Bibliographic record
Abstract
Despite the dynamic nature of real-world environments, previous research on change detection has primarily used static stimuli. However, research exploring the impact of motion on attention (Suchow & Alvarez, 2011) and memory (Blalock et al., 2014; Chung et al., 2023), two necessary components for successful change detection, suggests that motion can impair both processes. Consequently, the objective of the present experiment was to determine whether motion impairs change detection, as well as whether different motion types (i.e., synchronous and asynchronous) have different effects on detection accuracy. Additionally, we sought to determine whether perceptual load moderates the impact of motion. To address these objectives, we conducted a gradual change blindness experiment in which participants were presented with task-relevant (colourful, randomly-oriented isosceles triangles) and task-irrelevant (gray circles) stimuli that were either stationary, moving synchronously, or moving asynchronously. In each trial, one task-relevant stimulus gradually changed while participants attempted to identify the change target. Change detection was examined as a function of Change Type (Color, Orientation), Motion Type (Stationary, Synchronous, Asynchronous), and Load (3, 6, 9, 12 task-relevant stimuli). Interestingly, results showed that Motion Type significantly affected change detection for orientation changes, but not for color changes. Within the Orientation condition, detection accuracy was highest in the Stationary condition, lower in the Synchronous condition, and lowest in the Asynchronous condition. As hypothesized, we observed an interaction between Load and Motion in the Orientation condition, such that the effects of motion were absent when load was low (Load = 3), but emerged at higher loads (Load = 6, 9, 12). It was concluded that motion only impaired orientation change detection because, unlike color, the inherent movement of the orientation changes were masked by the Synchronous and Asynchronous movement.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".