Meta-learning as a principle for human-like visual representations

The structure of human visual representations underpins our capacity for adaptive behaviour.

While pretrained neural networks model human visual representations with unprecedented success, a large discrepancy remains.

We propose one reason: these networks optimise a single fixed objective, whereas human representations must support open-ended tasks.

We hypothesise this flexibility arises from meta-learning (learning to learn), a pressure shaping representations to acquire new tasks from few observations.

To test this, we train a sequence model, without any supervision from human data, across thousands of semantically rich tasks mapping images to high-level concepts.

Compared to their pretrained base encoders, meta-learned representations better predict human similarity judgements, semantic rule learning, and high-level visual cortex.

Behavioural gains depend on disentangled, high-level task distributions, while brain alignment is driven primarily by the learning-to-learn pressure.

Our results suggest the flexibility of human visual representations reflects the functional demand to learn new semantic relationships on the fly.