We compared various Bayesian learning models with each population. The main takeaway: children as young as 4-years-old showed adult-like program induction on our task. Monkeys and 3-year-olds mostly used local extrapolation.

In contrast, despite extensive training on algorithmic patterns, the monkeys relied on a simpler local linear extrapolation to make predictions. Interestingly, 3-year-olds mostly used this same strategy—and their accuracy across patterns correlated with monkeys much more than with adults.

We found striking differences across both development and species in our task. Below are some examples of predicted continuations on various sequences in each population.

If participants are learning structured programs in an expressive “Language of Thought”, they should (1) be able to learn the sequences we tested by the final timepoint; but (2) show patterns of multimodal uncertainty reflective of possible algorithms that are consistent with the sequence so far.

One possibility is that we can “learn by programming” rapidly inferring structured algorithms to model our observations.

Our paper tests this ability in adults, 3-7yo children, and two rhesus macaques. Participants predicted how 2D sequences would unfold starting from the first few timepoints.

"this rendition emphasizes her eyes, carefully positioned to create the illusion of a gaze that follows you"

and ascii mona lisa

I am proud to report that humans still have the upper hand on palindromes