Bibliographic record
Abstract
Most researchers now believe that human observers use a default coarse-to-fine strategy (i.e., from low to high spatial frequencies) to extract face information (e.g., Morrisson & Schyns, in press). Schyns, Bonnar & Gosselin (in press) recently discovered an analoguous differential use of face information along the spatial location and spatial frequency dimensions (see also Gosselin & Schyns, 2001). In sum, we know how time and frequency, as well as how frequency and location interact in face recognition; however, we do not know how location and time interact (neither do we know how spatial location, spatial frequency, and time interact, but this is another story). Here, we explore the spatio-temporal use of information in face recognition. Our stimuli set comprised 30 faces (i.e., [5 males + 5 females] * 3 expressions). The stimuli subtended 5.72 × 5.72 deg of visual angle and were presented for 320 ms. We utilized a novel technique called Bubbles (Gosselin & Schyns, 2001) to reveal directly the effective use of visual information. In a nutshell, we sampled space and time with small Gaussian windows (standard deviation = .22 deg in space and 43 ms in time), and adjusted their number on-line to maintain performance at 75% correct. We ran 10 subjects. A proportion-correct-when-available statistics was computed for each pixel (i.e., first order statistics); higher-order statistics were also computed. We obtained clear spatio-temporal modulations of effective use of information.
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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".