October 31, 2014

Introducing the Evolution of Inequality Project

The study of income and resource inequality has been the academic topic du jour this year, highlighted by Thomas Piketty's economic history opus [1] and a special issue of Science [2]. There was even a paper on the 1% vs. the 99% of academic publishing [3] which argues that in terms of citations, the rich tend to get richer. But how do these patterns emerge and evolve? Is it a merely a statistical artifact, or a reflection of how complex, hierarchical societies tend to evolve? For example, we might assume that extreme inequality is maladaptive. But upon simulating a range of artificial societies of different population sizes, initial degrees of stratification, and behavioral features, we might find that extreme inequality tends to occur under specific conditions.

This is the motivating factor for my interest in the topic. Unlike many of the more well-versed approaches to the topic, an alternative view of inequality is a cross between statistical distributions, nuanced views of human sociobiology, and the aftermath of social change. While most economists have taken a materialist view of inequality, I feel that evolutionary perspectives would be helpful in teasing out questions of inequality's origins. This might involve data as diverse as historical data, ethnographic data, evolutionary modeling, and behavioral/neurophysiological data. In the end, we will be able to provide a conceptual alternative to the usual discussion at the intersection of behavior, biology, and social change. An inclusive, multidisciplinary approach is a core component of this project.

My research organization (Orthogonal Research) is trying to initiate work on a project called "The Evolution of Value and Inequality". This project is an attempt to understand the emergence of these social inequalities as a set of evolutionary and biobehavioral phenomena. While the argument can be made that an evolutionary perspective might be helpful in understanding unequal allocations of resources, it is sorely lacking in the general discussion. The broadness of the initiative is necessary to make the connection between the pure inferential approach of evolutionary science coherent public policy outcomes. An initial grant application to the Washington Center for Equitable Growth (submitted January 2014) did not get funded, one reason being that the idea needed more conceptual and empirical fleshing out.

So upon doing some more conceptual refinement, I just finished submitting a second version of this proposal to the Institute for New Economic Thinking (INET). While I will not get into the technical details here, the basic idea is to construct adaptive computational models that mimic a social hierarchy (so-called hierarchical network models). Each node of these directed graphs are informed in their behavior by neuroimaging and other physiological sources of data on human behavior.

The project is focused around evolutionary models of social change (social and cultural change), the underlying assumptions of which can be verified by the collection of biobehavioral data (e.g. neuroimaging experiments). The empirical component is meant to test assumptions of individual and social behavior, and serves as an alternative to the rational expectations assumption that dominates much of conventional economics. However, it also refines many of the model-free findings that
characterize behavioral economics [4].

The evolutionary aspects of this work are also quite interesting. Because we are bridging the short-term (behavioral) and the longer-term (social evolution), there are at least three forms of adaptation: a social learning mechanism, a cultural evolutionary form of selection, and a neurophysiological imperative that satisfies various material, social, and existential needs of an individual. This gives us a fitness and selection criterion that is tangentially related to reproductive success. Subsequent evolutionary algorithms and simulations may bear out the evolutionary dynamics of value construction and social stratification.

Another contribution of this project is to link the statistical aspects of inequality with an evolutionary and demographic framework. The oft-referenced phrase the "1%" or the "0.01%" has its roots in an exponential (non-normal) statistical distribution called the power law. Power laws of various size tend to describe observed income distributions in many different types of society. As inequality increases sharply in a single society, or as different degrees of inequality are observed in different contexts, the power law and in conjunction with various stable states can be used as selection criteria.

Schematic of expected results. Both the C and L parameters refer to operations on intra- and bi-level hierarchical networks dynamics, respectively.

As proposed in the first part of this post, the resulting evolutionary algorithms and experimental inquiries provide us with a possibility space for given outcomes. Given a set of initial conditions, we can observe the tendencies of inequality of resource allocation. If there are common outcomes relative to a number of different initial conditions, this could tell us something cross-cultural and fundamental about the nature of inequality. Ultimately, the outcomes of this project could help to identify and predict opportunities to head off crises and as an architecture for achieving sustainable economic growth.

