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Record W4308001699 · doi:10.31234/osf.io/dsp58

Novelty preferences depend on goals

2022· preprint· en· W4308001699 on OpenAlexafffund
Claudia G. Sehl, Stephanie Denison, Ori Friedman

Bibliographic record

Venuenot available
Typepreprint
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 CanadaUniversity of Waterloo
KeywordsNoveltySet (abstract data type)PsychologySocial psychologyCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

People are sometimes drawn to novel items, but other times prefer familiar ones. In the present research we show, though, that both children’s and adults’ preferences for novel versus familiar items depend on their goals. Across four experiments, we showed 4- to 7-year-olds (total N = 498) and adults (total N = 659) pairs of artifacts where one was familiar and the other was novel (e.g., a four-legged chair and ten-legged chair). In Experiment 1, children wanted to have familiar artifacts, but to learn about novel ones. Experiment 2 replicated this pattern using a simpler procedure, and found the same pattern in adults. In Experiment 3, 4- to 6-year-olds and adults more strongly preferred familiar items when choosing which they would rather have than when choosing which they would rather try using. Finally, Experiment 4 replicated adults’ preferences to have familiar items and learn about novel ones with an additional set of items. Together these findings show that preferences for novelty depend on people’s goals. We suggest these effects arise because children and adults are motivated both by the promise of information and the desire for safe options in high commitment decisions that entail risk.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient 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 score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0040.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0480.004

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 head0.431
GPT teacher head0.490
Teacher spread0.059 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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