Phonological and visual working memory in processing of route guidance information
Bibliographic record
Abstract
The goal of my proposed thesis is to examine the role of working memory in processing route guidance information while driving. I will also assess how changes in the presentation and processing of route guidance and secondary task information influences the primary task of vehicle control. Results will be analyzed in terms of whether a change of route navigation presentation from visual to auditory will change the memory resources used to process the information. Furthermore, analyses will be done to assess whether changes in the processing of route navigation and secondary task information affect driving performance. To examine what working memory subsystems (i.e., phonological, visual, spatial, central executive) are used to process visual versus auditory route guidance information, the present research will require participants to drive a driving simulator while performing a route navigation task and a secondary working memory task. Performance of these secondary tasks will permit us to assess demands of route navigation tasks upon the various working memory subsystems. Accordingly, participants will retain a memory load while performing the route navigation tasks. This research is expected to raise significant HCI implications for the design of safer interfaces for vehicles and provide much needed detail about how specific mental codes and processes are involved in processing route navigation information.
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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 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".