The SHINE toolbox for controlling low-level image properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual perception can be influenced by top-down processes related to the observer's goals and expectations, as well as by bottom-up processes related to low-level stimulus attributes, such as luminance, contrast, and spatial frequency. When using different physical stimuli across psychological conditions, one faces the problem of disentangling the contribution of low- and high-level factors. Here we make available the SHINE (Spectrum, Histogram, and Intensity Normalization and Equalization) toolbox written with Matlab, which we have found useful for controlling a number of image properties separately or simultaneously. SHINE features functions for scaling the rotational average of the Fourier amplitude spectra (i.e., the energy at each spatial frequency averaged across orientations), as well as for the precise matching of the spectra. It also includes functions for normalizing and scaling mean luminance and contrast, as well as a program for exact histogram specification. SHINE offers ways to apply the luminance adjustments to the whole image or to selective regions only (e.g., separately to the foreground and the background). The toolbox has been successfully employed for parametrically modifying a number of image properties or for equating them across the stimulus set in order to minimize potential low-level confounds in studies on higher-level processes (e.g., Fiset, Blais, Gosselin, Bub, & Tanaka, 2008; Williams, Willenbockel, & Gauthier, 2009). The toolbox can be downloaded here: www.mapageweb.umontreal.ca/gosselif/shine.
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.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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 it