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Emotions before actions: When children see costs as causal

2024· article· en· W4393858146 on OpenAlexafffund
Claudia G. Sehl, Ori Friedman, Stephanie Denison

Bibliographic record

VenueCognition · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCognitive psychologyCognitionDevelopmental psychologySocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Adults expect people to be biased by sunk costs, but young children do not. We tested between two accounts for why children overlook the sunk cost bias. On one account, children do not see sunk costs as causal. The other account posits that children see sunk costs as causal, but unlike adults, think future actions cannot make up for sunk costs. These accounts make opposing predictions about whether children should see sunk costs as affecting emotions. Across three experiments, 4-7-year-olds (total N = 320) and adults (total N = 429) saw stories about characters who collected items that were easy or difficult to obtain, and predicted characters' emotions and actions. At all ages, participants anticipated that characters would feel sadder about high-cost objects, but only adults predicted that characters would keep high-cost objects. Our findings show that children see incurred costs as causal, and that costs are integrated children's and adults' theory of emotions. Moreover, the findings suggest that developmental differences in sunk cost reasoning may rest in children's incomplete mental accounting. We also discuss children's reasoning about rational and irrational action.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.019

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 head0.123
GPT teacher head0.414
Teacher spread0.291 · 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 designOther design
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

Citations4
Published2024
Admission routes2
Has abstractyes

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