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Record W2127744696 · doi:10.1093/geronb/gbt006

Aging, Culture, and Memory for Categorically Processed Information

2013· article· en· W2127744696 on OpenAlexafffund
Lixia Yang, Wenfeng Chen, Andy H. Ng, Xueliang Fu

Bibliographic record

VenueThe Journals of Gerontology Series B · 2013
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsYork UniversityToronto Metropolitan University
FundersNational Key Research and Development Program of ChinaCanadian Institutes of Health Research
KeywordsCategorizationTask (project management)PsychologyCognitionEthnic groupTest (biology)Cognitive psychologyComputer scienceArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: Literature on cross-cultural differences in cognition suggests that categorization, as an information processing and organization strategy, was more often used by Westerners than by East Asians, particularly for older adults. This study examines East-West cultural differences in memory for categorically processed items and sources in young and older Canadians and native Chinese with a conceptual source memory task (Experiment 1) and a reality monitoring task (Experiment 2). METHOD: In Experiment 1, participants encoded photographic faces of their own ethnicity that were artificially categorized into GOOD or EVIL characters and then completed a source memory task in which they identified faces as old-GOOD, old-EVIL, or new. In Experiment 2, participants viewed a series of words, each followed either by a corresponding image (i.e., SEEN) or by a blank square within which they imagined an image for the word (i.e., IMAGINED). At test, they decided whether the test words were old-SEEN, old-IMAGINED, or new. RESULTS: In general, Canadians outperformed Chinese in memory for categorically processed information, an effect more pronounced for older than for young adults. DISCUSSION: Extensive exercise of culturally preferred categorization strategy differentially benefits Canadians and reduces their age group differences in memory for categorically processed information.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.760
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

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

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 head0.082
GPT teacher head0.367
Teacher spread0.285 · 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 teacher head, 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

Citations35
Published2013
Admission routes2
Has abstractyes

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