Lastly, we found that previously established alignment methods do not consistently yield better results compared to non-aligned baselines.

Several other factors were important for alignment, such as model size, how separated class representations were, and intrinsic dimensionality.

We found that this cannot be fully attributed to pretraining data size in additional analyses.

CLIP-style models predicted human choices the best across the tasks, suggesting multimodal pretraining is important for representational alignment.

We tested humans on reward and category learning tasks using naturalistic images, where the underlying functions were generated using the THINGS embedding.

Alignment is more than comparing similarity judgments! How well do pretrained neural networks align with humans in few-shot learning settings? Come check our poster #3904 at #NeurIPS on Wednesday to find out