Strategies and pseudoneglect on luminance judgments: An eye-tracking investigation.
Bibliographic record
Abstract
Four experiments were conducted to examine competing hypotheses relevant to the strategies believed to underlie pseudoneglect. The 4 experiments implemented manipulations relevant to eye-movement monitoring to evaluate the potential role of a comparison versus a global strategy in producing a left bias in a task involving a judgment of luminosity. Experiment 1 required task completion under free viewing while eye movements were monitored. The link between the observed behavioral bias and the strategy inferred from the pattern of eye movements was then examined. In Experiment 2, the eye-movement manipulation promoted reliance on a comparison strategy. Experiment 3 forced participants to use a global strategy. Finally, Experiment 4 also forced participants to use a global strategy, but it minimized the influence of memory in the task. Results from all 4 experiments supported the preponderance of the global strategy as driving the left bias. In Experiment 1, the magnitude of the bias decreased as the number of comparison increased. In Experiment 2, the left bias disappeared. Finally, in Experiments 3 and 4, the bias was the largest in the present series of experiments. These findings are discussed in the context of existing explanations of pseudoneglect.
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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 | 0.010 |
| 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) | 0.001 | 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".