Can language help to shape the way we think? A cross-linguistic investigation into the effect of noun-adjective order on conceptual representation
Bibliographic record
Abstract
Previous research demonstrates how language can shape thought (Boroditsky, 2001). Differences in cognition as a function of specific native language characteristics such as metaphor use (Boroditsky., 2001), grammatical gender use (Boroditsky et al., 2002), and spatial frames of reference (Pederson et al., 1998) are well documented. This study looked at the effect of opposing canonical English (adjective-noun) and canonical Spanish (noun-adjective) word order on conceptual representation in a sentence comprehension and image response task. Using a novel response time paradigm, both native English-speakers and native Spanish-speaking bilinguals read passages in English before responding to image combinations in both ‘artefact-colour’ order (reflecting Spanish canonical word order) and ‘colour-artefact’ order (reflecting English canonical word order). For Experiment 1, native English-speakers performed the task with or without articulatory-suppression, whilst in Experiment 2 the same task was performed by native Spanish-speaking bilinguals (without articulatory-suppression). In both experiments, a response bias was found for ‘artefact-colour’ over ‘colour-artefact’ image combinations, providing support for the idea that nouns represent conceptual ‘pegs’ upon which modifiers (such as adjectives) are often ‘hung’ (Lambert & Paivio, 1956). Experiment 2 however revealed a more pronounced ‘artefact-colour’ response bias in native Spanish-speaking bilinguals (who habitually use noun-adjective word order) compared to native English-speakers, providing support for the idea that language can help to shape the way we think.ReferencesBoroditsky, L. (2001). Does language shape thought?: Mandarin and English speakers’ conceptions of time. Cognitive Psychology, 43, 1-22.Boroditsky, L., Schmidt, L., & Phillips, W. (2003). Sex, Syntax, and Semantics. In Gentner & Goldin-Meadow (Eds.), Language in Mind: Advances in the Study of Language and Cognition. MIT Press: Cambridge, MA.Lambert, W. E., & Paivio, A. (1956). The influence of noun-adjective order on learning. Canadian Journal of Psychology, 10, 9-12.Pederson, E., Danziger, E., Wilkins, D., Levinson, S., Kita, S., & Senft, G. (1998). Semantic typology and spatial conceptualization. Language, 74, 557-589.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".