Neither sharpened nor lost: the unique role of attention in children’s neural representations
Bibliographic record
Abstract
Abstract One critical feature of children’s cognition is their relatively immature attention. Decades of research have shown that children’s attentional abilities mature slowly over the course of development, including the ability to filter out distracting information. Despite such rich behavioral literature, little is known about how developing attentional abilities modulate neural representations in children. This information is critical to understanding exactly how attentional development shapes the way children process information. One intriguing possibility is that attention might be less likely to impact neural representations in children as compared with adults. In particular, representations of attended items may be less likely to be sharpened relative to unattended items in children as compared to adults. To investigate this possibility, we measured brain activity using fMRI while adults (21-31 years) and children (7-9 years) performed a one-back working memory task in which they were directed to attend to either motion direction or an object in a complex display where both were present. We used multivoxel pattern analysis and compared decoding accuracy of attended and unattended information. Consistent with attentional sharpening, we found higher decoding accuracy for task-relevant information (i.e., objects in the object-attended condition) than for task-irrelevant information (i.e., motion in the object-attended condition) in adults’ visual cortices. However, in children’s visual cortices, both task-relevant and task-irrelevant information were decoded equally well. What’s more, exploratory whole-brain analysis showed that the children represent task-irrelevant information more than adults in multiple regions across the brain, including the prefrontal cortex. These findings show that 1) attention does not sharpen neural representations in the child visual cortex, and further 2) that the developing brain can represent more information than the adult brain.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".