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Preschoolers' Current Desires Warp Their Choices for the Future

2006· article· en· W2109255707 on OpenAlexaff
Cristina M. Atance, Andrew N. Meltzoff

Bibliographic record

VenuePsychological Science · 2006
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Ottawa
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsChoseBaseline (sea)PsychologyIntervention (counseling)Developmental psychologyCognitionMultiple baseline designSocial psychology

Abstract

fetched live from OpenAlex

We introduce a new paradigm to assess how children's choices for the future are influenced by their current desires. Forty-eight 3-, 4-, and 5-year-olds were assigned to one of four conditions. In two of these conditions (intervention), we manipulated children's current state of thirst by allowing them to eat pretzels. In the remaining two conditions (baseline), we did not give them pretzels. The children were then asked to choose between water and pretzels. In one intervention and one baseline condition, they chose what they would like "now," whereas in the other intervention and baseline conditions, they chose what they would like "tomorrow." Results revealed that, despite children's overwhelming desire for pretzels in the baseline conditions, children in both intervention conditions chose water. The data support the notion that children's current state influences not only their choices for the present, but also their choices for the future. We discuss this finding in terms of both cognitive-developmental and adult social-cognitive theory.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.362
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), 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

Citations95
Published2006
Admission routes1
Has abstractyes

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