MétaCan
Menu
Back to cohort
Record W3161962566 · doi:10.1037/dev0001174

Oh … so close! Children’s close counterfactual reasoning and emotion inferences.

2021· article· en· W3161962566 on OpenAlexaff
Tiffany Doan, Ori Friedman, Stephanie Denison

Bibliographic record

VenueDevelopmental Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyCounterfactual thinkingDevelopmental psychologyTheory of mindCognitive psychologySocial psychologyCognition

Abstract

fetched live from OpenAlex

= 227, Experiment 4) consider close counterfactual alternatives when inferring other people's emotions. In Experiment 1, 6-year-olds (but not 4- and 5-year-olds) inferred that an agent would feel sadder about winning a mediocre prize if she later found out that a more attractive one could have easily been won. However, children of all ages failed to judge whether the better outcome could have easily happened. In Experiment 2, when 5- and 6-year-olds knew the locations of the prizes beforehand, they inferred that an agent would be equally happy about winning a mediocre prize, regardless if she almost won a better prize or not. Again, they did not recognize when the better outcome was a close counterfactual possibility. In Experiment 3, we included extra cues to the closeness of the alternative and both 5- and 6-year-olds inferred that she would feel sadder about winning a mediocre prize, and 6-year-olds acknowledged that the attractive prize was a close counterfactual alternative. In Experiment 4, adults considered close counterfactuals when inferring emotions. Our findings suggest that close counterfactuals influence children's emotion inferences before they become able to acknowledge their closeness. (PsycInfo Database Record (c) 2021 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental PsychologySame topicChild and Animal Learning DevelopmentFrench-language works237,207