Macaque spatiotemporal neural dynamics during perception of object-object occlusion images
Bibliographic record
Abstract
The dynamic neural mechanisms for recognizing partially occluded objects are not fully understood. Previous studies often used partial fragments (Tang et al., 2014) or geometric shapes (Bushnell et al., 2011; Namima & Pasupathy, 2021). We investigated neural dynamics under ecologically valid conditions where objects occlude each other as in natural scenes. We recorded neural activity using Utah arrays implanted in foveal V4 and posterior TE of two macaque monkeys. Fixating monkeys viewed eight single objects and 56 object-object occlusion stimuli (250 ms duration) — resulting from all pairings of the objects. Instantaneous firing rates were estimated using a 50 ms sliding window spike count. Using cross-temporal decoding (Meyers et al., 2008), we investigated the temporal dynamics of neural population coding for occluder and occluded objects using linear SVMs. Decodability of a front object versus other front objects was computed by averaging decoding accuracy across pairs sharing the same back object. Similarly, we assessed discriminability for each occluded object versus others. Results showed that representations of front objects emerge earlier, are more decodable and stable over time than the occluded objects. Additionally, representations in posterior TE lag behind those in V4 but exhibit a more stably decodable temporal code. To assess whether spatial and temporal representations are separable, we applied tensor component analysis (TCA; Williams et al., 2018). We modeled the neural data tensor by decomposing it into a spatial mode (representing object-specific neural patterns) and two temporal modes (capturing dynamics for front and back objects separately). We computed cross-validated variance explained (R²) compared to a baseline model. TCA models explained significantly more variance in V4 than in posterior TE, indicating that spatial and temporal representations are more separable in V4. TEp, intriguingly, exhibited less space-time separable dynamics, but more sustained decodability of front and back objects.
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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) | 0.001 | 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".