What is NESSHI?

NESSHI stands for “The Neuro-turn in European Social Sciences and Humanities: Impact of neuroscience on economics, marketing and philosophy”.

It is a 3-year European project involving partners from the UK, Netherlands, Germany and France, which started in June 2011. It aims at providing a better view of the transformation of disciplines from SSH (social sciences and humanities) under the influence of neuroscience. Didn’t you notice that they became more “brainy” lately? NESSHI wants to investigate how, when, why this happened, and for what consequences for our societies. Quite an ambitious program!

We choose to include a strong sociological, empirical dimension: with field work, lots of interviews and also with a bit of scientometrics, we hope to get as close as possible to the daily practices of researchers. The hope is to better understand why, from their point of view, neuro-SSH have been deemed interesting, irrelevant, hype, or revolutionary, and what difference did it make in their research routines. The utlimate end is to understand how these changes have already had an impact down the road on our societies: has marketing changed? (oh yes!), have economic policies changed? (soon, we’re told!), and what about the very notion of an individual’s autonomy?

The NESSHI team had its first workshop in Leiden on June 9, with each partner laying out their work plans for the next three years. Check the website of the project and participate in the associated forum (can be accessed from the website) to get news and add your voice to the conversation: Did you experience the neuro-turn in your discipline?

- Website of NESSHI: www.nesshi.eu

- Press release by Erasmus University Rotterdam (a participant to the project): http://www.rsm.nl/home/faculty/Research%20News?p_item_id=6635945

http://webcolleges.uva.nl/mediasite/Viewer/?peid=cb7fbbca8eb040ae9e99c75d4089dc481d

(does not properly display on Google Chrome 11 beta, looks fine on Internet Explorer 9 and Firefox 3.6)

Michael Anderson

To me, the most interesting contribution on Saturday was by Michael Anderson (a cognitive scientist and philosopher by training, from University of Lancaster, PA), who reported on his study of neuroimaging data: to counter the criticisms about reverse inference, and over-simplicity of localizationism, he presented a “simple” idea – that network analysis could shed light on the agency of cognitive functions. Those would not be over specialized and called sequentially,  but simultaneously recruited at the image of a cluster of computers. Your reaction might be “nothing new, how is that different from systems cognitive neuroscience?” Well, the metaphor of networks allows for new insights, since network analysis comes with an analytical framework to interpret the structure of the network (its density, clusterisation, diameter, etc.) where to my knowledge systems analysis remains quite silent. Also, network analysis translates in intuitive visualizations which help generate new hypotheses, and stimulate thinking generally (at least so I found, when using network analyses in other contexts). The paper which describes this “network in the brain” approach is here.

The sessions have been filmed, so Michael Anderson’s performance should be online soon! Meanwhile, here are the slides of my own presentation – that won’t provide you with a detailed argument, but give you an idea of the topics I evoked.

http://www.slideshare.net/levallois1/why-the-increasing-societal-interest-in-neuroimaging

PS: In the review, I did not comment on the book’s dust jacket. As can be seen above, it conveys a very gloomy impression of the topic of neuroeconomics – for a book which actually promotes it! I would be curious to know how this very wrong choice of an illustration came to be made.

To explain a bit: this is realized with Gephi, a software for  the visualization of networks evolving through time.

What is the data represented? This is a co-occurrence network, obtained through the AlchemyAPI plugin for Gephi. I know, this sounds cryptic! Basically, the idea is that words represented in the network are the most frequent ones used in webpages discussing neuromarketing. A link between any two nodes means that those two words frequently co-occur (ie, they are used in same documents, as opposed to being frequently used but never appearing simultaneously in the same text).

This is just a proof of concept –  the movie is hard to read and needs much refinement. I presented today at the Sunbelt conference a refined version, with accompanying slides (do click on the slides, not on the timeline below them).

“Economics Job Market Rumor” is a website which functions like a blog. You write a blogpost which consists most often of a single line, and which is the statement of a rumor you’ve heard. People can react to that by writing  a comment, confirming or disconfirming the rumor. The possibility to remain anonymous acts as an incentive to voice honest opinions, I suppose.

A friend of mine had posted a simple question related to “history of economic thought”, and got fairly sympathetic responses to this field. The average, job-seeking economist is not completely averse to history of economic thought, would it seem (good for me!)

