Reverse Spatiotemporal Hierarchy during Cross-modal Memory Recall and Imagery
Bibliographic record
Abstract
Abstract Recalling past events is often accompanied by mental imagery of those experiences. Based on previous research, this process engages memory- and sensory-related brain areas. However, the underlying spatiotemporal dynamics remain poorly investigated. Here, we used naturalistic videos of audiovisual events and recorded fMRI data during the tasks in which human participants recalled visual contents when hearing associated sounds and recalled sounds when watching silent videos, after they had well memorized the video contents. With time-resolved fMRI multivariate pattern analyses, we observed reverse spatiotemporal hierarchy during the visual memory recall and imagery: the neural activity in primary visual cortex was delayed compared with high-order visual areas. A similar pattern was found during auditory memory recall and imagery, where the bottom-up progression from the mid-level planum temporale to the high-level superior temporal gyrus observed during auditory perception was absent. However, the primary auditory area was not involved, suggesting modality differences in the role of primary sensory areas in corresponding memory recall. We also observed the activity of the hippocampus, the parahippocampal cortex, the retrosplenial cortex, and the precuneus and examined their temporal dynamics. Overall, our study provided both spatial and temporal accounts of neural activity during the cross-modal memory recall and imagery.
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 | 0.001 |
| 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".