Motion-defined face and object recognition: Evidence from psychophysics, neuropsychology, and functional imaging
Bibliographic record
Abstract
The studies we report concern recognition of complex objects, such as faces, defined solely by motion cues. Dynamic object shape cues, such as structure-from-motion, are thought to be largely mediated by dorsal-stream areas, such as MT and MST. However, object recognition in general, and unfamiliar face recognition in particular, are strongly believed to be mediated by ventral stream areas. Thus, recognition tasks involving motion defined faces offer a unique opportunity to probe dorsal-ventral integration and its role in complex object recognition. Here, we report data from several psychophysical, neuropsychological, and functional imaging studies that we have conducted in exploring these questions. Our results show that (a) purely motion-defined unfamiliar faces can be recognized, (b) classic effects such as the Inversion Effect may also apply to the recognition of unfamiliar faces defined by motion, (c) intact cortical motion processing mechanisms are necessary for the perception of structure-from-motion objects, (d) intact cortical face processing mechanisms are necessary for the recognition and learning of motion defined faces, and finally, (e) motion-defined faces may not engage the Fusiform Face Area, but the Occipital Face Area. Taken together, our results make several important theoretical contributions. First, that dorsal-ventral integration is necessary for motion-defined object recognition. Second, putative face areas identified thus far with face photographs may not be responsive to dynamic 3D percepts. Finally, the presence of this integration suggests our simplistic hierarchical view of the ventral stream is incomplete.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".