How to Rekindle Curiosity
When we tailor problems to match our capacity, learning becomes a primal reward
Learning is a primal pleasure, and so is wanting to know. Curiosity correlates tightly with the dopamine brain circuit — the same circuit that fires in response to food, drugs, and sex. The more curious you are, the more those regions light up.
These neurobiological curiosity signals predict not only how much you learn, but what you retain. The more curious you are about something, the likelier you are to recall even incidental details linked to the information at hand. If you’re anticipating a tantalizing piece of gossip, for example, you’re more likely to remember what your source of information was wearing or the expression on their face. The degree of your craving for knowledge controls the strength of your memory.
Curiosity is a propulsive force that pushes animals to act, just like hunger, thirst, the need for security, or the desire to reproduce. But it’s also part of what makes humans, as a species, unique. Whereas other animals merely visit the physical space around them, we explore conceptual worlds. We rejoice, for example, in the symmetry and pure beauty of mathematical patterns. A clever theorem can move us much more than a piece of chocolate. And when we suddenly discover that one of our implicit assumptions is wrong — say, that a road sign for “xing” means “crossing,” and not something that rhymes with “zing” — our brain triggers a response of amusement. We laugh at the absurdity of having been so wrong, but we’re also delighted to be corrected. Studies show that, when all other factors are equal, laughing during learning seems to increase curiosity and enhance subsequent memory.
Several psychologists have tried to specify the algorithm that underlies human curiosity. If we understood it better, we could perhaps gain control over this essential ingredient of our learning scheme, and even reproduce it in a machine that would eventually imitate the performance of the human species: a curious robot.
This algorithmic approach is beginning to bear fruit. Some of the greatest psychologists, from William James to Jean Piaget to Donald Hebb, have speculated on the nature of the mental operations that underlie curiosity. Over time, these various researchers have arrived at a consensus that curiosity is the direct manifestation of children’s motivation to understand the world and build a model of it. In other words, curiosity guides us to what we think we can learn. Its opposite, boredom, turns us away from what we already know, or from areas that, according to our past experience, are unlikely to have anything left to teach us.
This theory explains why curiosity follows a bell curve when it comes to novelty. We have no curiosity for the unsurprising; things that we have seen a thousand times before are boring. But we are also not attracted to things that are too novel or surprising, or so confusing that their structure eludes us. Between the boredom of the too-simple and the repulsion of the too-complex, our curiosity naturally directs us toward fields that are simultaneously new and accessible. Even babies a few months old orient toward stimuli that are neither too simple nor too complex, but whose structure is just right to be quickly learnable — a phenomenon known as the Goldilocks effect.
This vision of curiosity leads to an interesting prediction. It implies that in order for humans to be curious, they must be aware of what they do not yet know. In other words, they must possess metacognitive faculties, even at an early age. Metacognition encompasses everything we know about our own minds, and it plays a key role in curiosity. Indeed, to be curious is to want to know, which implies knowing what you don’t.
Metacognition may also explain why we lose our insatiable desire to know the “why” of just about everything, in early childhood, after a few years of school. As learning progresses, the expected learning gain shrinks: The better we master a field, the more we reach the limits of what it can offer, and the less interested we are in it. To maintain curiosity, schools must therefore continually provide children’s supercomputing brains with stimulants that match their intelligence. This is not always the case. In a standard classroom, the most advanced students often lack stimulation: After a few months, their curiosity fades and they no longer expect much from school, because their metacognitive system has learned that, unfortunately, they are unlikely to learn much more.
At the other end of the spectrum, students who struggle in school may wither away for the opposite reason. Metacognition remains the main culprit; after a while, they no longer have any reason to be curious, because they have learned that they do not succeed in learning. Their past experience has engraved a simple, though false, rule in the depths of their metacognitive circuits: I am incapable of learning math. Such metacognitive judgments are disastrous because they demotivate students and nip their curiosity in the bud.
The solution is the same for both schoolchildren and adults in the workforce: People have to be reminded that they are perfectly capable of learning, provided the problems are adapted to their level, and that learning brings its own reward. The theory of curiosity says that when we are discouraged by work that is either too easy or too challenging, we must restore our desire to learn by breaking down tasks to be both stimulating and achievable. In school and throughout life, tailoring problems to match our capacity rekindles the pleasure of learning something new. From there, our metacognitive system learns that we can learn, which puts curiosity back on track.