Perception of Lighting and Reflectance in Real and Synthetic Stimuli
Bibliographic record
Abstract
The human visual system estimates the proportion of light reflected off of a surface despite variable lighting in a scene, a phenomenon known as lightness constancy. Classically, lightness constancy has been explained as a 'discounting' of the lighting intensity (Helmholtz, 1866), and this continues to be a common view today (e.g., Brainard & Maloney, 2011). However, Logvinenko and Maloney (2006) have made a radically different claim that the human visual system does not have any perceptual access to an estimation of lightness. The experiments described in Chapter 2 use a novel experimental paradigm to test this new theory proposed by Logvinenko and Maloney. We provide evidence against Logvinenko and Maloney's theory of lightness perception while adding to existing evidence that the visual system has good lightness constancy. In Chapter 3, we manipulate screen colour and texture cues to test the realism of computer-generated stimuli. We find that by matching the chromaticity of an LCD screen to the surrounding lighting and using a realistic texture, LCD screens can be made to appear similar to physical paper. Finally, Chapter 4 is an extension of the ideas from Chapter 3, in which the knowledge about how to adjust color and texture cues on an LCD monitor is applied to a lightness matching task. Here, the LCD screen is a small part of a larger physical setup. Additionally, levels of lightness constancy are compared across physical and simulated surfaces in the same novel experimental paradigm in Chapters 2 and 4. We find that physical and simulated surfaced elicit different levels of lightness constancy on the same task.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".