Object updating: a force for perceptual continuity and scene stability in human vision
Bibliographic record
Abstract
How does the visual system provide us with the perception of a continuous and stable world in the face of the spatial–temporal chaos that characterizes its input? In this chapter we summarize several programs of research that all point to a solution we refer to as object updating . We use this phrase because perceptual continuity seems to occur at an object level (as opposed to an image level or a higher conceptual level) and because our research suggests that the visual system makes a sharp distinction between the formation of new object representations versus the updating of existing object representations. We summarize the research that led us to this view in the areas of masking by object substitution, the flash-lag illusion, response priming, and an illusion of perceptual asynchrony. Introduction Biological vision is the marvelous ability of an organism to be informed about its surroundings at a distance and with a high degree of spatial and temporal resolution. This ability allows us to know where things are, what shape and color they are, and equally importantly, when they are there, so that we may interact with them appropriately. Yet, contrary to many people's implicit understanding of how biological vision is accomplished, it is not a process by which light, reflected from surfaces in the three-dimensional world, is recorded faithfully by the brain in order to reconstruct the nature of the surfaces that gave rise to the recorded pattern of light.
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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 | 0.001 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| 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".