It is a lot like programming. Start off defining the algorithm to handle the general case and refine it until it works with no issues. Then add in edge cases so

It is a lot like programming. Start off defining the algorithm to handle the general case and refine it until it works with no issues. Then add in edge cases so that you tune it to handle more real-world situations. That is the way to build a solid, fail-safe code base. If you truly understand the problem you can eventually code enough to handle any real-world scenario. Don't be surprised if the code looks hairy and nothing like the original though. In their case, they started with a generalized "2D" simple case and concluded after study that a real-world scenario would look very similar or would be a neat permutation of their original grid-optimized discovery. They found instead that they had discovered that the rat brains optimized their navigation to the difficulty of the task and that once you add enough difficulty, the brains spread the computational resources in a different optimization in order to handle the newfound complexity. Patterns were still detectable but they did not follow the original hexagonal grid optimization discovered in the initial tests. This is, as you say, a process of learning one small piece of a bigger puzzle. In this case, they found that the complexity of the puzzle is higher than the initial guess they made after collating and analyzing all the data from the simple experiments. Useful things were learned but I was attempting to note that they have apparently made assumptions based on a very simple case that turned out not to be entirely accurate and that they appear to be surprised by that instead of accepting that the simplicity of the tasks used to generate the data may have biased the results. No doubt they will continue to add complexity and learn new things, and win more awards for their pioneering work.