Reversal or no reversal, that is the question – A conceptual replication of cross-cultural spatial-numerical associations
Bibliographic record
Abstract
Reading direction is a central determinant of spatial–numerical associations, yet evidence from right-to-left (RL) readers remains inconsistent, ranging from weakened to reversed spatial-numerical associations (SNAs). Shaki et al. (2009) provided the only systematic cross-cultural comparison controlling for reading direction of both words and digits. They reported canonical SNAs in Canadian left-to-right (LR) readers, no SNAs in Hebrew readers with mixed habits (RL words, LR digits), and a reversed SNAs in Palestinian adults with consistent RL reading. Subsequent studies produced mixed results for populations with similar reading profiles, potentially reflecting uncontrolled bilingualism, differing directional habits, and culturally shaped spatial routines, such as object counting and finger counting. Given the theoretical importance of a reverse SNAs and the absence of a successful replication of this finding in standard parity tasks, the present study replicates Shaki et al. (2009), using the refined go/no-go methodology while additionally documenting participants’ object- and finger-counting preferences. We will test German LR readers, Israeli mixed-direction readers, and Palestinian RL readers with adequately powered samples (n = 39 per subgroup). This replication will clarify whether and how culturally transmitted directional habits contribute to spatial–numerical mappings.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".