Negation and the Brain
Bibliographic record
Abstract
Abstract This chapter reports an investigation into possible brain bases for negation. It begins with a review of negation experiments that used behavioral studies (measuring Reaction Time—RT), and functional Magnetic Resonance Imaging (fMRI) experiments that sought to identify local activations that correlate with the presence of negation. The chapter dwells on a major methodological problem that permeates the experimental study of negation processing, and proposes a solution: instead of overt negation, we study expressions that contain a covert negation—expressions that are Downward Entailing (DE) as evinced by their ability to reverse inferences and license NPIs in their scope. DE operators are thus taken to contain a hidden, or covert, negation, and contrast with the Upward Entailing counterparts (few vs. many; less vs. more). The chapter reviews behavioral experiments in healthy adults that indicate that DE has a processing cost, and an fMRI study that finds a single brain location for this computation. These results serve as a basis for an experiment on individuals with Broca’s aphasia. Tests with DE and UE quantifiers with these patients resulted in a mixed picture, which is discussed and its implications are derived.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".