Our latest Report on Progress is a clear and accessible review of the field of neuroeconomics. “Understanding Human Decision-Making: Neuroeconomics” is by Dana Alliance member Paul Glimcher, Ph.D. Glimcher embodies the Alliance’s commitment to sharing brain science information and discoveries with all—science-curious, science-committed, and even intrigued sports fans.
In a lifetime of sports fandom milestones, no one game stands out more than Knicks vs. Lakers in the seventh game of the NBA finals in 1970. As a 14 year-old, I was glued to the radio (no TV coverage in those days for the game) as Marv Albert set the scene: injured center Willis Reed limps onto the court moments before tip- off, hits his first two shots, limps off, and the seemingly overmatched Knicks go on to win the game and their first NBA Championship. The stat line for second-year guard Walt “Clyde” Frazier was 36 points, 19 assists, and 7 rebounds.
One of the cool, under-the-radar programs the Society for Neuroscience offers during its annual conference is Meet the Experts, where top scientists volunteer to take any and all questions, mostly from grad students and postdocs. The researchers explain a bit about their work, but more about how they got to where they did and what they see in the field today. I attended two chats and wish I could have gone to more; they were an intriguing glimpse into how science is done, behind the posters, formal lectures, and symposia.
Fred “Rusty” Gage claimed that he didn’t have a lot of formal career advice for grad students—”I didn’t make any plans,” he said, following paths that interested him though they crossed disciplines. But he did have plenty to say on targeting research and dealing with criticism.
A Dana colleague and I attended a lecture on Tuesday about decision-making. Fitting, then, that we’d encounter the same travel problem—a delayed B train—and handle it differently. I watched three D trains pass and gambled that the next would be a B, the train that would get me to the lecture fastest. My colleague took the third D she saw (we weren’t together) and switched to a different train at the next station, the safer choice. We both arrived on time, though not without anxious moments.
I couldn’t help but think of my commute when Dana Alliance Member Paul Glimcher, professor of neural science, economics, and psychology at New York University, talked about a study involving apple trees during the James Arthur Lecture at the American Museum of Natural History. One of Glimcher’s graduate students set up an experiment asking people to “shake” virtual apple trees, moving as they pleased from tree to tree in an attempt to collect as many apples as possible (which would be exchanged for actual money). Perhaps the first shake would send a dozen apples to the ground, but if the third shake only resulted in two apples it was probably time to move on. It took the study’s subjects a few trees to get the hang of it, but they excelled at this task, often achieving close to maximum apple-acquisition efficiency.
The study conveys two central themes of Glimcher’s lecture: how evolutionary biology impacts decision making and whether natural selection pushes organisms towards the best possible traits, ideas popularized by Charles Darwin and economist/philosopher Adam Smith. Glimcher’s examples of wild animals innately performing extraordinary food calculations tells me that even if they aren’t maximizing their traits, they’re close enough.
Take the North American moose’s dietary dilemma. In cold climates, the moose must consume enough salt during the summer to last through the winter, when the ponds and lakes freeze. The moose has to balance several factors: it needs to eat enough dry food (grass) to keep its caloric intake above a certain point, but it also needs the salt from the water’s algae. It can’t just stuff its face, though, because its stomach is not all that big.
Glimcher showed this information plotted on a graph—how many calories the moose needed, how much salt it required, and the max its stomach could hold—and the combination of all three factors left just a tiny sliver of overlap. Sure enough, the moose’s intake falls right on the overlap. Its survival depends on it.
Glimcher also described a study involving a bird, in which researchers place various-sized worms on a conveyor belt that passed under the animal. Just as in the wild, the bird displayed remarkable decision-making, becoming more picky (only eating the bigger worms) when the conveyor belt moved quickly and less picky when the belt moved slowly (an indication that food was more scarce). Researchers devised a mathematical formula that considered the number of worms, their size, and the speed of the conveyor belt to determine the best strategy for the bird—like the moose, the bird always made the right choice.
Humans are certainly capable of similar reasoning—when we’re not, there are several explanations. A study involving Parkinson’s patients highlights the importance of dopamine neurons in decision-making. Glimcher mentioned a study where subjects played a simple computer game that involved collecting crabs in a bucket. There were two different buckets, and in each trial it should have been clear after a few “drops” which bucket was best. While most subjects performed exceptionally well, Parkinson’s patients off their medication did poorly, due in large part to the nerve cells in the brain that make dopamine having been damaged or destroyed.
Glimcher also referred to “the curse of choice,” and gave an example of a researcher posing as a jelly salesperson to highlight our errors in decision-making. In the experiment, the researcher offered potential customers six different flavors of jelly. If they sampled all six, they got a $1 discount on their purchase of any one jar. If they bought a jelly, the researcher e-mailed them to follow up on their satisfaction with their purchase. She found that 30 percent of people made the purchase and, of those people, 80 percent were satisfied.
The researcher upped the flavors of jellies to 24, but offered the same deal: sample six, get a $1 discount for one of the 24 jars. If you already have a favorite jelly, this should be a favorable offer—either you stick with your favorite or you prefer one of the six you sampled. Instead, as the number of options increased, peoples’ choices became less efficient. Only 3 percent of participants made the purchase, and there was just a 20 percent satisfaction rate. Glimcher said this problem could be fixed by devoting more neurons to the task, but neurons are “costly;” in other words, it’s inefficient to devote so many neurons to jelly selection.
After his lecture, the question-and-answer session highlighted several interesting points. When an audience member noted that environments are rarely static, Glimcher discussed the African cichlid fish, whose species evolved in separate lakes with different, changing environments and yet evolved to look very similar to each other.
Another question came from a middle-aged man who has difficulty choosing from a diner menu with so many options. While his kids sift through various choices and come to a decision in a reasonable amount of time, he finds himself poring over the menu before reverting to an item he’s ordered previously. Before he could even finish his question, Glimcher said, “You’re not going to like the answer.” He went on to say it’s possible our brains weren’t built for 60, 70, or 80 years of prime functioning, and it may be a while before our cognitive functions catch up with our increased lifespans.
When I’m a senior citizen, I’ll probably just forget about the B train and take a cab.