Author: jlolds

Native Angeleno, professor of Neuroscience and Public Policy at George Mason University

Lame Duck Congress Science Action

From ScienceInsider here. The article correctly raises the point of how relevant the America COMPETES act will be since it doesn’t actually appropriate any monies. In the meantime we read of draconian cuts in the UK science budget–foreshadowing the future here in the US?

I think the real issue in terms of science funding is whether we view it as a national security investment (I do) or whether we view it as a budget-buster. The key argument to make to the incoming Congress is that national security investments into science can help the economy, create jobs and enhance our national security and that the real issue with regards to deficits is entitlement reform.

My take on the Decline Effect

So I’ve read Jonah Lehrer’s New Yorker piece now several times. I take it seriously. The policy implications, particularly with regards to the use of pharmaceuticals, are incredibly disturbing. I’m less concerned with the Rhine’s ESP research in the 1930’s.

I should point out that there are many areas of science ranging from molecular biology to astrophysics where I don’t believe there is any evidence at all for such a “decline effect”.  The disciplines affected by the problem are those that generally depend on to a greater extent on parametric statistics (t-tests and the like) rather than categorical “yes-no” results (e.g. a gene sequence, the timing of an eclipse, a band in a gel).

So what about the causes? First, yes there is experimenter bias. Experimenters are (still) human and hence are imperfect.

But much more interesting to me is the problem of replicability.  As a journal editor myself, I have to make difficult decisions about what to publish and the reality of today’s scientific marketplace is that negative results have a hard time making it past editors and into print. So another real part of the problem is that when many studies are compared for replicability (meta-analysis), this type of research itself is inherently biased by the “dark matter” of unpublished negative results.

Is something else spooky going on here? I don’t think so. Science, I’m pleased to say, has not yet been seriously targeted by deconstructive criticism.

Jonah Lehrer’s piece in the New Yorker

Behind the firewall, here’s the abstract of Jonah’s new piece on the “decline effect”. And here’s Steven Novella’s response on his NeuroLogica Blog.

Basically what’s at stake is our (the community of scientists and those who use scientific results to create informed policies) faith in the Scientific Method (as defined best by Popper).

I’m still working through my own thoughts as to Lehrer’s article. It’s creating a big stir among my colleagues and it deserves a serious response. So stay tuned.

Science of Mind–the philosopher’s response

From today’s NY Times on-line, here. Tyler Burge, professor of philosophy at UCLA makes some excellent points and, as far as I know, coins a new term: “neurobabble”.

I think his strongest point is the analogy with physics–imagine if we had attempted to understand inheritance at the level of quantum mechanics (as opposed to the molecular structure of DNA). His view of modern cognitive neuroscience is that it approaching the phenomenology of human psychological events at a similarly mismatched level.

I prefer to view the modern approaches to understanding these phenomena as integrative neuroscience. The productive approach is to integrate and synthesize neurobiological knowledge across multiple, appropriate levels of the spectrum that extends from molecules to brains. Burge is simply wrong when he implies that modern cognitive neuroscientists are essentially practicing neophrenology. Successful cognitive neuroscience approaches are inherently integrative and combine molecular neuroscience, brain imaging and psychological testing superimposed upon the architecture (connectome) of neuroanatomy.

The Snow

Last winter we had 56 inches of snow at Reagan National Airport. The average is 15. Today we’ve commenced this year’s hopeful regression to the mean–I believe we’ll get around 3 inches in today’s storm. Of course, three inches of snow in DC is very different from the same in Chicago. Word from around town is that the roads are OK, but they are jammed: the general panic to purchase milk and toilet paper is on.

Looking out my office window, the trees in our small forest conjure up the apical dendrites of CA1 pyramidal cells–I can imagine the somata hidden just below the windowsill. The axons crossing those dendrites must also remain in my imagination. My view is more of a Golgi staining perspective. The real packing of neurons would be far denser.

But it’s a compelling scene for a neuroscientist, on a cold winter’s afternoon.

Annual Holiday Party

Today the Institute will take time off from science and education for a bit to celebrate. In rare Washington synchrony, we had our first dusting of snow this morning–just enough to accent the trees, not enough to cause chaos on the roads. It’s been a fine semester, although there are significant budget uncertainties ahead with Recovery Act (ARRA) funding going away next fiscal year. I’m exceptionally proud of our students, our faculty and our fine staff. It’s a pleasure to work with them.

Journal Club

I asked permission to sit in on the Journal Club that’s held regularly by our neuroscience doctoral students today. It was a real treat. They are a bright bunch.

A key point of discussion concerned the sort of operational definitions that are fairly common in behavioral neuroscience (e.g. habit learning, spatial learning). These definition are extremely important in the design of experiments and in the interpretation of results and they evolve over time: model-based and model-free were the two relevant contexts for today’s paper from Redish’s lab at University of Minnesota.

Central to these neuroscience approaches is the holy grail of dissociating the different types of learning that occur between regions of the brain. The problem of course is that many brain regions participate in any one kind of learning. Further, any experimental design, no matter how excellent (in my opinion, the late David Olton was among the very best) is likely to have any single learning experience confounded by multiple types of learning.