On choosing the next research project

How do you decide your next research project?

It safest to look back at previous work, conduct a logical extension of a published study or permutate common parameters. Looking backward leads to step-by-step movement. There is a small chance of complete failure but an equally small chance at innovation.

It more risky to look ahead and choose a research project based its potential to make a contribution. Looking forward leads to moving in leaps and bounds. This is an increased chance of failure, but also the opportunity for massive inovation.

Sample "blended" lecture slides from my course

I am taking a blended (or hybrid) approach by combing on-line resources with in-person lectures for my "Perception" course at The University of Maryland's Department of Psychology.

Syllabus

Sample Lecture Slides

The connectome & the connection economy

The connectome zeitgeist is rapid moving through the neuroscience community. Like the uncertainty principle or Schrödinger's cat, this scientific idea is a reflection of the larger culture.

The connections neurons (or people) make are often more important than their individual functions.

First day of class

I was honored with an invitation to teach "Perception" at the The University of Maryland for Spring Semester 2013. Day was the first day of class.

One aspect of teaching that I love is the creation of a mirco-world. It is similar in computer programming. Within a general framework, there is ample freedom to create something special. That freedom, both in programming and teaching, is often under-realized. I plan to use more of that freedom within my current class.

If you are interested in the map for the territory I have created, you can find the syllabus here.

Bayesians are still missing a big win

Nate Silver's The Signal and The Noise argues for a Bayesian approach (i.e., thinking probabilistically, making explicit predictions, and gathering "Big Data") to make better predictions. It is limited by the fundamental flaw in the Bayesian paradigm - casual mechanisms are given no weight.

Understanding specific causal mechanisms is one of the biggest "wins" for predication. It is not always possible, but the Bayesian paradigm does not value even trying.

Global vs. local science

The world's collective knowledge is instantaneously available. It is just sitting there waiting for me.

However, the world's collective knowledge is wide but shallow compared to the narrow but deep impact that specific mentors had my intellectual development.

I choose every day which type of impact I make.

Inspired by this post.

Answering the "what" and the "how" in science

Empirical investigation of surface phenomenon is a common departure point for science. The "What is going?" question is best answered at the bench or in the field.

On the often-neglected flipside is the search for the mechanism underlying that surface phenomenon. The "Why does that happen?" question is best answered at the whiteboard, fueled by coffee, or on the back of an envelope, fueled by other adult beverages.

Both are the work of science. The former is the long work. The later is the hard work.