Magnetoencephalography adaptation reveals depth-cue-invariant object representations in the visual cortex
Bibliographic record
Abstract
Independent of edges and 2-D shape that can be highly informative of object identity, depth cues alone can also give rise to vivid and effective object percepts. The processing of different depth cues engages segregated cortical areas, and an efficient object representation would be one that is invariant to depth cues. Here, we investigated depth-cue invariance of object representations by measuring the category-specific response to faces-the M170 response measured with magnetoencephalography. The M170 response is strongest to faces and is sensitive to adaptation, such that repeated presentation of a face diminishes subsequent M170 responses. We used this feature of the M170 and measured the degree to which the adaptation effect is affected by variations in depth cue and 3-D object shape. Subjects viewed a rapid presentation of two stimuli-an adaptor and a test stimulus. The adaptor was either a face, a chair, or a face-like oval surface, and rendered with a single depth cue (shading, structure from motion, or texture). The test stimulus was always a shaded face of a random identity, thus completely controlling for low-level influences on the M170 response to the test stimulus. In the left fusiform face area, we found strong M170 adaptation when the adaptor was a face regardless of its depth cue. This adaptation was marginal in the right fusiform and negligible in the occipital regions. Our results support the presence of depth-cue-invariant representations in the human visual system, alongside size, position, and viewpoint invariance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".