East-West cultural differences in encoding objects in imagined social contexts
Bibliographic record
Abstract
It has been shown in literature that East Asians are more inclined to process context information than individuals in Western cultures. Using a context memory task that requires studying object images in social contexts (i.e., rating objects in an imagined social or experiential scenario), our recent study revealed an age-invariant advantage for Chinese young and older participants compared to their Canadian counterparts in memory for encoding contexts. To examine whether this cultural difference also occurred during encoding, this follow-up report analyzed encoding performance and its relationship to subsequent memory based on the same data from the same task of the same sample. The results revealed that at encoding, Chinese participants provided higher ratings of objects, took longer to rate, and reported more vivid imagery of encoding contexts relative to their Canadian counterparts. Furthermore, only Chinese participants rated objects with recognized context at retrieval higher and slower relative to those with misrecognized context. For Chinese participants, primarily older adults, slower ratings were only related to better context memory but not item memory. Importantly, Chinese participants' context memory advantage disappeared after controlling for encoding differences. Taken together, these results suggest that Chinese participants' memory advantage for social contexts may have its origin in the construction of elaborative and meaningful object-context associations at encoding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".