[1] Piketty, T.   Capital in the 21rst Century. Harvard University Press (2014).

[2] Citation for the special issue: Science, 344, May 23 (2014). One article with particular relevance to social evolution is: Pringle, H.    The Ancient Roots of the 1%. Science, 344, 822-825 (2014).

[3] Ioannidis JPA, Boyack KW, Klavans R.   Estimates of the Continuously Publishing Core in the Scientific Workforce. PLoS One, 9(7), e101698. doi:10.1371/journal.pone.0101698 (2014).

[4] Camerer, C.F. and Loewenstein, G.   Behavioral Economics: past, present, future. In "Advances in Behavioral Economics". C.F. Camerer, G. Loewenstein, and M. Rabin eds. Chapter 1. Russell Sage Foundation (2004).

Behavioral Economics Reading List. Russell Sage Foundation blog, March 23 (2012).

October 26, 2014

C. elegans as an Evolutionary Model

In the past year, I have been starting to use the nematode Caenorhabditis elegans (roundworm) as a model organism. Not only have I helped to establish the DevoWorm project, I am also starting to engage with C. elegans in a wet-lab setting. As a consequence, I am learning about multiple facets of C. elegans biology. C. elegans is a well-established model organism, having well-characterized neural and developmental systems. The nervous system contains just 302 cells, with a full accounting of the connectome (synaptic connections) [1]. The developmental system is also well-characterized, with a lineage tree [2] having been worked out for the entire organism. While a lineage tree relies upon deterministic mechanisms (and thus cannot be applied to organisms such as Mammals), it does provide us with a clear accounting of cell differentiation and organ formation during development. Thus, C. elegans is a tractable model for whole-organism investigations (Figure 1).

Figure 1. Anatomy of the adult hermaphrodite. COURTESY: WormAtlas.

But what about evolution? At first pass, it seems as though asking evolutionary questions is not a tractable feature of roundworm biology. Nevertheless, we can use this worm to answer several outstanding questions in evolution [4]. I will use information from a recent review by Jeremy Gray and Asher Cutter [5] to discuss these potential research advances (Figure 2). The actual future applications of C. elegans as an evolutionary model might turn out to investigate other issues. As it turns out, the roundworm provides a happy medium between more traditional models of experimental evolution (microbes) and complex organisms with long generation times (humans). While C. elegans have a relatively short generation time (~50 hours), they also have complex phenotypes with organs.

Figure 2. The life cycle and means of experimental manipulation for evolution experiments. COURTESY: Figure 1 in [5].

The most common means of experimental evolution proposed in [5] is the mutation accumulation (MA) approach. MA may also serve as a weak factor in determining life-history traits in a species [6]. In experimental evolution, the MA approach allows us to observe the role of mutational variation in evolution. One way to apply this method might be to manipulate a single gene (using directed mutagenesis, gene editing, or RNAi -- see Figure 2) and then place it in a genetic background. Rather than waiting for a series of mutations to emerge in a population, mutation is induced to maximize the variation upon which evolution can act upon [7].

Another means of experimental evolution discussed in [5] is co-evolution between worms and pathogens. This can done by culturing worms in ecological context over several generations. One prediction involves the evolution of tradeoffs observed in already co-evolved relationships such as C. elegans growth rate and pathogenic resistance [8]. A secondary means of understanding the ecology of evolution involves introducing environmental fragmentation through introducing spatial variation (physical barriers or agar gradients) on a culture dish. This can produce to genetic bottlenecks and other effects related to population structure and neutral processes.

A third strategy discussed in [5] involves examining different reproductive strategies and degrees of adaptability between species of Caenorhabditis. The latter topic might include a better understanding of how the degeneracy [9] of neuronal and genetic circuits that lead to observable behaviors and phenotypes evolves. Yet there is also great potential for C. elegans to be used as an eco-evo-devo [10] model which integrates to response of environmental stimuli by cell and molecular mechanisms of development over evolutionary time (Figure 3). While I do not have plans on establishing my own C. elegans experimental evolution program in the near future, stay tuned.

Figure 3. A fledgling eco-evo-devo approach to C. elegans.

