Ideal-observer models of perceptual contrast enhancement
Bibliographic record
Abstract
In standard ideal-observer models of sensory cue integration [1], the perceptual estimate resulting from the combination of two cues lies in the interval bounded by the estimates of each cue separately. For example, this type of model accounts well for the psychophysical result - observers give an estimate in-between the haptic-alone and the visual-alone estimates, when asked to estimate ridges height with both vision and touch [2]. Nevertheless, a class of perceptual illusion is supposedly not accounted for by this type of model, namely contrast illusion, such as the size-weight illusion [3,4]. In the size-weight illusion, when asked to estimate the weight of two objects of the same mass but not the same size, observers estimate the larger as lighter. Using standard ideal-observer models, we showed that it is possible to account for this class of illusion provided that statistical correlation between each cue estimate is taken into account. Our argument is based on statistical inference models such as linear minimum-variance unbiased estimation, maximum a posteriori estimation, and least relative surprise estimation. This psychophysical model is general as long as the perceptual estimate deals with a physical quantity that is proportional to another physical quantity also available as a cue, such as mass and volume for a given material in the size-weight illusion.
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.
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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".