Here are some readings on networking and open science from my reading queue. The first is a paper on the life-cycle of a preprint on the arXiv. The top image is Figure 2 in the paper. The other two readings advocate for the use of open access protocols and social media to disseminate research and counter cultural biases towards keeping research behind laboratory doors.

Shuai, X., Pepe, A., and Bollen, J.   How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations. PLoS One, 7(11), e47523 (2012).

Allen , E.   “All research should be OA”. We agree! ScienceOpen blog, July 14 (2014).

Konkiel, S.   How to become an academic networking pro on LinkedIn. ImpactStory blog, April 24 (2014).

Four readings from the reading queue on human culture, behavior, and evolution:

Benjamin, D.J. et.al    The genetic architecture of economic and political preferences. PNAS, 10:1073/pnas.1120666109 (2014).

On the genetic architecture of economic and political preferences. Using a SNP analysis, the authors demonstrate that such traits have a polygenic architecture (e.g. many genes, small effect size for each). Studies that are underpowered (and no one knows what the appropriate sample sizes should be) can potentially generate many false positive associations between genes and behavior.

Mooney, C.   Scientists Are Beginning To Figure Out Why Conservatives Are …… Conservative. Mother Jones, July 15 (2014).

A story that equates (or perhaps confounds) the psychophysiology of political ideologies with the roots of more general ideological bias. Are we really looking at “natural” differences between liberals and conservatives? Or does this simply demonstrate that high-profile social issues with already polar liberal and conservative positions are undergirded by strong emotional responses? The standard evolutionary psychology explanation is a bit contrived as well. But it goes well with the previous article.

Ren et.al   Cross-Cultural Color-Odor Associations. PLoS One, 9(7), e101651 (2014).

Crossmodal and cross-cultural comparisons, unite! In this study, people from several different cultures were asked to make both “congruent” and “incongruent” associations between smells and colors. The authors come to the conclusion that cultural context through experience has both statistical (covariance) and semantic (linguistic) components.

Stix, G.   Neuroplasticity: new clues to just how much the adult brain can change. Scientific American blog, July 14 (2014).

A gateway article to several recent studies in the area of neuroplasticity. Learn about the “neural volume control knob” and much, much more.

Top picture courtesy: http://www.ucsf.edu/news/2009/01/8225/study-powerful-genes-and-cancer-points-vitamin-d-inflammation

Here is some Artificial Life for your summer reading. The Proceedings of Artificial Life XIV (complement to the conference at the end of this month in New York City) is a peer-reviewed venue for papers covering the following topics: evolutionary dynamics, soft robots, agent behavior, collective behaviors, social dynamics and evolution, and cellular automata/ self-organizing systems.

Today, I comment on two examples of pareidolia [1] from around the blogosphere:

TOP: We see the eggplant [2], God is the eggplant, I am the walrus. Coo coo ka-choo.

BOTTOM: A small bit of evidence, a massive failure of interpretation. The map is bad and the territory is rugged.


[1] McCardle, G.     Pareidolia, or Why is Jesus on my Toast? Skeptoid blog, June 6 (2011).


[2] Nasreen, T.   Proof for the existence of god. No Country for Women blog, July 10 (2014).

[3] Butler, F.   The Laminin Molecule and the Inadequacies of Evidentialism. Hip and Thigh blog, June 1 (2012).

Here are a few readings on the origins of the “horse head mask” meme. Part social experiment, part cousin of anonymous, and definitely distinct from furry fandom, the horse head is on! Here are some readings:

Ibsen, D.A.   Meme of the Day: horse head mask. FiveThot website, August 19 (2013).

Horse Head Mask. Know Your Meme website.

Horse Head Adventure. Lonely Planet Travel, June 22 (2005).

Murphy, L.   The best Guy Fawkes masks in Anonymous history. Daily Dot, October 31 (2013).

Here are some evolution-related links from my reading queue. Topics: morphological transformations [1], colinearity in gene expression [2], and sex determination [3].

The first two readings [1,2] place pattern formation in development in an evolutionary context, while the third [3] is a brand new paper on the phylogeny, genetic mechanisms, and dispelling of common myths involved with sex determination.

[1] Arthur, W.   D’Arcy Thompson and the Theory of Transformations. Nature Reviews Genetics, 7, 401-406 (2006).

[2] Rodrigues, A.R. and Tabin, C.J.   Deserts and Waves in Gene Expression. Science, 340, 1181-1182 (2013).

[3] Bachtrog et.al and the Tree of Sex Consortium   Sex Determination: Why So Many Ways of Doing It? PLoS Biology, 12(7), e1001899 (2014).

I invite you to take a look at a new paper by myself and Richard Gordon called “Toy Models for Macroevolutionary Patterns and Trends”, out now in the journal Biosystems. This will eventually be part of a special issue called “Patterns of Evolution”. There is also a Github repository, which will house examples of toy models and other supplemental information. The paper reviews and/or describes 13 toy models, some pre-existing and others brand new examples. Toy models are representations that are intentionally oversimplified, used to approximate overarching trends while at the same time being sensitive to evolutionary context.

Above is an example of a macroevolutionary toy model called the coupled avalanche. We introduce 13 different toy models that cover a range of macroevolutionary phenomena such as the generation of diversity, the representation of lineages, and nonlinear evolutionary changes. There is also an undercurrent of meta-theory and why that is important to evolutionary theory-building.

The paper also provides examples of application domains, such as Artificial Life simulations and the analysis of high-throughput data. Toy models can also be used in tandem to approximate difficult evolutionary problems. While I do not want to give away too much of the details, I will say that the paper should prove useful to hard-core biologists, evolutionary modelers, bioinformaticians, and philosophers of science alike.

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