I think it provides evidence because it implies that a lot of the much ballyhooed human sample-efficiency is not in the learning algorithm, but the priors. If you provide informative priors, then ordinary known learning algorithms are capable of matching human performance; which is then mutually reinforcing with the stylized fact that when we create a problem which disables humans’ informative priors but keep the problem’s algorithmic difficulty fixed, their performance suddenly stops being so impressive (implying that the human learning algorithm is similar to ordinary known learning algorithms).
I think it provides evidence because it implies that a lot of the much ballyhooed human sample-efficiency is not in the learning algorithm, but the priors. If you provide informative priors, then ordinary known learning algorithms are capable of matching human performance; which is then mutually reinforcing with the stylized fact that when we create a problem which disables humans’ informative priors but keep the problem’s algorithmic difficulty fixed, their performance suddenly stops being so impressive (implying that the human learning algorithm is similar to ordinary known learning algorithms).