The Smarter Brain Burns Less Energy, Not More

Over half an hour, a group of people were injected with a small dose of radioactive glucose and asked to solve reasoning problems. The better they did, the less their brain lit up on the scan.

It isn’t a typo.

It isn’t that higher-scoring people were “trying less hard” in the lazy sense of the phrase.

It’s that their brains needed less fuel to reach exactly the same place.

The experiment: radioactive glucose and a matrix test

Psychologist Arthur Jensen documents this work in The g Factor (1998), his synthesis of the science behind the general factor of intelligence (g). The experiment in question uses a technique called PET (positron emission tomography):

• A small amount of a radioactive glucose isotope is injected into the bloodstream.
• The person spends about 35 minutes solving a nonverbal reasoning test, the Raven’s Progressive Matrices, one of the most heavily g-loaded tests that exists.
• The scanner detects which brain regions metabolized the most glucose, in other words, where the most neural energy was spent.

Researcher Richard Haier, a leading figure in this field, found something that breaks intuition at first glance: the amount of glucose consumed during the test correlates negatively with the score obtained, with values between -0.7 and -0.8. The higher the score, the less energy spent solving the same kind of problem.

Why “more horsepower” is the wrong metaphor

It’s tempting to picture a smart brain as a bigger engine that burns more fuel and therefore performs better.

The data points in exactly the opposite direction.

• A more efficient brain seems to need less metabolic activity to reach the same correct result.
• That inverse relationship shows up across most of the cerebral cortex, though it’s more pronounced in the temporal regions.
• Jensen sums it up with a simple idea: g appears to be related to the efficiency of neural processing, not its volume.

Put another way: it isn’t that a more capable brain works harder. It’s that it gets more done for every calorie it spends.

The experiment that set it in motion: learning to play Tetris

The most interesting part isn’t a single snapshot, it’s what happens when someone gets better at something.

Haier and his team repeated the PET scan with a group of people while they learned to play Tetris, a video game that demands fast information processing, hand-eye coordination, and on-the-fly planning.

• In the first rounds, playing Tetris burned a relatively high amount of brain glucose.
• After practicing 30 to 45 minutes a day for 30 to 60 days, skill improved noticeably.
• At the same time, brain glucose consumption during the game dropped, and the correlation between that metabolic activity and the g factor weakened.

There’s a detail worth not missing: the rate at which that efficiency improved was itself correlated with scores on the Raven’s test. People who started with a higher IQ didn’t just improve more at the game, they also gained more energy efficiency doing it.

A maturing brain also gets cheaper to run

This same pattern, according to Jensen, also shows up in normal development in anyone.

PET studies across different ages show that brain glucose consumption decreases progressively from childhood into adulthood, right as mental capacity keeps growing.

One plausible explanation the book discusses is so-called neural pruning: the spontaneous reduction, especially in the first years of life, in the number of synaptic connections that aren’t useful. Fewer redundant connections, cleaner processing, less wasted energy.

And here’s a detail worth sitting with twice: insufficient pruning during early development is associated with certain types of intellectual disability. Having fewer active neurons, at the right point in development, isn’t always a loss. Sometimes it’s the necessary condition for the rest of the system to work better.

What this finding changes (and what it doesn’t)

None of this turns brain efficiency into something you can measure at home, or Tetris practice into a shortcut to a higher IQ.

• The energy cost of solving a problem and the capacity to solve it are two different things, related here, not the same.
• Practice reduces the energy cost of any task that becomes automatic, in anyone, though the starting point varies.
• Neural efficiency, as Jensen describes it drawing on Haier’s work, appears to be one of the most consistent biological correlates of the g factor, alongside others that have nothing to do with conscious effort.

What this finding does leave behind is a rather different picture of what “a powerful brain” means: not the one that works the hardest, but the one that needs to work the least to get to the same place.

If this same idea from another angle of the brain interests you, what your brain decides in a fifth of a second explains how even the simplest reaction speed, measured in milliseconds, relates to this same variable.

Sources

• Jensen, A. R. (1998). The g Factor: The Science of Mental Ability. Praeger.
• Haier, R. J. (1993). Cerebral glucose metabolism and intelligence. In P. A. Vernon (Ed.), Biological Approaches to the Study of Human Intelligence (pp. 317-373). Ablex.
• Haier, R. J., Siegel, B., Tang, C., Abel, L., & Buchsbaum, M. S. (1992). Intelligence and changes in regional cerebral glucose metabolic rate following learning. Intelligence, 16, 415-426.