3. What does the baseline activity in the brain represent?
Neuroscientists have mostly studied changes in brain activity that correlate with stimuli we can present in the laboratory, such as a picture, a touch, or a sound. But the activity of the brain at rest—its “baseline” activity—may prove to be the most important aspect of our mental lives. The awake, resting brain uses 20 percent of the body’s total oxygen, even though it makes up only 2 percent of the body’s mass. Some of the baseline activity may represent the brain restructuring knowledge in the background, simulating future states and events, or manipulating memories. Most things we care about—reminiscences, emotions, drives, plans, and so on—can occur with no external stimulus and no overt output that can be measured.
One clue about baseline activity comes from neuroimaging experiments, which show that activity decreases in some brain areas just before a person performs a goal-directed task. The areas that decrease are the same regardless of the details of the task, hinting that these areas may run baseline programs during downtime, much as your computer might run a disk-defragmenting program only while the resources are not needed elsewhere.
In the traditional view of perception, information from the outside world pours into the senses, works its way through the brain, and makes itself consciously seen, heard, and felt. But many scientists are coming to think that sensory input may merely revise ongoing internal activity in the brain. Note, for example, that sensory input is superfluous for perception: When your eyes are closed during dreaming, you still enjoy rich visual experience. The awake state may be essentially the same as the dreaming state, only partially anchored by external stimuli. In this view, your conscious life is an awake dream.
The awake state may be essentially the same as the dreaming state. In this view, your conscious life is an awake dream.
4. How do brains simulate the future?
When a fire chief encounters a new blaze, he quickly makes predictions about how to best position his men. Running such simulations of the future—without the risk and expense of actually attempting them—allows “our hypotheses to die in our stead,” as philosopher Karl Popper put it. For this reason, the emulation of possible futures is one of the key businesses that intelligent brains invest in.
Yet we know little about how the brain’s future simulator works because traditional neuroscience technologies are best suited for correlating brain activity with explicit behaviors, not mental emulations. One idea suggests that the brain’s resources are devoted not only to processing stimuli and reacting to them (watching a ball come at you) but also to constructing an internal model of that outside world and extracting rules for how things tend to behave (knowing how balls move through the air). Internal models may play a role not only in motor acts, like catching, but also in perception. For example, vision draws on significant amounts of information in the brain, not just on input from the retina. Many neuroscientists have suggested over the past few decades that perception arises not simply by building up bits of data through a hierarchy but rather by matching incoming sensory data against internally generated expectations.
But how does a system learn to make good predictions about the world? It may be that memory exists only for this purpose. This is not a new idea: Two millennia ago, Aristotle and Galen emphasized memory as a tool in making successful predictions for the future. Even your memories about your life may come to be understood as a special subtype of emulation, one that is pinned down and thus likely to flow in a certain direction.
5. What are emotions?
We often talk about brains as information-processing systems, but any account of the brain that lacks an account of emotions, motivations, fears, and hopes is incomplete. Emotions are measurable physical responses to salient stimuli: the increased heartbeat and perspiration that accompany fear, the freezing response of a rat in the presence of a cat, or the extra muscle tension that accompanies anger. Feelings, on the other hand, are the subjective experiences that sometimes accompany these processes: the sensations of happiness, envy, sadness, and so on. Emotions seem to employ largely unconscious machinery—for example, brain areas involved in emotion will respond to angry faces that are briefly presented and then rapidly masked, even when subjects are unaware of having seen the face. Across cultures the expression of basic emotions is remarkably similar, and as Darwin observed, it is also similar across all mammals. There are even strong similarities in physiological responses among humans, reptiles, and birds when showing fear, anger, or parental love.
Modern views propose that emotions are brain states that quickly assign value to outcomes and provide a simple plan of action. Thus, emotion can be viewed as a type of computation, a rapid, automatic summary that initiates appropriate actions. When a bear is galloping toward you, the rising fear directs your brain to do the right things (determining an escape route) instead of all the other things it could be doing (rounding out your grocery list). When it comes to perception, you can spot an object more quickly if it is, say, a spider rather than a roll of tape. In the realm of memory, emotional events are laid down differently by a parallel memory system involving a brain area called the amygdala.
One goal of emotional neuroscience is to understand the nature of the many disorders of emotion, depression being the most common and costly. Impulsive aggression and violence are also thought to be consequences of faulty emotion regulation.
6. What is intelligence?
Intelligence comes in many forms, but it is not known what intelligence—in any of its guises—means biologically. How do billions of neurons work together to manipulate knowledge, simulate novel situations, and erase inconsequential information? What happens when two concepts “fit” together and you suddenly see a solution to a problem? What happens in your brain when it suddenly dawns on you that the killer in the movie is actually the unsuspected wife? Do intelligent people store knowledge in a way that is more distilled, more varied, or more easily retrievable?
We all grew up with the near-future promise of smart robots, but today we have little better than the Roomba robotic vacuum cleaner. What went wrong? There are two camps for explaining the weak performance of artificial intelligence: Either we do not know enough of the fundamental principles of brain function, or we have not simulated enough neurons working together. If the latter is true, that’s good news: Computation gets cheaper and faster each year, so we should not be far from enjoying life with Asimovian robots who can effectively tend our households. Yet most neuroscientists recognize how distant we are from that dream. Currently, our robots are little more intelligent than sea slugs, and even after decades of clever research, they can barely distinguish figures from a background at the skill level of an infant.
Recent experiments explore the possible relationship of intelligence to the capacity of short-term memory, the ability to quickly resolve cognitive conflict, or the ability to store stronger associations between facts; the results are not yet conclusive. Many other possibilities—better restructuring of stored information, more parallel processing, or superior emulation of possible futures—have not yet been probed by experiments.
Intelligence may not be underpinned by a single mechanism or a single neural area. Whatever intelligence is, it lies at the heart of what is special about Homo sapiens. Other species are hardwired to solve particular problems, while our ability to abstract allows us to solve an open-ended series of problems. This means that studies of intelligence in mice and monkeys may be barking up the wrong family tree.
