meta-learning-human-visual-representations

name	meta-learning-human-visual-representations
description	Meta-learning methodology for achieving human-like visual representations. Proposes that meta-learning (learning to learn) pressure shapes neural representations to support open-ended tasks. Compared to pretrained models, meta-learned representations better predict human similarity judgments, semantic rule learning, and high-level visual cortex activity. Activation: meta-learning, visual representations, brain alignment, human similarity, semantic learning, few-shot learning, visual cortex
metadata	{"arxiv_id":"2606.28399","published":"2026-06-24","authors":"Can Demircan, Marcel Binz, Alireza Modirshanechi, Eric Schulz","tags":["meta-learning","visual-representations","brain-alignment","human-similarity","few-shot-learning"]}

Meta-learning as a Principle for Human-like Visual Representations

Core Thesis

Pretrained neural networks model human visual representations well but still show gaps. This paper proposes that the gap exists because these networks optimize a single fixed objective, whereas human representations must support open-ended tasks.

Key Hypothesis: Meta-learning (learning to learn) shapes representations to be flexible for rapid task acquisition from few observations.

Methodology

1. Training Approach: Train a sequence model across thousands of semantically rich tasks mapping images to high-level concepts
2. No Human Supervision: Model trained without any supervision from human data
3. Comparison: Compare meta-learned representations vs pretrained base encoders

Key Findings

Behavioral Level

• Meta-learned representations better predict human similarity judgments
• Better at semantic rule learning
• Gains depend on disentangled, high-level task distributions

Neural Level

• Better alignment with high-level visual cortex
• Brain alignment driven primarily by the learning-to-learn pressure

Core Insights

1. Flexibility through Meta-Learning: Human visual representation flexibility reflects the functional demand to learn new semantic relationships on the fly

2. Dissociable Mechanisms: Behavioral gains and brain alignment have different drivers:

   • Behavioral: task distribution structure (disentangled, high-level)
   • Neural: meta-learning pressure itself

3. Beyond Pretraining: Fixed-objective pretraining is insufficient; representations must be shaped by the pressure to learn new tasks rapidly

Methodological Implications

For Brain-Model Alignment

• Meta-learning provides a more biologically plausible training paradigm
• Captures the open-ended nature of human visual processing
• Better predicts neural responses in high-level visual areas

For AI Systems

• Few-shot learning capabilities emerge from meta-learning pressure
• Representations become more generalizable and flexible
• Semantic relationships can be acquired rapidly

Experimental Design

• Tasks: Thousands of image-to-concept mappings
• Evaluation: Human similarity judgments, semantic rule learning, fMRI visual cortex alignment
• Comparison: Meta-learned vs pretrained base encoders

Limitations & Future Directions

• Sequence model architecture choices not fully explored
• Could be extended to other modalities (auditory, multimodal)
• Integration with continual learning frameworks promising

Related Concepts

• Few-shot learning
• Brain-model alignment
• Visual cortex representations
• Meta-learning / learning to learn
• Semantic representations
• Open-ended learning

References

• arXiv:2606.28399 [cs.CV]
• Steinmetz et al. 2019 (neural data)
• Bolding & Franks 2018 (olfactory data, mentioned in related work)