Motion-Induced Object Position Adaptation in Macaque IT Cortex: Temporal Processing Limitations of Neural Networks
Bibliographic record
Abstract
Recent studies have demonstrated that the macaque inferior temporal (IT) cortex, a key area in the ventral visual pathway, supports not only object identification but also object position estimation—a function previously attributed to dorsal-stream mechanisms. In parallel, artificial neural networks (ANNs) optimized for object recognition replicate this positional decoding capability. Such findings invite an intriguing question: If these ventral-stream-aligned ANNs can recapitulate positional coding, can they also exhibit systematic positional biases induced by adaptation to motion, analogous to the well-known human aftereffects? We tested this by simulating adaptation in ANNs through the exponential decay of model features (Vinken et al., 2020). Using "brain-mapped" ANN architectures—feedforward convolutional networks (AlexNet, VGG), networks with skip connections (ResNet), and transformer-based models (ViT)—pre-trained on ImageNet, we analyzed responses from their most "IT cortex-like" layers to naturalistic test images. While these ANNs robustly decoded object positions under static conditions, none exhibited motion-adaptation-induced positional shifts. In contrast, when we presented motion-adapting rightward or leftward moving grating stimuli (3000 ms) to two passively fixating macaques and recorded large-scale IT responses to succeeding test images (40 images containing 1 of 8 objects, with varying latent parameters embedded in naturalistic backgrounds), the resulting position decodes from IT population activity showed directionally specific biases matching human perceptual aftereffects. These results suggest that the neural mechanisms in IT supporting adaptive shifts in perceived object position are not fully captured by current ANN models. Therefore, we hypothesized that additional history-dependent, nonlinear transformations might explain these dynamic adaptation effects. Testing a state-of-the-art dynamic video recognition model (SlowFast with ResNet-50 backbone) showed that even this more temporally sophisticated model failed to reproduce adaptation-induced positional aftereffects. Our results reveal a key gap: while current ANNs can decode position, they lack the temporal processing needed to replicate the adaptive positional biases in the macaque ventral stream.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".