How theory of mind leads to positive first impressions.
Bibliographic record
Abstract
A common conjecture is that social success relies on "theory of mind"-the everyday skill of imputing mental states to others. We test the hypothesis that individuals with stronger theory of mind skills and motivation garner more positive first impressions because of how they interact with others. Participants included 334 young adults who were paired with a peer for a first-time meeting. Dyads completed a cooperative Lego-building task, which was videotaped and later coded for behavioral manifestations of theory of mind by independent raters. Theory of mind accuracy and motivation were assessed with validated laboratory tasks and a self-report questionnaire, respectively. First impressions were assessed based on partner's ratings of participant likeability, enjoyment of the interaction, and changes in positive affect. Results of actor-partner interdependence mediation models revealed that the associations between theory of mind and first impressions are indirect and mediated through behaviors. Specifically, participants with stronger theory of mind demonstrated greater cognitive sensitivity and pragmatic conversational skills. However, only cognitive sensitivity subsequently predicted more favorable first impressions. This research shows that social-cognitive skills can affect others' social impressions through their behavioral manifestations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".