Well, our priors aren't easily expressible in terms of simple probability distributions. They could probably be modeled fairly well by gaussian process priors though.
The video claims the explanation is different. They experiments around things where humans have no priors and the humans still form beliefs more quickly than is justified by a Bayesian model - and they don't necessarily formulate correct beliefs.
The argument of the video is humans form beliefs to facilitate information exploration. In this context, any belief can be better than none.
The impression from the discussion I get is that human beliefs and behaviors tend to differentiate - people often have slightly different ideas about everything, "what is a bowl" was one example. People pick-up beliefs easily and change beliefs as they go along - as long as they have feedback.
This apparently works for groups of hunter-gathers and even for people driving cars but less well for people using the Internet to decide whether to vaccinate their children.
In a Bayesian model there's no such thing as having no priors, that's the problem with arguing against Bayesian human reasoning with a model that can't capture the richness of human priors (which means modelling all relevant knowledge and intuition, including innate human instincts). And human priors include very strongly-held ones like "the world is basically comprehensible, governed by rules that we can discover and understand." We cannot prove that, but to the extent that we are wrong about it, all cogitation is useless, so we assume it.
Our beliefs about "what is a bowl" include that it is an instrumental concept created by other agents similar to us in order to facilitate communication. This justifies very strong priors that it will be a simple concept and easy to generalize from small numbers of examples, at least for us. All this just by virtue of being a common word. So I don't see any way to argue that human behaviour is non-Bayesian here unless one ignores relevant prior information or ignores the decision theory question "what is the consequence of being wrong about what a bowl is".
As humans we have much sharper priors over hypothesis space than what we can easily model, which probably explains this discrepancy.