The Picture of the Linguistic Brain: How Sharp Can It Be? Reply to Fedorenko & Kanwisher
Bibliographic record
Abstract
What is the best way to learn how the brain analyzes linguistic input? Two popular methods have attempted to segregate and localize linguistic processes: analyses of language deficits subsequent to (mostly focal) brain disease, and functional Magnetic Resonance Imaging (fMRI) in health. A recent Compass article by Fedorenko and Kanwisher (FK, 2009) observes that these methods group together data from many individuals through methods that rely on variable anatomical landmarks, and that results in a murky picture of how language is represented in the brain. To get around the variability problem, FK propose to import into neurolinguistics a method that has been successfully used in vision research - one that locates functional Regions Of Interest (fROIs) in each individual brain.In this note, I propose an alternative perspective. I first take issue with FK's reading of the literature. I point out that, when the neurolinguistic landscape is examined with the right linguistic spectacles, the emerging picture - while intriguingly complex - is not murky, but rather, stable and clear, parsing the linguistic brain into functionally and anatomically coherent pieces. I then examine the potential value of the method that FK propose, in light of important micro-anatomical differences between language and high-level vision areas, and conclude that as things stand the method they propose is not very likely to bear much fruit in neurolinguistic research.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.006 | 0.020 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.024 | 0.060 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".