December 26, 2012

Happy Holidays (Sci-Fi)!

This is being cross-posted to Tumbld Thoughts.

Happy Holidays! Brought to you by science fiction [1].


[1] References courtesy Charlie X (Star Trek: TOS) and The X-files.

December 23, 2012

Media Neuroscience Lab Profile

Here is a new research lab [1] recently founded by my colleague Rene Weber [2] at UC-Santa Barbara. The Media Neuroscience Lab does research at the intersection of Virtual Worlds and Neuroscience. Their emphasis is on using physiological data [3] to understand the substrates of communication and cognition during use of entertainment media and other virtual worlds. Check it out.

Figure 1. This figure features an example from the lab's most recent paper [4] featured in a summary post on the Neuroskeptic blog [5]. Images courtesy [4] (middle, bottom) and [5] (top).


[1] the Media Neuroscience Lab is affiliated with the SAGE Center for the Study of the Mind and the Institute for Collaborative Biotechnologies (both at UCSB). The lab also maintains a Twitter feed.

[2] a pioneer in the area of applying innovative neuroimaging techniques to the study of video gameplay and television watching. He and I have worked together on the role of dynamic attentional processing during immersion in virtual environments (so far unpublished).

Picture of Dr. Weber in front of his primary scientific instrument.

[3] the lab also maintains an archive of neuroimaging (fMRI) and psychophysiological (Biopac) datasets if you are interested.

[4] Klasen, M., Weber, R., Kircher, T.T., Mathiak, K.A., and Mathiak, K. (2012). Neural contributions to flow experience during video game playing. Social, Cognitive, and Affective Neuroscience, 7(4), 485-495.

The free-viewing condition (fMRI experimental design) was used to understand what the brain is doing while the subjects played "Tactical Ops" (Figure 1, middle). The results were interpreted in the context of Csíkszentmihályi's flow theory of experiential cognitive (in this case, neural) processing.

[5] Post from Neuroskeptic blog on their recent paper "Neural contributions to flow experience during video game playing": How Your Brain Gets In The Game. Neuroskeptic, May 23, 2011.

December 13, 2012

Perhaps it's not too late.....or too soon

Just found out (via IEEE Spectrum) about an interesting contest sponsored by Intel and the HYVE [1]. It's called the Intel Future Contest. The point is to come up with an idea for a an idealized application for a smart sensor suite. What do you do with it? Use it in the home? Use it to monitor your health? Or use it in your favorite hobby [2]? See the description below:

"Imagine five years into the future. You have on you (or with you) this new sensing technology. It can see, hear, remember and understand everything around you all the time".

They also provide a number of criteria to keep in mind during the design process. These are included in the table below:

The deadline is December 18. To submit, they require a series of sketches or something similar. Judges will (hopefully) be fair and impartial [3]. Categories include: healthcare, communication, work, knowledge production, entertainment, infrastructure and environmental modeling, creative expression, and an "other" category. If you have an idea (that you are not submitting or have already submitted), perhaps you could also submit it in the comments section below [4]. 


[1] a self-proclaimed "open-source innovation company".

[2] skydiving reference and pic (below) courtesy Ars Technica and Google I/O 2012 keynote address.

[3] one of the judges is Mark Roth, who I recognize as the conductor of experiments on SO2-induced hibernation in mice (metabolic flexability).

[4] to see the pool of submissions (which features some impressive ideas), go to this site. My submission, "Sensor-enabled Relativistic Virtual Worlds", can be viewed here (or Figshare version here).

December 12, 2012

"Disrupting" the Scientific Establishment?

This series of videos [1] is for my academic scientists friends frustrated with the traditional funding streams [2]. Part of my continuing series on alternative funding models. The first is a cartoon-laden introduction to crowdsourcing (as a concept) called: "Crowdsourcing: what is it?".

The second video speaks more directly to the disruptive potential of crowdsourcing [4] on traditional academic research power structures [3]: "Disrupting conventional funding models".

The third featured video profiles the SciFund challenge [5], and provides insights into how crowdsourcing is effective as a practical tool: "Crowdfunding for Academic Research".

