Impact of personality factors on interface interaction and the development of user profiles: Next steps in the personal equation of interaction
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
These current comparative studies explore the impact of individual differences in personality factors on interface interaction and learning performance behaviors in both an interactive visualization and a menu-driven web table in two studies. Participants were administered three psychometric measures designed to assess Locus of Control, Big Five Extraversion, and Big Five Neuroticism. Participants were then asked to complete procedural learning tasks in each interface. Results demonstrated that all three measures predicted completion times. Additionally, analyses demonstrated that personality factors also predicted the number of insights participants reported while completing the tasks in each interface. Furthermore, we used the psychometric findings in conjunction with a follow-up psychometric survey with a further 50 participants to build initial user profiles based on the cognitive task being undertaken. We discuss how these findings advance our ongoing research in the Personal Equation of Interaction.
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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 | 0.004 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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