Looming and receding visual networks in awake marmosets investigated with fMRI
Bibliographic record
Abstract
An object that is looming toward a subject or receding away contains important information for determining if this object is dangerous, beneficial or harmless. This information (motion, direction, identity, time-to-collision, size, velocity) is analyzed by the brain in order to execute the appropriate behavioral responses depending on the context: fleeing, freezing, grasping, eating, exploring. In the current study, we performed ultra-high-field functional MRI (fMRI) at 9.4T in awake marmosets to explore the patterns of brain activation elicited by visual stimuli looming toward or receding away from the monkey. We found that looming and receding visual stimuli activated a large cortical network in frontal, parietal, temporal and occipital cortex in areas involved in the analysis of motion, shape, identity and features of the objects. Looming stimuli strongly activated a network composed of portions of the pulvinar, superior colliculus, putamen, parietal, prefrontal and temporal cortical areas. These activations suggest the existence of a network that processes visual stimuli looming toward peripersonal space to predict the consequence of these stimuli. Together with previous studies in macaque monkeys, these findings indicate that this network is preserved across Old and New World primates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".