What about neuroeconomics? I let you see the comments posted to a post consisting of the single word, “Neuroeconomics”. Very harsh!

Interestingly, it has been posted in the category “micro job rumors”, micro standing for “microeconomics” of course. Would the responses have been different in the smaller, but more oecumenic category “The Economics Discipline” instead? I just started a thread, let’s wait for the reactions!

This is exactly the strategic move attempted by Neurofocus (did they find it in their EEG readings, or were they advised by old-fashioned marketers?). I let you judge whether they succeed:

By the way, there is a real market developing for nerds rapping on uncool topics. Take your pick:

On Hayek and Keynes and business cycles

On natural selection, Darwinism and creationism (the performer is actually talented here!)

What do these big blobs mean?

The big, green one stands for all the journals published in neuroscience. The strings connecting this blob to some other blobs in the graph represent citations. The size of the blob represents the proportion of papers in neuroeconomics published in this discipline, according to a dataset spanning 1999-2009.

Not surprisingly, neurosciences is the biggest one. But, where is economics for example, and which size is it? Ok, I hope this was teasing enough (to neuroeconomists at least…): this is explained in much more details on a presentation I gave a few days ago, the slides of which you can find there. Note: if you are a neuroeconomist, a scientometrician, or a social network analyst, I am particularly interested in your feedback! Post a comment on this blog entry!

I am currently reading a fascinating book, “The Cognitive Revolution in Psychology” by Bernard J. Baars (1986).

With a long introduction, it provides informative material for an outsider like me on how the cognitive turn played out in psychology, and presents a clear historical background getting back to Wundt and the early experimentalists, and the origins of the behaviorist revolution. Then it is followed by a series of interviews of participants in the cognitive revolution: from the opponents (Skinner and others) to the enthusiasts, and the followers.

On the substance, I was stroke by how much behaviorism, which is the methodological orthodoxy that was overthrown by cognitive psychology, shares features with today’s textbook economics. Both share the status of a well-guarded orthodoxy: in their interviews, psychologists remember that behaviorism in psychology was exclusive, displaying a “nothing but” attitude: variables should be related to nothing but observable behavior, which disqualified the discussion of concepts like “memory” or “representations” ! Those words were taboo in psychology at least until the mid-1950s.  Looking back, psychologists consider that the methodological rigorousness of behaviorism, which insisted that each concept be operationally defined and testable, had the effect to strip psychology from its substance: the study of cognition, consciousness,  emotions and rational behavior were discouraged, virtually banned indeed, because these concepts did not readily translate into tightly defined behavioral variables that could be observed in an experimental setting.

I could not help but be reminded of a similar taboo in today’s economics, where the formation of preferences, or how the process of choice unfolds, is declared “out of bound” right from the introductory chapters in microeconomic textbooks: only an individual’s observable behavior, as it is instantiated in the outcome of the choice it performs, is to be taken into account.

Reading this book, neuroeconomists will also be strongly reminded of Princeton economists Gul and Pesendofer’ essay published in 2008, in which they defend a “nothing but” approach to the revealed preference approach – dismissing any kind of evidence from “inside the head”, and advocating bluntly a “mindless economics”. Behaviorists (or operationalists…) of the purest ink!

The cognitive revolution in psychology crystallized around the mid-1950s, early 1960s. Forty years later, nothing of that sort happened in economics, it seems to me. With behavioral economics and neuroeconomics, maybe that economics will jump directly to the next train: the neurocognitive turn. Or will it miss that one also?

Post-script: on an approaching topic, Wade Hands has a paper forthcoming in the CJE, which is a nice read.

A bibliometric study can be many things, in this case I am interested in how publications in neuroeconomics reflect its interdisciplinary nature. Online databases such as ISI Thomson help a great deal in performing such a study, and the remaining difficulties are probably of the conceptual sort (see the previous post!).

The results of those bibliometric studies are most commonly represented in graphs of social networks: they help read a tonne of information in just one picture.  I am currently training myself to use them, getting gray hair at pre-processing bibliometric files which then can be fed into those softwares…

This is how a social network can look like in practice (click on the pic to expand):

A co-citation network - green and yellow nodes indicate those papers citing heavily the original set