Neuroimaging project management insight

Errors scale faster by size. Small projects are more often on-time and if late, they are late by smaller amounts compared to larger projects. Think about trip length, compare going to the corner store to going to another continent.

Neuroimaging projects are inherently larger than cognitive psychology projects. Neuroimaging requires more technology, more data, more statistics, and more team members than cognitive psychology. That increased size increases the chances of a project being delayed, possibly in non-linear ways.

It behooves someone to be a varsity-level project manager in cognitive psychology before moving into neuroimaging. "Jumping levels" is a good way to stop moving.

Please do not report the average of a bimodal distribution

The authors of Power failure: why small sample size undermines the reliability of neuroscience state, “Our results indicate that the average statistical power of studies in the field of neuroscience is probably no more than between ~8% and ~31% …”

That is an unacceptable statement[1] given the bimodal distribution of their data. Average (i.e., mean) is a measure of central tendency. Bimodal distributions do not have a central tendency (by definition). That statement reduces the richness of their data to the point of distortion.

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1. It is irrelevant they report a range instead of a point estimate.  ↩
   

Defining a postdoc's job

A postdoctoral fellow's job is research. Research has many shades. If the goal is to publish, the projects should be entirely within the current knowledge base and skill set. If the goal is to develop research skills, the projects should just out of reach with ample guidance. If the goal is to mentor others on their research path, the projects should be well within the mentor's domain.

There are distinct and optimal work environments for each of those jobs. If you have all the knowledge and skills, the work is best done alone and uninterruptible (i.e., the novelist in a secluded cabin). Almost all interruptions will slow down the process. This is closed (and sound-proof) door work. If the research requires growth and collaboration, then access is needed. Digital access is fine but the exchanging of air molecules is far better. This is door opening "out" work. On the other hand, mentorship requires a door opening "in."

The typical office or research lab is only ideal for the last situation. The typical research environment does not have closed doors or the presence of the principal investigator. It does have frequent interruptions, often about "cake in the break room" or one person asking another person to be a human Stack Overflow. Those interruptions slow down the first two types of research. However if the goal is mentorship then those are not interruptions, they are teachable moments to make a human connection.

2013 Cognitive Neuroscience Society Annual Meeting Highlights

William Newsome Keynote Address

William Newsome does ground-breaking single-unit recordings of awake, behaving primates, the grist for my computational modeling mill. He shared new recoding data and his modeling of the data. The subject matter is engaging, gating of sensory information for decision-making, but the modeling is a game changer. It is the work of a post David Sussillo. The modeling is a premier example of dynamical neuroscience. First, he identified the relevant components to the signal. Second, he tracked how the relevant contribution of each of the components change over time. That might appear to be the next logical step but it is an additional dimension to the data, creating a wealth of information to explore. It was disappointing there was no question and answer period after the talk. A well-moderated Q&A would have added to both the science and the entertainment of already exceptional talk.

Analyzing Patterns Of Brain Activity To Understand Human Vision And Cognition Mini-Symposium

Frank Tong and John Serences were the warm-up for the main show of Jack Gallant. He researches the encoding and decoding of movies during fMRI. His work is much more than a “mind reading” pallor trick. He is able to identify the distributed semantic meaning network in the brain for percepts. In addition to what he does, I am also inspired by how he does it. He leverages cutting-edge web technology (i.e., HTML5) to facilitate the open exploration of his data. You can confirm (or disconfirm) his conclusions and explore your hypotheses All of this and more can be found on his lab website.

Posters

There were many interesting posters, here are my favorite two posters:

Lately I have been learning Python to tackle “big data” challenges. It was nice to see an application of this idea in Building Connectomes From The Cocomac Database Using Cocotool. The researchers data mined a macaque anatomical database with CoCoTools for Python. I have been playing with the code, it is simple, clean, and fast. I look forward to applying similar methods to human anatomical data.

Sebastein Hélie, a postdoctoral fellow while I was a graduate student in the Laboratory for Computational Cognitive Neuroscience, presented A Computational Cognitive Neuroscience Model Of Criterion Learning In Rule-guided Behavior. It included a new model for hebbian learning at inhibitory/GABA synapses which captures the properties of criterion selection during rule learning. The next challenge will be to translate the model to dopamine dependent learning.

General

This conference (and San Francisco) were a welcome working vacation. I have been doing mostly the “head’s down” work of research and teaching. It was nice to switch to the “head’s up” work of watching lectures, interacting with poster presenters, and having spirited conversations in the halls. I am reenergized to do the challenging work of cutting-edge science.

It was also a pleasure to catch up with old friends and colleagues and start new friendships and collaborations.

The current and future me

The current me always predicts the future me will have the same thought process as the current me. I will be in the same state of mind (with the same amazing insights).

However, my thought process is constantly changing. That churning is a source of endless creativity but also makes it hard to find my car keys if I don't put them in the same place every time.

The habit of writing things down communicates between these temporally-separated selfs. I frequently write short notes, letters mailed to my future self. This blog is an example of those letters.

Hanging out San Francisco for CNS Conference

I am in San Francisco for Cognitive Neuroscience Society Annual Conference from now until April 16.

If anyone wants to grab coffee, send me an email bspiering@gmail.com.

Repost: 4 pillars of college success in science

I am also a geeky kid-at-heart who wants to make a difference in the world. My mission is making cognitive neuroscience assessable to everyone.

When I teach, I have high expections and expect hard work, and I want everyone to succeed.

This is personally relevant since I also teach at the college level in Maryland. I personally know the issues he faces. It requires taking a diverse collection of students from where they are currently to where they want to be.

Minimizing static knowledge thinking with a simple heuristic

Glossing over controversies and unresolved issues is a necessary evil in undergraduate courses. There is often not time to provide the context to appreciate subject area nuances.

As a result, sometimes undergraduates perceive a subject area as more static than it truly is. They falsely believe al the interesting and assessable questions have all been answered.

I try to avoid that cognitive trap by applying the "Past, Present, and Future" heuristic. For any given issue, I briefly cover past perspectives. I spend a majority of the time on the present, bookended with a discussion of possible future directions. This encourages the students to realize that knowledge is not static and they have the potential to contribute.

Introducing Google Nose

Vision dominates the human experience but that is no reason to exclude the other senses. Apple with iOS has realized the value of touch. Until now the chemical senses have been underserved in the digital marketplace. I plan to use Google Nose in my current Perception class to help explain the complexity of olfactory sensation and perception to my "digital native" students.

It is amazing to witness the cutting edge of sensation and perception.
(Some might say over the edge).

Serving science through null results

There are two reasons for null results. First, there is a true effect that was not observed. Second, there is  no true effect.

If you believe the latter is true, science is best served by publishing the nulls results. Given that "publish" is now a button on everyone's computer, it easier than it has ever been to put a true null result out in the world. Holding back is robbing the world.