Dataset supplementing Veto, Peter & Uhlig, Marvin & Troje, Nikolaus F. & Einhäuser, Wolfgang. (2018). Cognition modulates action-to-perception transfer in ambiguous perception. Journal of Vision. 18(8),5.
Bibliographic record
Abstract
The following dataset supplements the publication Veto, Peter & Uhlig, Marvin & Troje, Nikolaus F., & Einhäuser, Wolfgang. (2018). Cognition modulates action-to-perception transfer in ambiguous perception. Journal of Vision. 18(8), 5. doi: 10.1167/18.8.5. It is free to use for scientific purposes as long as the article is appropriately cited. Data are contained in individual .mat files for each experimental block (file name: participant, block, belt/gear condition, direction condition). Each file consists of rows of raw data organized in columns: 1: time stamp 2: manipulandum position (angle in degrees) 3: belt/gear condition 4: direction condition Run analyzeMain.m to re-create the analysis of the aforementioned paper. This project was supported by the German Research Foundation through the projects "CRC/Transregio 135 – Cardinal mechanisms of perception: Prediction, valuation, categorization" and "International Research Training Group 1901 – The Brain in Action." NFT's contribution was funded by a Natural Sciences and Engineering Research Council Discovery Grant.
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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.263 | 0.190 |
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".