Coupling of regional activations in a human brain during an object and face affect recognition task
Bibliographic record
Abstract
Magnetic field tomography (MFT) was used to extract estimates for distributed source activity from average and single trial MEG signals recorded while subjects identified objects (including faces) and facial expressions of emotion. Regions of interest (ROIs) were automatically identified from the MFT solutions of the average signal for each subject. For one subject the entire set of MFT estimates obtained from unaveraged data was also used to compute simultaneous time series for the single trial activity in different ROIs. Three pairs of homologous areas in each hemisphere were selected for further analysis: posterior calcarine sulcus (PCS), fusiform gyrus (FM), and the amygdaloid complex (AM). Mutual information (MI) between each pair of the areas was computed from all single trial time series and contrasted for different tasks (object or emotion recognition) and categories within each task. The MI analysis shows that through feed-forward and feedback linkages, the "computation" load associated with the task of identifying objects and emotions is spread across both space (different ROIs and hemispheres) and time (different latencies and delays in couplings between areas)-well within 200 ms, different objects separate first in the right hemisphere PCS and FG coupling while different emotions separate in the right hemisphere FG and AM coupling, particularly at latencies after 200 ms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".