Neural representation of occluded objects in visual cortex
Bibliographic record
Abstract
The ability of the human visual system to recognize occluded objects is striking, yet current models of vision struggle to account for this successfully. Previous studies investigating occlusion at both the behavioural and neural levels typically used simple shapes or cut outs as occluders, rather than other objects. The goal of the present study was to understand what best explains neural representations of occluded objects under more realistic occlusion i.e., when objects occlude other objects. We approached this by explicitly relating activity patterns of occluded objects (e.g. a cup occluding a face) with those generated when viewing the same objects in isolation (the cup or the face). In an event-related fMRI design, participants (N=12) performed a one-back task while being presented with objects presented in isolation (un-occluded), occluded by another object, or cut out by a corresponding object silhouette. We defined anatomical regions of interest in EVC (V1-V3), mid-visual regions (V4/LO1-3) and IT. Decoding analyses showed that EVC responses to occluded objects were better determined by the visible features whereas in IT inferred features also explained the responses well. Our data also showed strong effects of competition across multiple object representations in EVC, although these were significantly weaker in IT. Separate linear regression analyses further showed that the weights assigned to occluded objects in IT were well predicted by independent categorization judgements (higher weights corresponded to lower accuracy and slower RT). Whereas in EVC weights instead were predicted by the magnitude of occlusion, with smaller weights assigned as the percentage of object occluded increases. In sum our results demonstrate that IT better decouples responses to real-world occluded objects with robust representations evident across multiple competing objects. Thus, our data support the importance of investigating neural mechanisms underlying object recognition under more naturalistic occlusion scenarios.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".