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Record W4248948618 · doi:10.32920/ryerson.14656899.v1

Spatial memory in Canadian and Indian young and older adults: the effects of age, culture and cultural orientation

2021· preprint· en· W4248948618 on OpenAlexafffundabout
Khushi Patel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsToronto Metropolitan UniversitySystems, Applications & Products in Data Processing (Canada)
FundersMitacs
KeywordsCollectivismPsychologyIndividualismContext (archaeology)Orientation (vector space)Developmental psychologyYoung adultSocial psychologySpatial contextual awarenessCultural diversityCross-cultural studiesDemographyGeographySociologyPolitical scienceAnthropology

Abstract

fetched live from OpenAlex

Cross-cultural research suggests that individualistic Americans have a tendency to process focal objects; in contrast, collectivist Asians have a tendency to bind objects and context (Park & Huang, 2010). However, little is known whether the reported cultural differences are moderated by cultural orientation. In light of these results and the well-reported age-related decline in binding abilities, the current study examined cultural and age differences in cultural orientation, spatial memory and strategy use with young and older Canadian and Indian adults. There was little difference between Canadian and Indian participants’ cultural orientation. While cultural orientation did not moderate the relationship between culture and spatial memory, spatial memory and strategy use differed as a function of age. The use of context-specific strategies resulted in performance gains in older adults, however overall older adults had poor spatial memory, with Indian older adults scoring significantly lower than Canadian older adults on the courtyard task.

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)0.002
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 score0.084
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.210
Teacher spread0.207 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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Same topicSpatial Cognition and NavigationFrench-language works237,207