Depth-resolved ultra-high field fMRI reveals feedback contributions to surface motion perception
Bibliographic record
Abstract
Abstract Human visual surface perception has neural correlates in early visual cortex, but the extent to which feedback contributes to this activity is not well known. Feedback projections preferentially enter superficial and deep anatomical layers, while avoiding the middle layer, which provides a hypothesis for the cortical depth distribution of fMRI activity related to feedback in early visual cortex. Here, we presented human participants uniform surfaces on a dark, textured background. The grey surface in the left hemifield was either perceived as static or moving based on a manipulation in the right hemifield. Physically, the surface was identical in the left visual hemifield, so any difference in percept likely was related to feedback. Using ultra-high field fMRI, we report the first evidence for a depth distribution of activation in line with feedback during the (illusory) perception of surface motion. Our results fit with a signal re-entering in superficial depths of V1, followed by a feedforward sweep of the re-entered information through V2 and V3, as suggested by activity centred in the middle-depth levels of the latter areas. This positive modulation of the BOLD signal due to illusory surface motion was on top of a strong negative BOLD response in the cortical representation of the surface stimuli, which depended on the presence of texture in the background. Hence, the magnitude and sign of the BOLD response to the surface strongly depended on background properties, and was additionally modulated by the presence or absence of illusory motion perception in a manner compatible with feedback. In summary, the present study demonstrates the potential of depth resolved fMRI in tackling biomechanical questions on perception that so far were only within reach of invasive animal experimentation.
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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".