“Painting with the Eyes”: Sensory Perception Flux Time-Integrated on the Physical World
Bibliographic record
Abstract
We introduce the concept of a “veillogram”, a measurement of sensing or perception of physical objects as if those objects were like photographic film, to register the degree of sensing over time. A time-integrated sensory field is tracked in 3D space. Human visual gaze on an object, for example, produces a veillogram which is a detailed sensory map, rather than merely following the gaze of the eyes as one single point. This 3D sensory measurement space, was transformed and rendered onto physical objects, to indicate sensory attention integrated (accumulated) over an object's surface. Applications can include the assessment of control panels in aircraft cockpits, automobiles, and industrial control rooms, by measuring the visual attention directed at parts of a display or control panel, in real-time as information appears, and as a human attempts to control using the information available. The purpose is to then better design those interfaces according to real-world measured attention, as a full veillogram, rather than by merely tracking gaze angle. Similarly, for medical or commercial environments, behavioural studies, or user-interface design applications, a veillogram can create a richly detailed mapping of visual attention as a time-varying, spatially-varying function.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".