How robust are measures of online control to offline mechanisms?
Bibliographic record
Abstract
The importance of online visual feedback for the accurate completion of reaching movements is widely accepted. In contrast, the quantification of online feedback utilization can still be challenged. For example, many contemporary measures of online control are calculated across multiple trials, which brings about potential confounds via the contribution of offline trial-to-trial changes in performance. The current study sought to contrast the robustness of common measures of online control and a novel frequency-based measure (de Grosbois & Tremblay, 2015) to offline-induced changes in reaching trajectories. The main experimental conditions included reaches performed in no-vision conditions with: a) optical prisms that shifted the perceived location of the target and environment and b) terminal feedback [TF] via a 1-s window of vision after each movement. Also, control full-vision conditions were performed with and without TF. The measures of online control included two between-trial measures (i.e., variable error [VE] and normalized correlations of position at 75 % of movement time relative to the end-position [Z2]) and two within trial measures (i.e., the time-after-peak-velocity [TAPV] and a novel frequency analysis [pPower]). The results indicated that VE and TAPV were sensitive to the prism manipulation (i.e., not robust to offline control mechanism), whereas Z2 and pPower were relatively immune to the offline changes caused by the prisms. Supplementary analyses comparing the two full-vision conditions (i.e., with and without TF) indicated that the pPower measure was the most robust to offline influences. Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF)
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 | 0.022 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".