[1] Jarrell, T.A., Wang, Y., Bloniarz, A.E., Brittin, C.A., Xu, M., Thomson, J.N., Albertson, D.G. Hall, D.H., and Emmons, S.W.   The Connectome of a Decision-Making Neural Network. Science, 337, 437-444 (2012).

Please also see The Connectome Project website.

[2] Sulston, J.E., Schierenberg, E., White, J.G., and Thomson, J.N.   The Embryonic Cell Lineage of the Nematode Caenorhabditis elegans. Developmental Biology, 100, 64-119 (1983).

[3] Jovelin, R., Dey, A., Cutter, A.D.   Fifteen Years of Evolutionary Genomics in Caenorhabditis elegans. eLS, doi:10.1002/9780470015902.a0022897 (2013).

[4] For a review of Caenorhabditis phylogeny and evolutionary biology, please see: Fitch, D.H.A. and Thomas, W.K.   Evolution. In "C. elegans II", Chapter 29. Cold Spring Harbor Laboratory, Woods Hole, MA (1997).

[5] Gray, J.C. and Cutter, A.D.   Mainstreaming Caenorhabditis elegans in experimental evolution. Proceedings of the Royal Society B, 281, 20133055 (2014).

[6] Danko, M.J., Kozlowski, J., Vaupel, J.W., and Baudisch, A.   Mutation Accumulation May Be a Minor Force in Shaping Life History Traits. PLoS One,  7(4), e34146 (2011).

[7] Thompson, O., Edgley, M., Strasbourger, P., Flibotte, S., Ewing, B., Adair, R., Au, V., Chaudhry, I., Fernando, L., Hutter, H., Kieffer, A., Lau, J., Lee, N., Miller, A., Raymant, G., Shen, B., Shendure, J., Taylor, J., Turner, E.H., Hillier, L.W., Moerman, D.G., and Waterston, R.H.   The million mutation project: a new approach to genetics in Caenorhabditis elegans. Genome Research, 23(10), 1749-1762 (2013).

[8] Schulte, R.D., Makus, C., Hasert, B., Michiels, N.K., and Schulenburg, H.   Multiple reciprocal adaptations and rapid genetic change upon experimental coevolution of an animal host and its microbial parasite. PNAS USA, 107, 7359 –7364 (2010).

[9] Degeneracy involves structurally different elements (such as functional neuronal networks) that converge upon the same output. An example of this within C. elegans: Trojanowski, N.F., Padovan-Merhar, O., Raizen, D.M. and Fang-Yen, C.   Neural and genetic degeneracy underlies Caenorhabditis elegans feeding behavior. Journal of Neurophysiology, 112, 951-961 (2014).

[10] Abouheif, E., Fave, M.J., Ibarraran-Viniegra, A.S., Lesoway, M.P., Rafiqi, A.M., and Rajakumar, R.   Eco-evo-devo: the time has come. Advances in Experimental Medicine and Biology, 781, 107-125 (2014).

October 20, 2014

October, 21, 2015 is roughly 365 days away!

Or 365.37708 days away, to be a bit more precise.

Screenshot courtesy of Back to the Future Countdown and Hero Complex. Are we approaching yet another "temporal paradox"? Or is it just a multiverse? Ah, I see. Someone must have gone back and changed something....

October 10, 2014

The Grids of Nobel (Medial Temporal Lobe-rific)

This year's Nobel Prize in Physiology and Medicine went to John O'Keefe, May-Britt Moser, and Edvard I. Moser for their work on the neurophysiology of spatial navigation [1]. The prize was awarded "for their discoveries of cells that constitute a positioning system in the brain". Some commentators have referred to these discoveries as constituting an "inner GPS system", although this description is technically and conceptually incorrect (as I will soon highlight). As a PhD student with an interest in spatial cognition, I read (with enthusiasm!) the place cell literature and the first papers on grid cells [2]. So upon hearing they had won, I actually recognized their names and contributions. While recognition of the grid cell discovery might seem to be premature (the original discovery was made in 2005), the creation of iPS cells (the subject of the 2012 award) only dates to 2007.

