Representation of Value in the Primate Brain
Paul Glimcher, New York University
Description: Pigeons really like millet seed, monkeys crave juice, and humans get a kick out of winning money. While all animals don't enjoy the same rewards, Paul Glimcher has discovered some common features in the way animal brains learn to recognize and pursue something of value.
Glimcher is one of the founding fathers of the young field of neuroeconomics, in which economic theories help inform investigations of brain function. It's not surprising then, that his approaches include game theory as well as measuring the firing of single neurons. Glimcher's talk details his research from the past 15 years, what he describes as an attempt to "add something" to the classic studies on the basal ganglia circuit conducted by fellow symposium speaker Okihide Hikosaka. From Hikosaka's data and other research, Glimcher came to believe that neurons of the substantia nigra (part of the basal ganglia) were coding for something of worth to an animal, but that these neurons were "responding not to reward per se, but to deviations to expectation." For instance, if a pigeon expected a delivery of millet seed following a conditioned cue, no neurons fired, but if the reward was delayed, then suddenly delivered, the pigeon would find its initial prediction in error, and its neurons burst into action.
Various models emerged to capture the ways in which these neurons, energized primarily by the neurotransmitter dopamine, enabled animals to adjust expectations about and predict rewards. But Glimcher found fault with others scientists' "conditional parameters." He says, "As an economist, this is frustrating." So he developed three mathematical axioms for testing the so"called Reward Prediction Error (RPE) models. His work "suggested a way of unifying the data," with the notion that the basal ganglia learns "the values of actions in a quantitative way from the dopamine neurons and the incoming stimulus."
Glimcher hypothesized that dopamine neurons take the value of a reward just received, "and subtract it from a weighted exponential average of previous rewards, and if there's no mismatch, there should be no firing ofdopamine neurons." Human, monkey and pigeon studies -- based on gambling, juice, and seed rewards, respectively -- solidified his notion that dopamine neurons are part of an RPE encoding system where they convey the differences between rewards expected and rewards received. This has led Glimcher to believe that "one of the principle functions of the basal ganglia is to learn the values of our actions, represent them, and pump out the data to produce choice."
About the Speaker(s): Paul Glimcher has focused for the past decade on identifying and characterizing the signals that intervene between the neural processes involved with sensory encoding, and the neural processes involved in generating movement -- the signals, he says, that in principle underlie decision"making.
Glimcher came to New York University from the University of Pennsylvania, where he earned his Ph.D. in Neuroscience in 1989, and where he served as a postdoctoral research fellow. He graduated from Princeton University with an A.B. in Neuroscience in 1983.
Glimcher is a member of the Society for Neuroscience, the American Economic Association, the Society for Neuroeconomics (of which he was founding president), and AAAS.
He is the author of Neuroeconomics: Decision Making and the Brain(Elsevier, 2008), and Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics (MIT Press, 2004), among other books and publications.
Host(s): School of Science, McGovern Institute for Brain Research at MIT
It looks like no one has posted a comment yet. You can be the first!
More from MIT World — special events and lectures
Added over 2 years ago | 01:09:00 | 1577 views
Added over 2 years ago | 00:57:18 | 6946 views
Added over 2 years ago | 00:26:39 | 2204 views
Added over 2 years ago | 00:48:09 | 1610 views
Added over 2 years ago | 00:47:19 | 2903 views
Added over 2 years ago | 02:13:00 | 6870 views