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Record W4214683575 · doi:10.1016/j.jecp.2022.105385

Lie-telling for personal gain in children with and without externalizing behavior problems

2022· article· en· W4214683575 on OpenAlexaff
Victoria Talwar, Jennifer Lavoie

Bibliographic record

VenueJournal of Experimental Child Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyNormativeDevelopmental psychologyLyingTheory of mindSocial psychologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

Few studies have examined the lie-telling behavior of children who have externalizing problems using experimental procedures. In the current study, children's lie-telling for personal gain (N = 110 boys aged 6-11 years) was examined using an experimental paradigm in relation to their theory-of-mind abilities and inhibitory control as well as their moral evaluations of truths and lies. Children with externalizing behavior problems (n = 53) were significantly more likely to lie and to be less skilled at lying than a typical comparison group (n = 57). Children who had lower theory-of-mind scores were significantly more likely to tell a lie for personal gain compared with those who had higher theory-of-mind scores. Children with externalizing problems who told personal gain lies were also more likely to rate tattle truths more positively than other children. For a subsample of children (n = 55), parent-reported diaries of the frequency of children's lies over 2 weeks revealed a higher frequency of lies by children with externalizing problems compared with the typical comparison group. Children whose parents reported a high frequency of lies for their children were also more likely to lie in the experimental personal gain lie paradigm. Results suggest that children with externalizing behavior may have a different pattern of lie-telling than has been previously reported for normative lie development.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.359
Teacher spread0.327 · 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.

Study designObservational
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

Citations13
Published2022
Admission routes1
Has abstractyes

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