Differential processing of sharp versus blurred targets presented in figure and ground? It depends on the task
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wong and Weisstein ([1983]. Sharp targets are detected better against a figure, and blurred targets are detected better against a background. Journal of Experimental Psychology: Human Perception and Performance, 9(2), 194–202) reported that accuracy on a near-threshold target detection task was more accurate for sharp targets that appeared in a region of visual space perceived as figure, and for blurred targets appearing in a region of visual space perceived as ground. Here, we sought to see if this interesting pattern, which has generated considerable interest, generalizes beyond the methods used in the original study. Two experiments were conducted in which sharp and blurred line targets were presented on figure and ground, while the participants’ task was to make a speeded orientation discrimination of a supra-threshold target. Because in neither experiment did we obtain the interaction reported by Wong and Weisstein, we suggest that their interesting interaction may not generalize to speeded responses to supra-threshold stimuli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it