Comparing integral and incidental emotions: Testing insights from emotions as social information theory and attribution theory.
Bibliographic record
Abstract
Studies have indicated that observers can infer information about others' behavioral intentions from others' emotions and use this information in making their own decisions. Integrating emotions as social information (EASI) theory and attribution theory, we argue that the interpersonal effects of emotions are not only influenced by the type of discrete emotion (e.g., anger vs. happiness) but also by the target of the emotion (i.e., how the emotion relates to the situation). We compare the interpersonal effects of emotions that are integral (i.e., related to the situation) versus incidental (i.e., lacking a clear target in the situation) in a negotiation context. Results from 4 studies support our general argument that the target of an opponent's emotion influences the degree to which observers attribute the emotion to their own behavior. These attributions influence observers' inferences regarding the perceived threat of an impasse or cooperativeness of an opponent, which can motivate observers to strategically adjust their behavior. Specifically, emotion target influenced concessions for both anger and happiness (Study 1, N = 254), with perceived threat and cooperativeness mediating the effects of anger and happiness, respectively (Study 2, N = 280). Study 3 (N = 314) demonstrated the mediating role of attributions and moderating role of need for closure. Study 4 (N = 193) outlined how observers' need for cognitive closure influences how they attribute incidental anger. We discuss theoretical implications related to the social influence of emotions as well as practical implications related to the impact of personality on negotiators' biases and behaviors. (PsycINFO Database Record
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 | 0.013 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".