Culture and attention: Recent empirical findings and new directions in cultural psychology
Bibliographic record
Abstract
Abstract Over the past 3 decades, cultural psychologists have empirically investigated the influence of cultural meaning systems on human psychology. Under the rubric of holistic versus analytic thought, researchers have demonstrated that there are substantial cultural variations in social cognition, and that such variations are observable even in so‐called fundamental psychological processes, such as attention. The aim of this paper is to review 3 major themes in culture and attention: (1) culture and attention to nonsocial scenes, (2) culture and attention to social scenes, and (3) culture and aesthetics. The paper also discusses 4 major strands of research that could be considered important candidates to further advance the understanding of culture and attention: (1) research on “culture ➔ human psychological processes,” where we investigate how culture influences modes of attention; (2) research on “human psychology ➔ cultural processes,” where we investigate how those who hold a specific mode of attention create cultural products and tangible representations of culturally shared meanings; (3) research on cultural neuroscience, where we investigate underlying mechanisms and processes of specific modes of attention; and (4) research on cultural transmission processes, where we investigate how specific modes of attention is taught by adults and internalized by children.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".