Cognitive Mode Detectable with Task-Based fMRI: Default Mode B (DMB)
Bibliographic record
Abstract
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 Default Mode B (DMB), a task-negative and late-trial peaking cognitive mode. The BOLD signal configurations associated with DMB are modulated by a range of tasks, and here we present eight. For each task, we report: (1) specific pattern-based (as opposed to coordinate-based) anatomical details essential for distinguishing DMB from other BOLD-based cognitive modes, and (2) task-induced BOLD signal changes associated with DMB over a range of task conditions. In order to facilitate recognition, we nick-named the anatomical patterns specific to DMB as follows: (1) In Flight, (2) Medial Temporal Dots, (3) Snowman Nose, (4) Angel Wings, and (5) Tripod. Evidence for DMB was derived from the timing and magnitude of task-induced BOLD signal changes induced by the following tasks: working memory, spatial capacity, semantic association, evidence integration, Raven’s matrices, autobiographical event simulation, meditation and social perception. It was observed that deactivations in DMB were sensitive to cognitive load during attention to specific features of the external environment, based on evidence from working memory, spatial capacity, semantic association, evidence integration, and Raven’s matrices. It was also observed that activations in DMB involved a cognitive process for engaging in mental projection into self-relevant social narratives, based on evidence from autobiographical event simulation, meditation, and social perception. Future research may explore DMB activation over a wider range of tasks in larger samples.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".