coarse-feedback-human-aligned-visual

name

coarse-feedback-human-aligned-visual

description

Shows that extremely coarse feedback signals (distinguishing as few as 8 broad categories) produce neural representations matching or exceeding 1000-class supervised models in brain alignment with primate vision. Trains hundreds of CNNs and ViTs across granularity levels, comparing against macaque electrophysiology and human fMRI. Use when analyzing brain-aligned vision models, role of supervision granularity in neural alignment, or human perceptual similarity learning.

Coarse Feedback for Human-Aligned Visual Representations

Based on: Mehta & Bonner (2026). An extremely coarse feedback signal is sufficient for learning human-aligned visual representations. arXiv:2605.05556 [cs.CV].

Core Finding

Networks trained to distinguish as few as 8 broad categories learn representations that match or exceed the neural alignment of models trained on 1,000-class ImageNet classification.

Most Surprising Result

These coarsely trained networks align more closely with human perceptual similarity judgments than ALL other models evaluated, including:

• Networks trained with fine-grained supervision (1,000 classes)
• Self-supervised models (SimCLR, DINO, etc.)
• Leading large-scale vision models (CLIP, DINOv2)

Methodology

Stimulus Granularity Manipulation

The paper parametrically varies the granularity of the learning signal:

Categories: 2 → 4 → 8 → 16 → 32 → 64 → 1,000

Categories are created via PCA-based splits of pre-trained embeddings, ensuring natural data-driven groupings.

Training

• Architectures: CNNs (ResNet) and Transformers (ViT)
• Tasks: Coarse classification at each granularity level
• Evaluation: Representational Similarity Analysis (RSA) against:
  • Macaque electrophysiology recordings (V1-V4, IT)
  • Human fMRI (THINGS dataset, 720 stimuli, 3 subjects)

Key Results

Granularity	V1/V2 Alignment	IT Alignment	Perceptual Similarity
2 classes	Moderate	Low	Good
8 classes	Best	Moderate	Best overall
64 classes	Good	Good	Good
1,000 classes	Good	Best	Worse than 8-class
Self-supervised	Good	Moderate	Below 8-class

Implications

For Computational Neuroscience

• Reframes what learning signals vision requires: The brain may not need fine-grained category supervision
• Suggests broad categorical feedback is sufficient for developing brain-like representations
• Questions the trend toward increasingly fine-grained supervision in brain-aligned AI

For AI

• Opens a path toward building AI systems that are more aligned with human perception
• Coarse supervision is more efficient (less labeling, simpler tasks)
• Suggests current large-scale vision models may be over-specified for human-like visual learning

For Understanding Biological Vision

• Primate visual systems may learn from relatively coarse behavioral outcomes
• The 8-category sweet spot may correspond to evolutionarily relevant category distinctions
• Supports the idea that broad ecological pressures shaped visual representations

Theoretical Explanation

The paper suggests:

• Fine-grained distinctions may introduce category-specific noise that distorts representations
• Coarse tasks force the network to learn genuinely generic visual features
• Self-supervised objectives optimize for instance discrimination, which may not match human perceptual organization

When to Use

• Brain-aligned model design: Use coarse supervision as a stronger baseline
• Perceptual similarity research: Reference for how human-like similarity emerges
• Computational neuroscience: Evidence for the role of coarse feedback in visual development
• Representational alignment studies: New paradigm for studying supervision granularity

Citation

@article{mehta2026coarse,
  title={An extremely coarse feedback signal is sufficient for learning human-aligned visual representations},
  author={Mehta, Yash and Bonner, Michael F},
  journal={arXiv preprint arXiv:2605.05556},
  year={2026}
}

Activation

• coarse feedback visual representations
• human-aligned vision networks
• supervision granularity neural alignment
• coarse category learning vision
• brain-aligned representation learning
• minimal supervision brain alignment
• perceptual similarity emergence