Finally, there is an example from the UK called Open Science Space (an IndieGoGo project) run by Peter Cubbin. In the video, he explains one proposal for how "open-source" funding fits into the scientific enterprise.

So the questions are raised: is this a viable alternate path forward? Do such models have the potential to "disrupt" how academic science is done and rewarded [6]? Please feel free to discuss and share.


[1] All contributions courtesy various YouTube contributors.

[2] NIH, NSF, DARPA, etc. While the predominant way to fund science, these models do not work well for every type or research or project.

[3] And markets for research and researchers (scientists). But more on that in another post.

[4] Actually, the disruptive innovation here is the enabling technology (e.g. the internet).

[5] Currently in Round 3 (the initial round began a few years back). I direct you to the call to arms, put out by Jai Ranganathan and Jarrett Byrnes.

[6] This is somewhat of an aside, but there has been much talk about academic elitism (e.g. money and attitudes) and its perpetuation recently in the blogosphere and news media. See the following two articles for a taste of this zeitgeist:

"Is Michigan State really better than Yale?" (New York Times)

"Ph.D.'s From Top Political-Science Programs Dominate Hiring, Research Finds" (Chronicle of Higher Education).

December 8, 2012

Algorithmic Self-assembly (with DNA) Profile

Another popular post that is being re-posted from my microblog, Tumbld Thoughts (on Algorithmic Self-assembly).

Here is David Doty (Math/CS) from Caltech discussing the theory of Algorithmic Self-Assembly [1] featured in a Communications of the ACM article — picture on the left, and Vimeo video slideshow — picture on the right). Here is a related blog post from 80 Beats (Discover magazine science blog) on DNA LEGO bricks. Enjoy both.

Associated trivia: the “Abstract Tile Assembly Model” [2] featured in the Vimeo video was developed by Erik Winfree (another DNA Computing person), who is the son of Arthur Winfree. Art Winfree wrote an excellent book called the “Geometry of Biological Time”, and was a mentor of Steven Strogatz [3].


[1] process by which “things autonomously (no directedness) assemble themselves into larger functional units”.

[2] for a demo, try out the the Xgrow simulator.

[3] a nice story about this is featured in the book “Sync

December 5, 2012

Triangulating Scientific “Truths”: an ignorant perspective

I have recently read the book "Ignorance: how it drives science" by Stuart Firestein, a Neuroscientist at NYU. The title "Ignorance" refers to the ubiquity of how what we don't know influences the scientific facts we teach, reference, and hold up as popular examples. In fact, he teaches a course at New York University (NYU) on how "Ignorance" can guide research, with guest lecturers from a variety of fields [1]. This is of particular interest to me, as I hosted a workshop last summer (at Artificial Life 13) called "Hard-to-Define Events" (HTDE). From what I have seen in the past year or so [2], people seem to be converging on this idea.

In my opinion, two trends are converging that seem to be generating interest in this topic. One is the rise of big data and the internet, which make the communication of results and rendering of the "research landscape" easier. Literature mining tools [3] are enabling discovery in and of itself, but also revealing the shortcomings of previously-published studies. There has also been a good deal of controversy raised over the last 10 years in terms of a replication crisis [4] coupled with the realization that the scientific method is not as rigorous [5] as previously thought.

The work of Jeff Hawkins, head of Numenta, Inc. [6], addresses many of these issues from the perspective of formulating a theoretical synthesis. For a number of years, he has been interested in a unified theory of the human brain. While there are challenges in terms of both testing such a theory AND getting the field to fit it into their conceptual schema, Jeff has nevertheless found success building technological artifacts based on these ideas.

Jeff's work illustrates the balance between integrating what we do know about a scientific field with what we don't know. This involves using novel neural network models to generate "intelligent" and predictive behavior. Computational abstraction is a useful tool in this regard, but in the case of empirical science the challenge is to include what we do know and exclude what we don't from our models.

According to this viewpoint, the success of scientific prediction (e.g. the extent to which a theory is useful) is dependent upon whether findings and deductions are convergent or divergent. By convergent and divergent, I mean how independent findings can be used to triangulate a single set of principles or predict similar outcomes. Examples of convergent findings include Darwin’s finch beak diversity to understand natural selection [7] and the use of behavioral and neuroimaging assays [8] to understand attention. 

