Augmented feedback influences upper limb reaching movement times but does not explain violations of Fitts' Law
Bibliographic record
Abstract
Fitts' (1954) classic theorem asserts that the movement time (MT) of voluntary reaches is determined by amplitude and width requirements (i.e., index of difficulty: ID). Actions associated with equivalent IDs should elicit equivalent MTs regardless of the amplitude and/ or width requirements. However, contemporary research has reported that amplitude-based contributions to IDs yield larger increases in MTs than width-based contributions. This discrepancy may relate to the presence of augmented terminal feedback in Fitts' original research, which has not been provided in more recent investigations (e.g., Heath et al., 2011). To address this issue, participants performed reaching movements during two sessions wherein feedback regarding terminal accuracy was either provided or withheld. It was hypothesized that the absence of augmented terminal feedback would result in a stereotyped performance across target widths and explain the violation of Fitts' theorem. Yet, the results revealed distinct influences of amplitude- and width-based manipulations on MT, which also persisted across feedback conditions. This finding supports the assertion that the unitary nature of Fitts' theorem does not account for a continuous range of movement amplitudes and target widths. A secondary analysis was competed in an attempt to further investigate the violation of Fitts' Law. Based on error rates, participants were segregated into accuracy- and speed-prone groups. Additionally, target's IDs were recalculated based on each participant's performance using the effective target width (i.e., IDWe) instead of the nominal target width. When using MT data from the accuracy-prone group with this IDWe, the aforementioned violation was alleviated. Overall, augmented terminal feedback did not explain the violation of Fitts' theorem, although one should consider using the effective target width and participant's strategy in future investigations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".