Guelph University Address correspondence to:
Bibliographic record
Abstract
Four modes of selection — Page 2 The concept of selective attention is fundamental to understanding human behavior. And in everyday life, it is clear that there are marked differences in attention between individuals. Some have special talents and others face special challenges; individuals of different age display predictably different capacities. Some of these differences reflect a genetically determined plan governing neural growth, maturation and senescence, whereas others reflect specific events: positive events such as the acquisition of expertise, and negative events such as pathology or injury. In this article we address three questions: What is attention? How does attention change during childhood? What are the consequences of these changes in daily life? We examine these questions primarily for the visual modality, though we believe that attention in other modalities follows similar principles. A harsh reality that confronts all students of selective attention is that the literature is fragmented, making the study of individual differences a daunting task. This is especially true of individual differences that stem from human development
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.803 | 0.429 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".