Early Visual Areas are Activated during Object Recognition in Emerging Images
Bibliographic record
Abstract
Human observers can reliably segment visual input and recognise objects. However, the underlying processes happen so quickly that they normally cannot be captured with fMRI. We used Emerging Images (EI), which contains a hidden object and extends the process of recognition, to investigate the involvement of early visual areas (V1, V2 and V3) and lateral occipital complex (LOC) in object recognition. The early visual areas were located with a retinotopy scan and the LOC with a localiser. The participants (N=8) then viewed an EI, followed by the hidden object’s silhouette (disambiguation), and then, the EI was repeated. BOLD responses before and after disambiguation were compared. The retinotopy parameters were used to back-project the BOLD response onto the visual field, creating spatially detailed maps of the activity change. V1 and V2 (but not V3) showed stronger response after disambiguation, while there was no difference in the LOC. The back-projections revealed no distinct pattern or changes in activity on object location, indicating that the activity in V1 and V2 is not specific for voxels corresponding to the object location. We found no difference before and after disambiguation in the LOC, which may be repetition suppression counteracting the effect of recognition.
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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 | 0.000 |
| 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) | 0.004 | 0.001 |
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".