Thought-Feeling Accuracy in Person Perception and Metaperception: An Integrative Perspective
Bibliographic record
Abstract
People often want to know what their interaction partners are thinking. How accurate are they, what information do they use, what predicts how accurate they will be, and does accuracy matter? We organize our review of thought-feeling accuracy, defined as the accuracy of individuals' judgments about the content of another person's thoughts and feelings in live interaction, around these questions. At the same time, we argue that often people are especially interested in what others are thinking about them, such that research on the accuracy of individuals' metaperceptions regarding others' views of them is highly relevant to understanding thought-feeling accuracy more broadly construed. In particular, we maintain that systematic biases characterizing individuals' spontaneous metaperceptions are an important source of preventable and harmful forms of thought-feeling inaccuracy. We advocate for integration across the thought-feeling accuracy and meta-accuracy literatures so as to generate new insights that can move them both forward.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".