Feature selection methods not so useful for graphical model structure learning

As discussed in one of my previous posts (here), feature selection and graphical model structure learning are highly related. On one hand, variables in the Markov blanket of a target variable can be used as a compact and effective feature set for predicting the value of the target variable; on the other hand, traditional feature selection methods can used to shed light on the local structures around a target variable (see another post of mine). However, the paper "Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification" (Part I, Part II) provides extensive experimental results that support the first practice but question the second practice. The authors found that traditional feature selection methods tend to return variables that are highly predictive but are often not in the Markov blanket of the target variable and therefore not useful in structure learning.

An ET point of view of neuroscience

How would an extraterrestrial view the state of the art of our neuroscience research? The paper "Neuroscience and the correct level of explanation for understanding mind: An extraterrestrial roams through some neuroscience laboratories and concludes earthlings are not grasping how best to understand the mind–brain interface" (a really long title...) in Trends in Cognitive Sciences (2010 Jul; 14(7)) describes a possible point of view from ET (or more realistically, from a high-level point of view of the big picture). Specifically, the author argues that studying the low-level neural mechanisms, like the behavior of individual neurons, may not be the right way to understand mind, just like we do not need to understand quantum mechanics before understanding the Newtonian mechanics. This reminds me of the long-standing symbolism vs. connectionism debate in AI research. What is the right level of research towards an artificial general intelligence? Symbols? neurons? Or somewhere in-between?