Testing the State and Trait Accuracy Model: How Accurate Judgments of State Affect Relate and Contribute to Accurate Judgments of Personality Traits
Bibliographic record
Abstract
Psychology has long traditions of measuring the accuracy of judging personality and the accuracy of judging emotional states, but little theoretical progress has been made to connect these two processes that are so important in daily life. The State and Trait Accuracy Model (STAM) was designed to describe how accurate judgments of affective states might influence the accuracy of judgments of personality traits. Four studies (total N = 1484) tested the predictions of STAM that 1) accuracy of judging affect is positively related to accuracy of judging traits of the same target people, and 2) accuracy of affect judgments influences accuracy of trait judgments. Judges observed targets who had been recorded in situations of medium strength so that targets with different levels of traits would respond differently, and then provided judgments of affect and traits. Consistent support was found for the positive relation between affect accuracy, especially for positive emotions, and trait accuracy. Some support was also found for the causal connection from affect accuracy to trait accuracy, although this connection was not always in the expected direction. Results from this type of research enhance understanding of the process of judgments of affect and traits, and could be used to design effective methods for increasing the accuracy of judgments of others.
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.
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 | 0.025 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".