The management of uncertainty in the human brain: new experimental insights

When someone talks to me about using neuroscience to inspire AI theory, I always complain that we simply don't understand the brain well enough for this to be feasible yet.

I definitely stand by this statement -- but, I'm always excited when some neuroscience results come out that seem to have some connection with ideas I've encountered in my AI work.

Along these lines: Some recent neuroscience results, pointed out to me by Pei Wang, appear to qualitatively validate the approach taken in my Novamente AI system and Pei's NARS AI system (and some other AI approaches such as Walley's imprecise probability theory), in which numbers measuring frequency are augmented by additional numbers measuring the uncertainty in these frequency measures.

In other words, some of us maverick AI theorists have been saying for a while that using just ONE number (typically probability) to measure uncertainty is not enough. Two numbers -- e.g. a probability and another number measuring the "weight of evidence" in favor of this probability (or to put it differently, the "confidence" one has in the probability) -- are needed to make a cognitively meaningful algebra of uncertainty.

Well, this paper suggests that the brain also reckons in terms of uncertainties-of-probabilities as well as probabilities themselves:

Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making
Ming Hsu, Meghana Bhatt, Ralph Adolphs, Daniel Tranel, and Colin F. Camerer, Science 9 December 2005: 1680-1683

Much is known about how people make decisions under varying levels of probability (risk). Less is known about the neural basis of decision-making when probabilities are uncertain because of missing information (ambiguity). In decision theory, ambiguity about probabilities should not affect choices. Using functional brain imaging, we show that the level of ambiguity in choices correlates positively with activation in the amygdala and orbitofrontal cortex, and negatively with a striatal system. Moreover, striatal activity correlates positively with expected reward. Neurological subjects with orbitofrontal lesions were insensitive to the level of ambiguity and risk in behavioral choices. These data suggest a general neural circuit responding to degrees of uncertainty, contrary to decision theory.

Of course, these results contradict aspects of traditional statistical decision theory, but they don't contradict mathematical probability theory in general -- just some particular, conventional ways of using it to study decisions. The way probability theory is used in Novamente, and the way it's used by imprecise-probabilities-theorists like Peter Walley, is actually somewhat validated by these findings.

The article can be obtained (for money) at

http://www.sciencemag.org/cgi/content/abstract/310/5754/1680

and some related journalistic discussion is at

http://groups.yahoo.com/group/envecolnews/message/2651

-- Ben Goertzel