Object Stability, Attention, and Temporal Order Judgments
Bibliographic record
Abstract
Our visual lives are filled with stable and unstable objects, some relatively human-sized like furniture and boxes, others much larger like trees and buildings. Yet not much is known about how the stability of these objects affects the deployment of our attention across the visual field. To address this, we have investigated how perceived stability interacts with our attentional system with a series of experiments using a cueless temporal order judgment task. Participants were presented with stable and unstable objects, separated by small temporal intervals, and asked to report which object appeared first in the display. Each experiment employed a different pair of stable and unstable stimuli, including objects with stability as a feature at the global scale (e.g., large art pieces; Experiment 1), local scale (e.g., traffic cone; Experiment 2), and never-before-seen objects (e.g., NOUN Database; Experiment 3). The use of never-before-seen objects was aimed to provide a control for novelty, as unstable objects may be more novel due to context (i.e., a physically unstable traffic cone) and attended to more. Additionally, Experiment 4 compared an object, an ottoman, that maintained its stability while being presented upright vs. upside down to control for inversion effects. Participants’ responses were fit to logistic regression models, and their point of subjective simultaneity was calculated using the fitted model. The results show that stability at the local scale captures attention, and this effect is present even in never-before-seen objects (Exp 3). Likewise, the lack of attentional capture in with the ottoman (Exp 4) suggests that these observed effects are due to the objects’ stability rather than their inverted nature. Overall, the results of this study help inform us of the role stability plays in perceiving objects in our visual environments.
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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 | 0.002 | 0.018 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".