Cognitive Mode Detectable with Task-Based fMRI: Maintaining Internal Attention (MAIN)
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 maintaining internal attention (MAIN), a mid-trial-peaking cognitive mode. The task-induced BOLD signal changes associated with MAIN are modulated by a range of tasks, and we present seven here. For each task, we report: (1) highly specific pattern-based (as opposed to coordinate-based) anatomical details essential for distinguishing MAIN from other BOLD-based cognitive modes, and (2) task-induced BOLD signal changes associated with MAIN across a range of task conditions. In order to facilitate recognition, we nick-named the anatomical patterns specific to MAIN as follows: (1) Left-Lateralized Upper Triangle, (2) Left-Lateralized Lower Triangle, (3) Right-Handed Crab Claw, (4) Found a Peanut. Evidence for MAIN was derived from the timing and magnitude of task-induced BOLD signal changes for following tasks: autobiographical event simulation, verbal working memory, non-verbal working memory, thought generation, task-switching, self-reference, and semantic association. Inspection of the task-induced BOLD signal changes associated with MAIN, over the range of tasks mentioned above, consistently supported the cognitive mode interpretation of maintaining attention to internal mental representations.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".