Culture and Attention: Future Directions to Expand Research Beyond the Geographical Regions of WEIRD Cultures
Bibliographic record
Abstract
Henrich et al. (2010) highlighted the necessity of broadening the range of regions for cross-cultural investigation in their seminal paper "The weirdest people in the world." They criticize the current psychological framework for relying dominantly on American undergraduate students for their participant database, and state that there is a risk associated with investigating human nature by focusing solely on a unique population. This line of research has, over the past 30 years, successfully demonstrated the diversity of human cognition. However, it is true that there are still only a limited number of studies that have extended their geographical regions of research outside of G7 (Canada, France, Germany, Italy, Japan, United Kingdom, and United States) and G20 countries (Argentina, Australia, Brazil, China, India, Indonesia, Mexico, Russia, Saudi Arabia, South Africa, South Korea, Turkey, EU countries, and the above G7 countries). In order to fully examine the issue of culture and cognition, we maintain that the field of psychology must extend its research globally. In this paper, we will briefly discuss the history of cross-cultural research in the 1960s which can be seen as the beginning of addressing the above concerns, and review some contemporary empirical studies which took over their 1960s predecessors' mission. Here we address three strengths of extending the geographical scope to advance cultural psychology. In the second half of the paper, we will introduce our preliminary study conducted in Mongolia as a sample case study to demonstrate a way of administering cultural psychological research outside of the existing research field. We will then discuss implications of this line of research, and provide tips on how to open a new research site.
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 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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.042 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".