A re-evaluation of fitts (1954): Veridical target width and effector precision influence the scaling of reach trajectories
Bibliographic record
Abstract
The classic theorem of Paul Fitts' (1954) asserts that the combined effects of movement amplitude and target width (index of difficulty: ID) define movement times (MT) for goal-directed reaches. Moreover, Fitts' theorem states that reaches yielding the same ID produce equivalent MTs regardless of the response's amplitude and width combination. The present study examined the utility of Fitts theorem in the context of reaches to virtual targets wherein width- and amplitude-based ID changes (3, 4, 5 and 6 bits of information) were presented in separate blocks (à la Fitts) and randomly interleaved on a trial-by-trial basis. In addition, we examined whether finger- (Experiment 1: N = 12) and stylus-directed (Experiment 2: N = 12) reaches influence MT/ID relations. Experiments 1 and 2 showed that blocked and random trial presentations elicited a linear increase in MT as a function of increasing ID (Fs > 61); however, slopes for MT/ID relations were markedly shallower in the width as compared to the amplitude manipulation. Moreover, slopes for amplitude-based ID manipulations were equivalent across finger- (b = 101 ms; R2 = 0.99) and stylus-directed (b = 134 ms; R2 = 0.99) reaches whereas slopes for width-based manipulations were shallower in the former condition (finger-directed: b = 5 ms: R2 = 0.87; stylus-directed: b = 58 ms: R2 = 0.99). Importantly, examination of reach endpoints indicates that the differences observed here cannot be tied to between-condition differences in error rates. Thus, the present findings add to the extant literature insomuch as they demonstrate that the information-processing capacity of the motor system is not broadly reflected by a simple line function.Acknowledgments: Supported by NSERC.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".