There are two ways in which the author proposes that ignorance operates in the domain of science. For one, ignorance defines our knowledge. The more we discover, the more we discover we don't know. This is often the case with problems and in fields for which little a priori knowledge exists. The field of neuroscience has certainly has its share of landmark discoveries that ultimately raise more questions than provide answers in terms of function or mechanism. Many sciences often go through a "stamp-collecting" or "natural history" phase, during which characterization is the primary goal [9]. Only later does hypothesis-driven, predictive science even seem appropriate.

The second role of ignorance is a caveat, based on the first part of the word: "to ignore". In this sense, scientific models can be viewed as tools of conformity. There is a tendency to ignore what does not fit the model, treating these data as outliers or noise. You can think about this as a challenge to the traditional use of curve-fitting and normalization models [10], both of which are biased towards treating normalcy as a statistical signal signature.

If we think about this algorithmically [11], it requires a constantly growing problem space, but in a manner typically associated with reflexivity. What would an algorithm defining "what we don't know" and “reflexive” science look like? Perhaps this can be better understood using a metaphor of the Starship Enterprise embedded in a spacetime topology. Sometimes, the Enterprise must venture into uncharted regions of space (but one that still corresponds to spacetime). While the newly-discovered features are embedded in the existing metric, these features are unknown a priori [12]. Now consider features that exist beyond the spacetime framework (beyond the edge of the known universe) [13]. How does a faux spacetime get extrapolated to features found here? The word extrapolation is key, since the features will not necessarily be classified in a fundamentally new way (e.g. prior experience will dictate what the extended space will look like).

With this in mind, there are several points that occurred to me as I was reading “Ignorance” that might serve as heuristics for doing exploratory and early-stage science:

1) Instead of focusing on convexity (optimal points), examine the trajectory:

* problem spaces which are less well-known have a higher degree of nonconvexity, and have a moving global optimum.

* this allows us to derive trends in problem space instead of merely finding isolated solutions, especially for an ill-defined problem. It also prevents an answer marooned in solution space.

2) Instead of getting the correct answer, focus on defining the correct questions:

* according to Stuart Firestein, David Hilbert's (Mathematician) approach was to predict best questions rather than best answers (e.g. what a futurist would do).

* a new book by Michael Brooks [14] focuses on outstanding mysteries across a number of scientific fields, from dark matter to abiogenesis.

3) People tend to ask question where we have the most complete information (e.g. look where the light shines brightest, not where the answer actually is):

* this leads us to make the comparison between prediction (function) and phenomenology (structure). Which is better? Are the relative benefits for each mode of investigation problem-dependent?

Stepping back from the solution space-as-Starfleet mission metaphor, we tend to study what we can measure well, and in turn what we can characterize well. But what is the relationship between characterization, measurement, and a solution (or ground-breaking scientific finding)? There are two components in the progression from characterization to solution. The first is to characterize enough of the problem space so as to create a measure, and then use that measure for further characterization, ultimately arriving at a solution. The second is to characterize enough of the problem space so that coherent questions can be asked, which allows a solution to be derived. When combined, this may provide the best tradeoff between precision and profundity. 

Yet which should come first, the measurement or the question? This largely depends on the nature of the measurement. In some cases, measures are more universal than single questions or solutions (e.g. information entropy, fMRI, optical spectroscopy). Metric spaces are also subject to this universality. If a measure can lead to any possible solution, then it is much more independent of question. In “Ignorance”, von Neumann's universal constructor, as applied to NKS theory by Wolfram [15] are discussed as a potentially universal measurement scheme. 

There are two additional points I found intriguing. The first is a fundamental difference between scientific fields where there is a high degree of "ignorance" (e.g. neuroscience) versus those where there is a relatively low degree (e.g. particle physics). This is not a new observation, but has implications for applied science. For example, the interferometer is a tool used in the physical sciences to build inferences between and find information among signals in a system. Would it be possible to build an interferometer based on neural data? Yes and no. While there is an emerging technology called brain-machine interfaces (BMI), these interfaces are limited to well-characterized signals and favorable electrophysiological conditions [16]. Indeed, as we uncover increasingly more about brain function, perhaps brain-machine interface technology will become closer to being like an interferometer. Or perhaps not, which would reveal a lot about how intractable ignorance (e.g. abundance of unknowable features) might be in this field. 

