User Interface Evaluation Through Implicit-Association Tests
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 implicit-association test (IAT) is a method for measuring subconscious associations between concepts in memory. It is widely used in social psychology research for assessing associations that people may be unable or unwilling to articulate, including those relating to race, gender, self harm, and risk-taking behaviour. We describe the motivation for adapting the IAT to user interface evaluation, including its potential to support rapid A/B testing that is amenable to online crowd-source dissemination, while also potentially reducing the validity risks caused by biases such as the good subject effect. We present a method (the UI-IAT) for conducting implicit association tests for A/B user interface evaluation, and we present results of two experiments demonstrating that, although the method can successfully discriminate between 'good' and 'bad' interfaces, its sensitivity is low. We discuss implications for practical use of the UI-IAT and for further work.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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