John O'Keefe is a pioneer in the area of place cells, which provided a sound neurophysiological basis for understanding how spatial cognitive mechanisms are tied to their environmental context. The Mosers [3] went a step further with this framework, discovering a type of cell that provides the basis for a metric space (or perhaps more accurately, a tiling) to which place cell and other location-specific information are tied. The intersection points on this grid are represented by the aptly-named grid cells. Together, these types of cells provide a mental model of the external world in the medial temporal lobe of mammals.

Locations to which grid cells respond most strongly.

Place cells (of which there are several different types) are small cell populations in the CA1 and CA3 fields of the Hippocampus that encode a memory for the location of objects [4]. Place cells have receptive fields which represent specific locations in space. In this case, a cell's receptive field corresponds to locations and orientations to which the cell responds most strongly. When the organism is located in (or approaches) one of these receptive fields, the local field potential of the cell population is activated at a maximum of 20Hz. As place cells are in the memory encoding center of the brain, place cells respond vigorously when an animal passes or gets near a recognized location. Grid cells, located in the entorhinal cortex, serve a distinct but related role to that of place cells. While spatial cognition involves many different types of input (from multisensory to attentional), place cells and grid cells are specialized as a mechanism for location-specific memory.

Variations on a grid in urban layouts. COURTESY: Athenee: the rise and fall of automobile culture.

How do we know this part of the brain is responsible for such representations. Both place and grid cells have been confirmed through electrophysiological recordings. In the case of place cells, lesions studies [5] have been conducted to demonstrate behavioral deficits during naturalistic behavior. In [5], lesions (made via lesion studies) of hippocampal tissue results in deficits in spatial memory and exploratory behavior. In humans, the virtual Morris Water Maze [6] can be used to assess performance with regard to finding a specific landmark (in this case, a partially-submerged platform) embedded in a virtual scene. The recall of a particular location is contingent on people's ability to a) find a location relative to other landmarks, and b) people's ability to successfully rotate their mental model of a particular space. 

An example of learning in rats during the Morris Water Maze task. COURTESY: Nutrition, Neurogenesis and Mental Health Laboratory, King's College London.

As a relatively recent discovery, grid cells provide a framework for a geometric (e.g. Euclidean) representation of space. Like place cells, the activity of grid cells are dependent upon the behavior of animals in a spatial context. Yet grid cells help to provide a larger context for spatial behavior, namely the interstitial space between landmarks. This allows for both the creation and recognition of patterns at the landscape spatial scale. Street patterns in urban settlements that form grids and wheel-and-spoke patterns are no accident -- it is the default way in which humans organize space.

An anatomical and functional view of the medial temporal lobe. COURTESY: Figure 1 in [7].

There are some interesting but unexplored relationships between physical movement and spatial navigation which both involve a coordinate system for the world that surrounds a given organism. For example, goal-directed arm movements occur within a multimodal spatial reference frame that involves the coordination of visual and touch information [8]. While limb movement and walking involve timing mechanisms associated with the motor cortex and cerebellum, there are implicit aspects of spatial memory in movement, particularly over long distances and periods of time. There is an emerging field called movement ecology [9] which deals with these complex interconnections.

Another topic that falls into this intersection is path integration [10]. Like the functions that involve place and grid cells, path integration also involves the medial temporal lobe. Path integration is the homing ability of animals that results from an odometer function -- the brain keeps track of footsteps and angular turns in order to generate an abstract map of the environment. This information is then used to return to a nest or home territory. Path integration has been the basis for digital evolution studies on the evolutionary origins of spatial cognition [11], and might be more generally useful in understanding the relationships between the evolutionary conservation of spatial memory and its deployment in virtual environments and city streets. While this is closer to the definition of an "inner GPS system", there is so much more to this fascinating neurophysiological system.

UPDATE (10-18): Here is the Nature feature on the 2014 Nobel Prize in Physiology and Medicine.

[1] Nobel Prize Committee: The Nobel Prize in Physiology or Medicine 2014. Nobelprize.org, Nobel Media AB. October 6 (2014). 

