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Record W4288384888 · doi:10.1037/pag0000702

Absence of a mere-exposure effect in older and younger adults.

2022· article· en· W4288384888 on OpenAlexfundno aff
Jason K. Chow, Stephen Rhodes, Nicholas O. Rule, Bradley R. Buchsbaum, Lynn Hasher

Bibliographic record

VenuePsychology and Aging · 2022
Typearticle
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPsychologyPsycINFOYoung adultDevelopmental psychologyAge groupsDemographyMEDLINE

Abstract

fetched live from OpenAlex

The mere-exposure effect, in which repeated stimuli are liked more than novel stimuli, is a well-known effect. However, little research has studied adult age differences in mere-exposure effects, despite possible applications in helping older adults transition to new living environments. Here, we report four experiments assessing mere-exposure to neutral-face stimuli in groups of older and younger adult participants tested online. In each experiment, repeated face exposure did not increase liking within either age group; rather, Bayesian evidence favored the null hypothesis of no effect. Older adults reported higher overall liking ratings relative to younger adults, and both groups preferred younger faces, though this tendency was stronger in the younger group. Further exploratory analysis considering factors such as gender or race of the faces and participants did not reveal any consistent results for the mere-exposure effect. We discuss these findings in relation to other recent studies reporting mixed evidence for mere-exposure effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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)0.002
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.010
GPT teacher head0.310
Teacher spread0.300 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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