Thinking in a foreign language distorts allocation of cognitive effort: Evidence from reasoning.
Bibliographic record
Abstract
Bilinguals, in their foreign language, are spared from common decision-making biases. Typically, this “Foreign Language Effect” results in increased accuracy. We examined the Foreign Language Effect in the context of logical reasoning, in which reasoners are required to track the logical status of a syllogism, ignoring its believability. Across three experiments, we found the reverse Foreign Language Effect; foreign language reasoners are less able to evaluate the logical structure of syllogisms, but no less biased by their believability. One path to succeeding in reasoning tasks is always engaging in reflective processing. A more efficient strategy is metacognitively tracking whether belief-based intuitions conflict with logic-based intuitions and only reflecting when such conflict is present. We provide evidence that foreign language reasoners are less accurate because they struggle to detect belief-logic conflict, and in turn fail to engage in reflective processing when necessary to override the incorrect, intuitive response. We propose that foreign language reasoners are less able to detect belief-logic conflict either due to weakened intuitions or due to a more conservative threshold for the detection of conflict between multiple competing intuitions. Data for the experiments can be accessed publicly at https://osf.io/phbuq/
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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".