Multiple password interference in text passwords and click-based graphical passwords
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The underlying issues relating to the usability and security of multiple passwords are largely unexplored. However, we know that people generally have difficulty remembering multiple passwords. This reduces security since users reuse the same password for different systems or reveal other passwords as they try to log in. We report on a laboratory study comparing recall of multiple text passwords with recall of multiple click-based graphical passwords. In a one-hour session (short-term), we found that participants in the graphical password condition coped significantly better than those in the text password condition. In particular, they made fewer errors when recalling their passwords, did not resort to creating passwords directly related to account names, and did not use similar passwords across multiple accounts. After two weeks, participants in the two conditions had recall success rates that were not statistically different from each other, but those with text passwords made more recall errors than participants with graphical passwords. In our study, click-based graphical passwords were significantly less susceptible to multiple password interference in the short-term, while having comparable usability to text passwords in most other respects.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it