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Record W2962546401 · doi:10.1037/xge0000639

The interpersonal costs of dishonesty: How dishonest behavior reduces individuals’ ability to read others’ emotions.

2019· article· en· W2962546401 on OpenAlexaff
Julia Lee, Ashley Elizabeth Hardin, Bidhan L. Parmar, Francesca Gino

Bibliographic record

VenueJournal of Experimental Psychology General · 2019
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsNuclear Waste Management Organization
FundersUniversity of MichiganHarvard Business SchoolWashington University in St. LouisHarvard University
KeywordsPsychologyDishonestySocial psychologyPsycINFOInterpersonal communicationEmpathic concernEmpathyTraitDehumanizationConstrual level theoryInterpersonal relationshipSocial perceptionDeceptionCognitive psychologyPerspective-taking

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.360
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2019
Admission routes1
Has abstractyes

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