One Label or Two? Linguistic Influences on the Similarity Judgment of Objects between English and Japanese Speakers
Bibliographic record
Abstract
Recent findings have re-examined the linguistic influence on cognition and perception, while identifying evidence that supports the Whorfian hypothesis. We examine how English and Japanese speakers perceive similarity of pairs of objects, by using two sets of stimuli: one in which two distinct linguistic categories apply to respective object images in English, but only one linguistic category applies in Japanese; and another in which two distinct linguistic categories apply to respective object images in Japanese, but only one applies in English. We conducted four studies and tested different groups of participants in each of them. In Study 1, we asked participants to name the two objects before engaging in the similarity judgment task. Here, we expected a strong linguistic effect. In Study 2, we asked participants to engage in the same task without naming, where we assumed that the condition is close enough to our daily visual information processing where language is not necessarily prompted. We further explored whether the language still influences the similarity perception by asking participants to engage in the same task basing on the visual similarity (Study 3) and the functional similarity (Study 4). The results overall indicated that English and Japanese speakers perceived the two objects to be more similar when they were in the same linguistic categories than when they were in different linguistic categories in their respective languages. Implications for research testing the Whorfian hypothesis and the requirement for methodological development beyond behavioral measures are discussed.
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 | 0.002 | 0.017 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".