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Record W4321370461 · doi:10.1080/00221325.2023.2177522

Attentional Control Moderates the Relation between Sympathy and Ethical Guilt

2023· article· en· W4321370461 on OpenAlexaff
Sebastian P. Dys, Marc Jambon, Stephanie Buono, Tina Malti

Bibliographic record

VenueThe Journal of Genetic Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsWilfrid Laurier UniversityUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsSympathyPsychologyCognitionSocial psychologyDevelopmental psychologyControl (management)Remorse

Abstract

fetched live from OpenAlex

In response to ethical transgressions, some children respond with ethical guilt (e.g., remorse), while others do not. The affective and cognitive precursors of ethical guilt have been widely studied on their own, however, few studies have looked at the interaction of affective (e.g., sympathy) and cognitive (e.g., attention) precursors on ethical guilt. This study examined the effects of children’s sympathy, attentional control, and their interaction on 4 and 6-year-old children’s ethical guilt. A sample of 118 children (50% girls, 4-year-olds: Mage = 4.58, SD = .24, n = 57; 6-year-old: Mage = 6.52, SD = .33, n = 61) completed an attentional control task and provided self-reports of dispositional sympathy and ethical guilt in response to hypothetical ethical violations. Sympathy and attentional control were not directly associated with ethical guilt. Attentional control, however, moderated the relation between sympathy and ethical guilt, such that sympathy was more strongly related to ethical guilt at increasing levels of attentional control. This interaction did not differ between 4- and 6-year-olds or boys and girls. These findings illustrate an interaction between emotion and cognitive processes and suggest that promoting children’s ethical development may require a focus on both attentional control and sympathy.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.070
GPT teacher head0.392
Teacher spread0.322 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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