Posts Tagged ‘Dayan’

Computational neuroeconomics?

Computational neuroeconomics?

Neuroeconomists develop their own jargon, as it is to be expected from a consolidating community of scientists with distinct interests. But denominations, categorical classifications, and basic concepts in neuroeco are very much still in the early stages of their definitions – they have not been “blackboxed” yet.

“Computational neuroeconomics” is one of such terms. I was a bit tired of nodding to my interlocutors when computational neuroeco popped up in interviews, without being sure to understand how different it was from “not computational” neuroeco, or from computational modeling in cognitive neuroscience.

A first possibility was that it could resemble this class of models where connectivity of different brain regions is represented by an analogy with the architecture of a computer.  This is the kind of model used by Peter Dayan and Szabolcs Kali in a paper in 2004, who discussed memory storage and retrieval.

It could have also been the models inspired not by computer hardware, but by softwares: algorithm processes which demonstrate that starting with very simple building blocks and logical rules, an organism could  achieve complex cognitive tasks like letter recognition and other sensory to motor tasks.

But computational neuroeconomics seems in fact to represent an alternative, third possibility.  An entire session was devoted to it in the third day of the annual meeting of the Society for Neuroeconomics. It featured papers which were basically game theory applied to social cognitive problems. The language of game theory provides concepts  to think many useful parameters of behavior: strategies, payoffs, probabilities, types. As I understand it, the task of computational neuroeco is to operationalize those concepts. In the speeches of the session, it was interesting to see how the presenters navigated between mathematical sophistication, and constant references to pragmatic issues in social behavior: what theory of mind emerges from repeated interactions between players, or how risk minimization is accomplished through learning.

Is it a new approach in neuroeconomics? Not really. When one thinks about it, it is “simply” further work in a direction impulsed by Paul Glimcher and his collaborators since the very beginnings of neuroeconomics, when they introduced expected utility and then Bayesian Nash equilibrium in their studies of neurons in the LIP area for monkeys.  In this light, computational neuroeco appears to be at the very core of the new discipline.


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