Cultural differences in strategic behavior: A study in computational estimation.
Bibliographic record
Abstract
Imbo and LeFevre (2009) observed that Asians (responding in their 2nd language) selected strategies less adaptively than did non-Asians (responding in their 1st language). In the present research, we tested whether adaptive strategy selection is (a) really more resource demanding for Asians than for non-Asians or (b) more resource demanding for participants answering in a nonpreferred language. Three groups of participants were tested on a computational estimation task (e.g., 42 × 57 ≈ ?) in no-load and load conditions: 40 Belgian-educated adults who answered in their first language (Dutch), 40 Chinese-educated adults who answered in their first language (Chinese), and 40 Chinese-educated adults who answered in their second language (English). Although the Chinese were faster and more accurate than the Belgians, they selected strategies less adaptively. That is, the Chinese were less likely to choose the strategy that produced the best estimate; this was especially so when their working memory was loaded. Further, we also observed that the Chinese who answered in English were slower than the Chinese who answered in Chinese; this difference was larger for difficult strategies and under working memory load. These results are interpreted in terms of the encoding complex model, whereas the explanation for the adaptivity results is based on cultural differences in educational history.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".