I think that if you hypothesize that learning may dominate (aspects of) what the system acquires, then they can be useful as models of that portion of the process — bearing in mind that like any model (organism), they are wrong. They offer a way of testing hypotheses about 1/3

A bias for simplicity by itself does not guarantee good generalization (see the No Free Lunch Theorems). So an inductive bias is only good to the extent that it reflects structure in the data. Is the world simple? The success of deep nets (with their intrinsic Occam's razor) would suggest yes(?)

Hi thanks for the comment! I'm not too familiar with the robot-learning literature but would love to learn more about it!

Thank you Andrew!! :)

On a personal note, this is my first full-length first-author paper! @ekdeepl.bsky.social and I both worked so hard on this, and I am so excited about our results and the perspective we bring! Follow for more science of deep learning and human learning!

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Thank you to amazing collaborators!
@ekdeepl.bsky.social @corefpark.bsky.social @gautamreddy.bsky.social @hidenori8tanaka.bsky.social @noahdgoodman.bsky.social
See the paper for full results and discussion! And watch for updates! We are working on explaining and unifying more ICL phenomena! 15/

💡Key takeaways:
3) A top-down, normative perspective offers a powerful, predictive approach for understanding neural networks, complementing bottom-up mechanistic work.

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