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Record W7154609112 · doi:10.48448/chyy-r722

Evaluating actions: Do young children prefer actions completed efficiently over those completed inefficiently?

2025· other· W7154609112 on OpenAlexaff
Cognitive Science Society 2025, Stephanie Denison, Ori Friedman, Claudia G. Sehl

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCharacter (mathematics)PerceptionPreferenceChoseReplicate

Abstract

fetched live from OpenAlex

Efficiency informs perceptions and expectations of people’s actions from early in life. We examined whether young children aged three and four consider efficiency when evaluating how well agents completed goals. In three experiments, we showed children scenarios where two characters each walked to target objects, and then asked children which character did a better job. In the first experiment, children appeared to consider efficiency. They more often chose a character who took a direct path over one who took an indirect path, but only when the latter character could have taken a shorter path. Two follow-up experiments, though, failed to replicate this pattern and the success in the initial experiment could be explained in terms of the features of the paths (not strictly related to efficiency) used in that experiment. The findings suggest, then, that three- and four-year-olds do not yet use efficiency to normatively evaluate actions. We consider two alternative explanations for this.

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)0.009
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0120.028
Science and technology studies0.0090.012
Scholarly communication0.0040.002
Open science0.0080.004
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0270.014

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 head0.129
GPT teacher head0.419
Teacher spread0.290 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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