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)
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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 | 0.000 |
| 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) | 0.000 | 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".