17. Within-Object Attention: Attentional Concentration and Amplification in Moving Versus Static Conditions
Bibliographic record
Abstract
When presented with multiple stimuli, attention serves to help us select which items to focus on for further processing. The techniques we apply to determine where to focus within an object are less clearly understood than those applied for selecting objects. This study aims to observe if there are any existing trends in how we allocate our attention within objects, as well as to examine how different factors affect this cognitive process. Two previously identified trends in how attention is allocated within lines are attentional concentration – a tendency to focus on the center of the lines – and attentional amplification – the increase in center focus as line length increases (Alvarez and Scholl, 2005). Object dynamics is a variable that can be manipulated to test if these effects represent a higher-order tracking strategy, or whether they will still be exhibited when presented with stationary lines. This study examines whether these effects can be replicated when tested with moving lines as well as stationary lines. 22 participants completed tracking tasks, either moving or static, where they tracked two designated target lines while simultaneously watching for the appearance of a probe at either the center or end of any of the lines. In the moving condition, probe detection was significantly lower at the ends of lines (indicating attentional concentration), and this decrease in performance grew slightly as line length increased (indicating weak attentional amplification); however, no significant differences in probe detection between centers and ends of lines were found in the static condition.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".