The influence of conscientiousness and context on the emotional response during understanding
Bibliographic record
Abstract
Introduction: In this exploratory study, we use a logistic multilevel model to examine the interplay between personality, emotions, and understanding. Methods: The impact of Conscientiousness, a Big Five Personality trait, on emotional responses during riddle-solving attempts was investigated in 101 participants, who each tackled 15 riddles remotely via Zoom. Video recordings were analyzed using iMotions with AFFDEX to identify the emotion type (positive, negative, and epistemic) experienced during the riddle-solving (understanding) process. Results: The study revealed significant effects of both positive and negative emotions on understanding scores. Upon examining cross-level interactions, the effect of trait Conscientiousness on the association between emotion type and understanding scores was not significant. Our random intercepts model appeared to provide the most satisfactory explanation for our exploratory findings. Further, the model demonstrated good discrimination ability, as evidenced by our area under the Receiver Operating Characteristic (ROC) curve. Discussion: Our results highlight the fundamental role of context, specifically the individual's perceived value of a learning activity and the nature of their understanding-emergent or established-in shaping the emotions evoked during the process of understanding. For future endeavors, we recommend a concentrated focus on the complex interplay between personality, emotions, and understanding, especially when understanding is in its emergent phase and the task employed for measurement provokes emotional investment.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".