Dynamic versus static stimuli for localization of the cerebral areas involved in face perception
Bibliographic record
Abstract
Functional Magnetic Resonance Imaging (fMRI) studies investigating human face perception use localizers to identify specific brain areas, such as the fusiform face area (FFA), occipital face area (OFA), and superior temporal sulcus (STS). These usually consist of a simple block design contrasting viewing of static pictures of faces and viewing of other objects. Functional localizers seldom identify all regions in all participants, however, reducing their utility in the study of single subjects. We asked whether the use of more ecologically valid dynamic stimuli, such as video clips of faces and objects, may result in a more reliable activation of face-processing areas in all participants. Sixteen young volunteers participated in an fMRI study that contained two functional localizers, one with static photographs and the other with dynamic video clips of faces and objects. The results showed that the use of the static localizer resulted in the identification of the FFA bilaterally in 13 participants, the left OFA in 11, the right STS and right OFA in eight, and the left STS in four. The use of the dynamic localizer allowed us to detect activity within the FFA bilaterally, the right STS and the right OFA in all 16 participants, and the left STS and left OFA in 13. Furthermore, face-selective regions identified with the dynamic localizer had peaks with 25% greater t-values and had, on average, twice many voxels than the regions identified with the static localizer. These findings suggest that the use of more realistic dynamic stimuli, rather than static photographs, results in a more reliable localization of the brain regions involved in face perception.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".