[2] Hafting, T., Fyhn, M., Molden, S., Moser, M-B., and Moser, E.I.   Microstructure of a spatial map in the entorhinal cortex. Nature, 436(7052), 801–806 (2005). 

[3] Moser, E.I., Kropff, E., and Moser, M-B.   Place Cells, Grid Cells, and the Brain's Spatial Representation System. Annual Review of Neuroscience, 31, 69-89 (2008).

[4] O'Keefe, J. and Nadel, L.   The Hippocampus as a Cognitive Map. Oxford University Press (1978). 

[5] For the original Morris Water Maze paper: O'Keefe, R.G., Garrud, P., Rawlins, J.N., and O'Keefe, J.   Place navigation impaired in rats with hippocampal lesions. Nature, 297(5868), 681–683 (1982). 

[6] For the virtual adaptation of the water maze for humans, please see: Astur, R.S., Taylor, L.B., Mamelak, A.N,, Philpott, L., and Sutherland, R.J.   Humans with hippocampus damage display severe spatial memory impairments in a virtual Morris water task. Behavioral Brain Research, 132, 77–84 (2002).

[7] Bizon, J.L. and Gallagher, M.   More is less: neurogenesis and age-related cognitive decline in Long-Evans rats. Science of Aging, Knowledge, and Environment, (7), re2 (2005).

[8] Shadmehr, R. and Wise, S.P.   The Computational Neurobiology of Reaching and Pointing. MIT Press, Cambridge, MA (2005).

For more about these interconnections, there is a new MOOC related to Spatial Cognition on Coursera from Duke University ("The Brain and Space" taught by Jennifer Groh).

UPDATE (10-22): Here is a brand new paper on "head-direction cells", which work according the same principle as place and grid cells, but are instead related to encoding the details of and executing spatial orientation during attention:

Marchette, S.A., Vass, L.K., Ryan, J., and Epstein, R.A.   Anchoring the neural compass: coding of local spatial reference frames in human medial parietal lobe. Nature Neuroscience, doi:10.1038/ nn.3834 (2014).

[9] Nathan, R.   An emerging movement ecology paradigm. PNAS, 105(49), 19050–19051 (2008).

[10] McNaughton, B.L., Battaglia, F.P., Jensen, O., Moser, E.I., and Moser, M-B.   Path integration and the neural basis of the 'cognitive map'. Nature Reviews Neuroscience, 7, 663-678 (2006).

[11] Grabowski, L.M., Bryson, D.M., Dyer, F.C., Pennock, R.T., and Ofria, C.   A case study of the de novo evolution of a complex odometric behavior in digital organisms. PLoS One, 8(4), e60466 (2013) AND Jacobs, L.F., Gaulin, S.J., Sherry, D.F., and Hoffman, G.E.   Evolution of spatial cognition: sex-specific patterns of spatial behavior predict hippocampal size. PNAS, 87(16), 6349-6352 (1990).

October 6, 2014

The Map of the Cat, the Hair of the Dog, and Other Metaphors and Descriptors

What's in a set of descriptions, or a set of metaphors for that matter? Quite a bit or very little, depending on whether or not you are working in your area of specialty. Richard Feynman once (and to the great consternation of neurophysiologists within earshot) referred to a feline brain atlas as the “map of the cat” (not to be confused with Arnold’s Cat Map).

Recurrent cats! But what about its brain?

This parable, of course, speaks to the role of jargon in science. I am generally in support of jargon-filled science, providing it serves to conceptually unify and serve as shorthand for complex phenomena. The problem occurs when it serves as a membership proxy into the high priesthood of Discipline x or Disipline y (ironically for Feynman, one of these disciplines was and is theoretical physics).

Far from making one sound like a drunken PoMo generator, jargon and highly-specialized language is sometimes an efficient information encoding scheme. But sometimes shortcuts that transcend jargon (but only briefly) are quite useful as well. But words are not enough. Sometimes it takes not a paradigm shift but a conceptual shift. And sometimes that takes a semi-humorous (and non-specialized) turn of phrase. Perhaps even a pun or two (to wit):

Q: what do an airplane crash investigators and experimental scientists have in common?

A: both look for an answer inside of a black box!