[habitue]

This doesn't seem to be correct. The article talks about reinforcement learning agents optimizing a communication game to trade off color description accurately vs. effort.

The words they use aren't real words, they're partitions of the color space, and the researchers found that the partitions the agents came up with to win the game were similar to human partitioning of the color space.

Now, did the design of the game and the reward function smuggle in human notions of reasonableness that made the outcome a foregone conclusion? Maybe that's a more reasonable criticism, I don't know.

[readflaggedcomm]

The study relies on "colors" defined by human perception, which could be interpreted as a form of training, when all inputs are restricted to that definition.

The efficiency/complexity insight doesn't rely on that data, but the human-like output produced by human-like color data combined with communications limitations does rely on it, and that's what the article is all about.