Salient images: Evidence for a component process architecture for visual imagination
Bibliographic record
Abstract
Abstract The ability to visualise different people and places in imagination is a core element of human cognition. Contemporary neuroimaging research highlights regions of primary visual cortex as important in instantiating images in the mind’s eye. Here we combine task-based functional magnetic resonance imaging with measures of intrinsic brain organisation to show the ventral attention, or salience, network, also plays an important role in visual imagination. In a task-based study we replicated prior observations of regions of medial occipital cortex, including Brodmann Area 17, showing greater neural activity during imagination than when perceiving similar stimuli. In addition, we found regions of the ventral attention network, and in particular right dorsolateral prefrontal cortex (BA 9/46), were activated by the same acts of imagination. In a subsequent resting-state study, we demonstrated medial occipital cortex regions form a functional network with regions of the ventral attention network that were also active during imagination. Furthermore, this pattern was maximised in an overlapping region of right dorsolateral prefrontal cortex for individuals whose self-reported imagery had stronger negative affect. Together these data suggest visual imagination relies on functional interactions between regions of primary visual cortex with the ventral attention system, and in particular, with a region of right dorsolateral prefrontal cortex.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".