. Children and adolescents (N=106; Mage=13.51) with a diagnosis of either autism (n=32), ADHD (n=35), or ID/LD (n=39) visually tracked 3 of 8 spheres for 8 seconds. Task difficulty adapted to the participant’s capability on a trial-by-trial basis. Performance was defined as the average speed in cm/s, where participants correctly tracked all target items. Results indicated that MOT performance mapped onto a logarithmic function, which resembled a typical learning curve at R2=0.87. Performance improved by 105% from the first to last day of testing. Moreover, day-one performance was predicted by intelligence: R2=0.28, and the rate of change in performance (i.e., learning) differed across diagnostic groups. Children and adolescents with autism (MsD1-1=1.11) demonstrated a greater standardized change than those with ADHD (MsD1-15=0.54) or ID/LD (MsD1-15=0.52). These differences highlight variability in learning capability and attention resource capacity, which vary by diagnosis and higher-level cognitive ability, such as fluid reasoning intelligence. Overall, these findings emphasize the promise and utility of MOT to define both attentional and learning capabilities across individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".