> It is Kepecs' thesis that statistics - generated by the objective processing of sensory and other data - is the ultimate language of the brain. Every time I read stuff like this, I lose more confidence that the scientific method as currently applied, funded, and published is capable of truly conquering complex systems like the brain or nutrition. I read article after article like this one, where a prominent researc…
The way to reconcile those claims is to say that many of our statistical/estimation patterns are very bad. Mostly, they were good once, but are completely unequipped to deal with the modern world. I know "we're optimized for the savannah" is a laugh-line at this point, but it doesn't seem absurd to claim that we're using statistical techniques that are easy but inflexible. Flawless Bayesian updating is basically unachievable, and a lot of our biases seem like once-useful heuristics that screw us a lot in the modern world.
Tetlock's Superforcasting seems like a good reference here: it's a study of people who are uncommonly good at estimating future probabilities. These are people who estimate near-future events to better than half a percent (as in, rounding from .1% to .5% reliably lowers their accuracy). Most of them claim to use a lot of numerical probability estimates, which suggests that they might be employing an existing system consciously to get the bugs out.
None of this means I buy the study, but I'm not willing to reject the hypothesis, either. Given that we're running on neural nets, I wouldn't be surprised if we deal in actually probabilities and are just exceedingly bad at it.