Consciousness is the *most recent* module, and that *does* mean that. I'm sorry, I thought this was one point that wasn't even in dispute. It was laid out pretty clearly in the Evolution Sequence:

Complex adaptations take a very long time to evolve. First comes allele A, which is advantageous of itself, and requires a thousand generations to fixate in the gene pool.

Onlythen can another allele B, which depends on A, begin rising to fixation. A fur coat is not a strong advantage unless the environment has a statistically reliable tendency to throw cold weather at you. Well, genes form part of the environment of other genes, and if B depends on A, B will not have a strong advantage unless A is reliably present in the genetic environment

I was struggling to word the doctor parapgraph in a manner which was succinct but still got the idea across. I think query worded it better.

On math curriculum, that advanced classes build off of calculus is a function of current design. They could recenter courses around statistics and have calculus be an extension of it. Some of the calculus course would need to be reincorporated into the stats courses, but a lot of it wouldn't. You're going to have a hard time convincing me that trigonometry and vectors are a necessary precursor for regression analysis or Bayes' theorem. The minority of students in physics and engineering that need both calculus and statistics should not dictate how other majors are taught. Fixing the curriculum isn't an easy problem, but they've had more than a century to solve it and there seems to be little movement in this direction.

*3 points [-]So you're fitting a straight line. Parameter estimates don't require linear algebra (that is, vectors and matrices). Super. But the immediate next step in any worthwhile analysis of data is calculating a confidence set (or credible set, if you're a Bayesian) for the parameter estimates; good luck teaching that if your students don't know basic linear algebra. In fact, all of regression analysis, from the most basic least squares estimator through multilevel/hierarchical regression models up to the most advanced sparse "p >> n" method, is built on top of linear algebra.

(Why do I have such strong opinions on the subject? I'm a Bayesian statistician by trade; this is how I make my living.)