The effect of context priming and task type on augmentative communication performance
Bibliographic record
Abstract
Augmentative and Alternative Communication (AAC) devices include special purpose electronic devices that generate speech output and are used by individuals to augment or replace vocal communication. Word prediction, including context specific prediction, has been proposed to help overcome barriers to the use of these devices (e.g., slow communication rates and limited access to situation-related vocabulary), but has not been tested in terms of effects during actual task performance. In this study, we compared AAC device use, task performance, and user perceptions across three tasks, in conditions where the AAC device used either was, or was not, primed with task specific vocabularies. The participants in this study were adults with normal physical, cognitive, and communication abilities. Context priming had a marginally significant effect on AAC device use as measured by keystroke savings; however, these advantages did not translate into higher level measures of rate, task performance, or user perceptions. In contrast, there were various statistically significant process and performance differences across task type. Additionally, results for two different emulations of human performance showed significant keystroke savings across context conditions. However, these effects were mitigated in actual performance and did not translate into keystroke savings. This indicates to AAC device designers and users that keystroke-based measures of device use may not be predictive of high level performance.
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 | 0.002 | 0.024 |
| 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 | 0.001 |
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".