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Record W2266763298 · doi:10.1177/0301006616629033

Big Money: The Effect of Money Size on Value Perceptions and Saving Motivation

2016· article· en· W2266763298 on OpenAlexaff
Johanna Peetz, Monica Soliman

Bibliographic record

VenuePerception · 2016
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsWilfrid Laurier UniversityCarleton University
Fundersnot available
KeywordsAffect (linguistics)CashPerceptionValue for moneyValue (mathematics)Time value of moneyPsychologyMoney measurement conceptMonetary economicsSocial psychologyEconomicsVelocity of moneyEndogenous moneyFinanceMonetary policyPublic economicsComputer science

Abstract

fetched live from OpenAlex

Motivated perception has been shown to affect people's estimates of money (e.g., perceiving coins as larger than real size). In the present research, we examine whether simply varying the size of a picture of money can affect its perceived value and subsequent decisions. Participants presented with a picture of money enlarged by 15% perceived the depicted money as more valuable compared with those seeing a real-size picture (Study 1). When told to imagine their own cash and banked money in the depicted form, participants presented with a picture enlarged by 15% felt more subjectively wealthy and reported fewer intentions to conserve their money compared with those seeing a real-size picture of the same money (Study 2). Together, these studies suggest that judgments about money and even attitudes toward personal spending can be influenced by manipulating the size of a picture of money.

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)0.011
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.043
GPT teacher head0.364
Teacher spread0.321 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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