Flexible visual statistical learning: Transfer across space and time.
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
The environment contains considerable information that is distributed across space and time, and the visual system is remarkably sensitive to such information via the operation of visual statistical learning (VSL). Previous VSL studies have focused on establishing what kinds of statistical relationships can be learned but have not fully explored how this knowledge is then represented in the mind. These representations could faithfully reflect the details of the learning context, but they could also be generalized in various ways. This was studied by testing how VSL transfers across changes between learning and test, and the results revealed a substantial degree of generalization. Learning of statistically defined temporal sequences was expressed in static spatial configurations, and learning of statistically defined spatial configurations facilitated detection performance in temporal streams. Learning of temporal sequences even transferred to reversed temporal orders during test when accurate performance did not depend on order, per se. These types of transfer imply that VSL can result in flexible representations, which may in turn allow VSL to function in ever-changing natural environments.
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 | 0.000 | 0.000 |
| 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) | 0.003 | 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