Bibliographic record
Abstract
Every day, our visual attention needs to be allocated across the diverse and complex scenes we encounter. Previous research demonstrated that various physical features that make up the objects in the visual field (e.g., colour, size, shape, texture, and motion), as well as learned information such as context (e.g., a toaster on a counter vs a toaster in a sink), play a role in how we allocate our attention various scenes. Through a lifetime of interactions with objects and environments, we also learn that the physical properties of objects, such as shape, are often tied to our perception of structure in the visual world. These learned physical properties may apply both to individual objects (e.g., a canonical pyramid is highly stable vs an upside-down pyramid is highly unstable) and to the relationships between objects (e.g., for a two-square box tower, we can remove the top box easily but removing the bottom box would make the top box unstable.) Thus, we are investigating whether the perceived stability of an object or group of objects influences the allocation of attention. In a series of experiments, we used a cueless temporal order judgment task to answer this question. Participants were presented with both stable and unstable objects or sets of objects, separated by small temporal intervals, and asked which one appeared first in the display. Participants’ responses were fit into logistic regression models, and their point of subjective simultaneity (PSS) was calculated using the fitted model. These PSSs were near zero, indicating that stable and unstable objects or sets of objects did not differ in how attention was allocated to them. Overall, the results of these experiments 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".