Cognitive Mode Detectable with Task-Based fMRI: Language (LAN)
Bibliographic record
Abstract
In the context of task-based functional magnetic resonance imaging (fMRI), cognitive modes can be defined as task-general cognitive/sensory/motor processes which reliably elicit specific blood-oxygen-level-dependent (BOLD) signal pattern configurations. A number of cognitive modes are detectable with task-based fMRI, and here we focus on language (LAN). The BOLD signal configurations associated with LAN are modulated by a range of tasks, and here we present six. For each task, we report: (1) specific pattern-based (as opposed to coordinate-based) anatomical details essential for distinguishing LAN from other BOLD-based cognitive modes, and (2) task-induced BOLD signal changes associated with LAN over a range of task conditions. In order to facilitate recognition, we nick-named the anatomical patterns specific to LAN as follows: (1) Tears Blown Leftwards/Eyebrows, (2) Rail Shot Coronal, (3) Rail Shot Axial, and (4) Disappearing Face. Evidence for LAN was derived from the timing and magnitude of task-induced BOLD signal changes induced by the following tasks: two lexical decision tasks, metrical stress, noun/verb discrimination, semantic association, and facial emotion discrimination. The evidence consistently supports that the LAN includes both activation and suppression depending on the linguistic information processing required to achieve the task goals, as well as possible extension to non-verbal communication.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".