Decoding Contextual Effects in Vision: A Cross-Species Behavioral Approach
Bibliographic record
Abstract
The significance of context in visual perception is undeniable. Our understanding of the natural world is shaped not just by the foveated visual objects but also by the surrounding scene and prior experiences. While the influence of context on vision has been demonstrated psychophysically, the underlying mechanisms integrating objects and surrounding information during scene comprehension are not fully understood. Studies have extensively examined "low-level" contextual effects, such as extra-classical receptive fields and surround suppression, yet gaps remain in comprehending how context affects "higher-level" visual recognition tasks. To elucidate these neural processes, a detailed examination of the neural networks involved is essential. Rhesus macaques, with their visual processing circuits akin to humans, present an ideal model for this purpose. In our study, we assessed the behavior of 90 human participants via Amazon Mechanical Turk in a binary match to sample object discrimination task, using images with varied contexts (full, incongruent, no context, etc.). The results revealed a significant alteration in human performance due to contextual changes, exhibiting a consistent behavioral pattern across context categories (trial-split reliability of ~0.8). This finding was crucial for comparison with macaques. After training monkeys (n=2) to achieve ≥80% accuracy in object categorization with full-context images, we exposed them to the same contextually manipulated images. The behavioral variance shared between humans and monkeys was significant (~31%), and not attributable to low-level image factors such as object size or contrast. Interestingly, naive macaque inferior temporal (IT) neural responses did not fully account for the observed human-monkey shared variance (13% of image-level explained shared variance), suggesting that the effects are likely driven more by learning processes and feedback mechanisms than by innate IT response statistics. This research paves the way for future investigations into the neural mechanisms of contextual modulation in primate vision.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".