Embodied and Embedded: The Dynamics of Extracting Perceptual Visual Invariants
Bibliographic record
Abstract
Perceptual Stability and Lability Perception provides well-adapted organisms with the vital links they must establish between themselves and their environment. To do this, perception must simultaneously supply stable and reliable reference points about what the environment provides at the behavioral level and allow great flexibility in capturing information. Ideally, perception must be the agent of this compromise between stability and lability at any given time and place, in accordance with the transitory behavioral objectives that organisms target. The complexity and unpredictability of the act of perception, as well as the effect of continuity experienced at the phenomenal level, result from the interplay of these constraints. In this chapter, we present theories and a methodology to probe into the dynamics of perceptual–motor processes as they bear the emergence of perceptual constancy. Visual Perception and Constancy of Position Visual perception of space is what guides action in the visible world; it does so by specifying environmental features to organisms that will allow them to achieve their behavioral objectives in a mobile fashion. To fulfill this mission, visual perception must manage to keep the properties of external objects constant, despite the continuously changing projection of the images on the retina of the eye. Perceptual constancy is maintained by transcending the hiatus separating distal and proximal stimuli, that is, by bridging between the physical nature of the perceived object and the physiological stimulation of the retina.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".