When we typically think of how decision-making works in the brain, we think of new input coming in, perhaps through the eyes or ears, being processed in the relevant sensory areas, and then sent to the ‘decision-making’ areas (the basal ganglia, prefrontal cortex, or anterior cingulate cortex) where this information is used to make a decision. Although useful and intuitive, this modular view ends up giving short shrift to some areas that do heavy lifting.
Sensory areas are not actually the ruthless calculating machines that we tend to think of, but are in fact quite plastic. This ability of sensory cortex to modify its own responses allows it to participate in certain decisions: for instance, it can learn how long to wait in order to get a reward. If a rat receives two visual cues that predict how long it will have to wait in order to receive a reward – either a short time or a long time – neurons in the initial part of visual cortex, V1, will maintain a heightened firing rate to match that duration.
This is accomplished through something like reinforcement learning. When learning whether a visual cue is giving an animal information about how long it will have to wait for a reward, acetylcholine acts as a ‘reinforcement signal’. The effect is to change encoding of the reward by modifying the strength of the synapses in the network.
Although we tend to think of certain ‘decision-making’ areas of the brain, in reality all of the brain is participating in every decision at some level or another. In certain cases – perhaps when speed is of the essence or maybe when you want other areas of the brain to be involved in the computations and processing of that decision – even sensory portions of the brain are learning how to make decisions. It is not always dopamine, the ‘rewarding’ or ‘motivational’ chemical in the brain that supports this decision-making: other neuromodulators like acetylcholine often play the very same role.
Chubykin, A., Roach, E., Bear, M., & Shuler, M. (2013). A Cholinergic Mechanism for Reward Timing within Primary Visual Cortex Neuron, 77 (4), 723-735 DOI: 10.1016/j.neuron.2012.12.039
And with this hurricane of digital records, carried along in its wake, comes a simple question: How can we have this much data and still not understand collective human behavior?
There are several issues implicit in a question like this. To begin with, it’s not about having the data, but about the ideas and computational follow-through needed to make use of it—a distinction that seems particularly acute with massive digital records of human behavior. When you personally embed yourself in a group of people to study them, much of your data-collection there will be guided by higher-level structures: hypotheses and theoretical frameworks that suggest which observations are important. When you collect raw digital traces, on the other hand, you enter a world where you’re observing both much more and much less—you see many things that would have escaped your detection in person, but you have much less idea what the individual events mean, and have no a priori framework to guide their interpretation. How do we reconcile such radically different approaches to these questions?
In other words, this strategy of recording everything is conceptually very simple in one sense, but it relies on a complex premise: that we must be able to take the resulting datasets and define richer, higher-level structures that we can build on top of them.
What could a higher-level structure look like? Consider one more example—suppose you have a passion for studying the history of the Battle of Gettysburg, and I offer to provide you with a dataset containing the trajectory of every bullet fired during that engagement, and all the movements and words uttered by every soldier on the battlefield. What would you do with this resource? For example, if you processed the final day of the data, here are three distinct possibilities. First, maybe you would find a cluster of actions, movements, and words that corresponded closely to what we think of as Pickett’s Charge, the ill-fated Confederate assault near the close of the action. Second, maybe you would discover that Pickett’s Charge was too coarse a description of what happened—that there is a more complex but ultimately more useful way to organize what took place on the final day at Gettysburg. Or third, maybe you wouldn’t find anything interesting at all; your analysis might spin its wheels but remain mired in a swamp of data that was recorded at the wrong granularity.
We don’t have that dataset for the Battle of Gettysburg, but for public reaction to the 2012 U.S. Presidential Election, or the 2012 U.S. Christmas shopping season, we have a remarkable level of action-by-action detail. And in such settings, there is an effort underway to try defining what the consequential structures might be, and what the current datasets are missing—for even with their scale, they are missing many important things. It’s a convergence of researchers with backgrounds in computation, applied mathematics, and the social and behavioral sciences, at the start of what is by every indication a very hard problem. We see glimpses of the structures that can be found—Trending Topics on Twitter, for example, is in effect a collection of summary news events induced by computational means from the sheer volume of raw tweets—but a general attack on this question is still in its very early stages.
What is the question about your field that you dread being asked?
(In neuroscience? Anything.)