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> http://www.physorg.com/news82190531.html > > "Rabinovich and his colleague at the Institute for Nonlinear Science at the > University of California, San Diego, Ramon Huerta, along with Valentin > Afraimovich at the Institute for the Investigation of Optical Communication > at the University of San Luis Potosi in Mexico, present a new model for > understanding decision making. Their paper, titled "Dynamics of Sequential > Decision Making," has been published on Physical Review Letters."
I did not read the paper (as it is not available free online) but at this page http://inls.ucsd.edu/wlc_locust.html I found a powerpoint that illustrates the main idea, and points to a number of related papers with abstracts online. I think the basic idea they describe is probably sound, although not as new as they suggest, and not deserving of the hype used to describe it. There has long been a question regarding the best way to apply nonlinear dynamics concepts to the brain. At first, naively, it was proposed that ideas, memories, perceptions, etc. should be viewed as **attractors** of neurodynamics -- but eventually folks realized that the brain rarely settles into attractors in the dynamical systems theory sense, so that (much as in the dynamical systems theory analysis of financial time series, for example) one is generally dealing with complex dynamics of **transients** rather than attractors. What Rabinovich et al are saying is, in essence, that neural systems are characterized by complex attractors with multiple "lobes", and that decision-making often boils down (at the neurodynamic level) to the neural net state switching between one lobe or another of the multi-lobe attractor -- during the "transient" period while the dynamics is "converging" to the attractor (but note that in a real-life neurodynamic context, convergence basically never happens because of new stimuli coming into the brain and perturbing the state in another direction...). So, then you can look at a probabilistic model of switching between lobes of the attractor -- a kind of Markov chain model where the states are attractor-lobes and (in the simplest case) there is a matrix of transition probabilities between attractor-lobes. And in general this will be a Markov process with history. The notion of attractors with lobes is loosely related to the notion of hyperchaos http://www.scholarpedia.org/article/Hyperchaos which treats attractors with multiple Liapunov exponents. I wrote about this sort of thing in 1994 in the book Chaotic Logic, see e.g. section 2.3.2 at the end of http://www.goertzel.org/books/logic/chapter_two.htm Rabinovich et al are getting at a similar idea, but in extremely simple contexts (e.g. behavior of insects and mollusks).... Is this a "window on consciousness", as Rabinovich describes? Well, sure it is, in a sense that consciousness is related to making decisions, and it's interesting to observe how decision-making may resolve itself in terms of complex nonlinear neurodynamics. But this kind of observation (about the neurodynamic correlates of decision-making experience) is of course only a small part of the picture regarding consciousness, or decision-making, or intelligence, etc. -- Ben G ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
