Object motion representation in the macaque ventral stream – a gateway to understanding the brain’s intuitive physics engine
Bibliographic record
Abstract
Abstract Effective interaction with moving objects and the ability to infer and predict their motion (a core component of “intuitive physics”) is essential for survival in the dynamic world. How does the primate visual system process such stimuli, enabling predictive capabilities for dynamic stimuli statistics like motion velocity and expected trajectories? In this study, we probed brain areas in the ventral visual pathway of rhesus macaques implicated in object recognition (areas V4 and inferior temporal, IT, cortex) to evaluate how they represent object motion speed and direction. We assessed the relationship between the distributed population activity in the ventral stream and two distinct object motion-based behaviors—one reliant on information directly available in videos (speed discrimination) and the other predicated on predictive motion estimates from videos (future event predictions). Further, employing microstimulation strategies, we confirm the causal, functional role of the IT cortex in these behaviors. Our results underscore the need to re-examine the traditional functional segregation of the primate visual cortices into “what” and “where” pathways and provide empirical constraints to model their interaction for a better circuit-level understanding of visual motion and intuitive physics.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".