The second point involves the nature of innovation, or rather, innovations which lead to useful inventions. It is generally thought that engaging in applied science is the shortest route to success in this area. After all, pure research (e.g. asking questions about the truly unknown) invoves blind trial-and-error and ad-hoc experiments which lead to hard-to-interpret results. Yet "Ignorance" author Firestein argues that pure research might be more useful in terms of generating future innovation than we might recognize. This is becuase while there are many blind alleyways in the land of pure research, there are also many opportunities for serendipity (e.g. luck). It is the experiments that benefit from luck which potentially drive innovation along the furthest.


[1] One example is Brian Greene, a popularizer of string theory and theoretical physics and faculty member at NYU.

[2] via researching the literature and internal conversations among colleagues.

[3] for an example, please see: Frijters, R., van Vugt, M., Smeets, R., van Schaik, R., de Vlieg, J., and Alkema, W. (2010). Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases. PLoS Computational Biology, 6(9), e1000943.

[4] Yong, E. (2012). Bad Copy. Nature, 485, 298.

[5] Ioannidis, J.P. (2005). Why Most Published Research Findings Are False. PLoS Medicine, 2(8), e124 (2005).

[6] Also the author of the following book: Hawkins, J. and Blakeslee, S. (2004). On Intelligence. Basic Books.

[7] here is a list of examples for adaptation and natural selection (COURTESY: PBS).

[8] for an example of how this has affected the field of Psychology, please see: Sternberg, R.J. and Grigorenko, E.L. (2001). Unified Psychology. American Psychologist, 56(12), 1069-1079.

[9] this was true of biology in the 19th century, and neuroimaging in the late 20th century. There are likely other examples I have not included here.

[10] here are more information on curve fitting (demo) and normalization (Wiki).

[11] this was discussed in the HTDE Workshop (2012). What is the computational complexity of a scientific problem? Can this be solved via parallel computing, high-throughput simulation, or other strategies?

Here are some additional insights from the philosophy of science (A) and the emerging literature on solving well-defined problems through optimizing experimental design (B):

A] Casti, J.L. (1989). Paradigms Lost: images of man in the mirror of science. William Morrow.

B] Feala, J.D., Cortes, J., Duxbury, P.M., Piermarocchi, C., McCulloch, A.D., and Paternostro, G. (2010). Systems approaches and algorithms for discovery of combinatorial therapies. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2, 127.  AND  Lee, C.J. and Harper, M. (2012). Basic Experiment Planning via Information Metrics: the RoboMendel Problem. arXiv, 1210.4808 [cs.IT].

[12] our spacetime topology corresponds to a metric space, a common context, or conceptual framework. In an operational sense, this could be the dynamic range of a measurement device or the logical structure of a theory.

[13] I have no idea how this would square away with current theory. For now, let’s just consider the possibility.....

[14] the citation is: Brooks, M. (2009). 13 Things That Don't Make Sense. Doubleday.

[15] NKS (New Kind of Science) demos are featured at Wolfram's NKS website.

[16] this is a theme throughout the BMI and BCI literature, and includes variables such as type of signal used, patient population, and what is being controlled.

December 1, 2012

CoE #46 is now on Figshare

After reading a blog post (Posting Blog Entries to Figshare) by C. Titus Brown (from Living in an Ivory Basement) on strategies for furthering open-source science, I decided to post the Carnival of Evolution blog carnival I hosted back in April (The Tree (Structures) of Life) to Figshare [1].

Figshare is a public repository that provides a digital identifier (DOI) to each document shared. DOI identifiers make it easy for non-standard content (blog posts, slideshows, etc) to be formally cited and cataloged. In his post, C. Titus Brown suggests a programming solution to convert blog posts to Figshare documents. I did not use his method for this, but I simply formatted it as a more formal document. Enjoy.


[1] CoE #46: The Tree (Structures) of Life.