The interpersonal costs of dishonesty: How dishonest behavior reduces individuals’ ability to read others’ emotions.
Bibliographic record
Abstract
In this research, we examine the unintended consequences of dishonest behavior for one's interpersonal abilities and subsequent ethical behavior. Specifically, we unpack how dishonest conduct can reduce one's generalized empathic accuracy-the ability to accurately read other people's emotional states. In the process, we distinguish these 2 constructs from one another and demonstrate a causal relationship. The effects of dishonesty on empathic accuracy that we found were significant, but modest in size. Across 8 studies (n = 2,588), we find support for (a) a correlational and causal account of dishonest behavior reducing empathic accuracy; (b) an underlying mechanism of reduced relational self-construal (i.e., the tendency to define the self in terms of close relationships); (c) negative downstream consequences of impaired empathic accuracy, in terms of dehumanization and subsequent dishonesty; and (d) a physiological trait (i.e., vagal reactivity) that serves as a boundary condition for the relationship between dishonest behavior and empathic accuracy. (PsycINFO Database Record (c) 2019 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".