The effects of a spatial tonal relationship on keyboard typing proficiency
Bibliographic record
Abstract
The role of vision when operating a keyboard is critical, affecting both hand placement and button searching processes. As a result, individuals with visual impairments may have difficulties with these tasks, ultimately affecting their ability and proficiency to type. Technological advances often make it harder for those with visual impairments to successfully and efficiently use modern-day communication devices. The concurrent presentation of auditory and visual feedback can have an augmentative effect on the performance of motor skills (Lewiston, 2009). This study examines the role of auditory feedback on learning a novel keyboard. Using the spatial tonal compatibility relationships thought to be inherent in humans (e.g., Pratt, 1930; Mudd, 1963; Hansen et al., 2013), auditory tones were assigned to all alphabetical keys of a DVORAK keyboard. Participants were assigned to a Compatible (COMP) or Incompatible (INCOMP) condition, such that higher tones were associated with keys either higher and rightwards, or lower and leftwards, respectively. Results suggest that, on average, participants commit fewer errors overall and are faster at performing the task 48-hours post-task acquisition. This is consistent with a learning effect, such that throughout the study, participants are able to type letter sequences at a faster and more accurate rate. Results will be discussed in the context of a spatial tonal relationship and the relative strength of learning these relationships as a function of practice.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".