Adaptation to Up/Down Head Rotation in Face Selective Cortical Areas
Bibliographic record
Abstract
Although faces are naturally seen in both left/right and up/down rotated views, virtually all fMRI work on the representation of face views has examined only left/right rotation around frontal views. Accordingly, we designed an fMRI adaptation study to test multiple cortical areas for up/down viewpoint selectivity. Face-selective regions of interest were determined in a block-designed scan comparing responses to faces versus houses. This identified five face-selective regions of interest: fusiform face area (FFA), occipital face area (OFA), lateral occipital complex (LOC), superior temporal sulcus (STS), and inferior frontal sulcus (IFS). Event-related scans with a cross-adaptation paradigm were used to examine BOLD signals in each face region. Subjects adapted to frontal, up 20°, or down 20° views followed by one of these as a test view, thus producing nine different adapt/test combinations. Twelve subjects with normal vision were scanned. An initial two way ANOVA examined effects of hemisphere and self-adaptation (i.e. identical test and adapt stimuli). This analysis showed an effect of hemisphere (right magnitudes larger) only in FFA, and significant adaptation effects in FFA (p <0.001), OFA (p <0.01), and IFS (p <0.028). A second ANOVA compared results for all adapt and test view combinations to their no adapt conditions in these three areas. FFA and IFS showed a significant cross-adaptation as well as self-adaptation. In general, upward faces produced greater adaptation than adaptation to frontal or downward faces in these areas, thus indicating view selective tuning in the up/down direction. Results in OFA, however, suggest an invariance to up/down head rotation. Thus, up/down head rotation is encoded in some but not all face selective cortical areas.
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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 | 0.000 | 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) | 0.002 | 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".