The Expectation Effect is a concept in psychology that suggests we get the results in life that we have come to expect. Sadly, many of us – myself once included – have developed a belief that we are “no good” at math. If you are one of those people, it may come as a surprise that this self-belief is almost certainly false, because you are hard-wired for math and you are making highly complex probabilistic calculations all the time. You may have a Bayesian brain.

Introducing Thomas Bayes

A contemporary of Isaac Newton and a defender of Newton’s invention of calculus, Thomas Bayes was a theologian and mathematician (those two fields were not as far apart in his time as they have drifted today) who is best known for his invention of probabilistic prediction. Basically, Bayesian theory states that you can predict the likelihood of future outcomes based on the frequency of events in the past. As new data becomes available, it must be employed to refine the prediction. In many applications, this approach amounts to generating a model, or approximation of a statement of truth.

Today, his work is used to predict a lot about our world, including the weather, politics, astrophysics and criminal justice. Google Research is studying how to use Bayesian analysis to predict the impact of ads on marketing and sales. Machine learning applies the theorem to provide part of the foundation for how a computer or software program can be said to learn, by continuously updating predictions based on new data.

So what does all this math have to do with the brain? A lot, as it turns out.

Hermann von Helmholtz theorized that the brain takes a Bayesian approach to understanding the world, internally interpreting (some would say “constructing“) a model of the world that is constantly tested and revised based on experience. While Helmholtz was operating in the field of educational psychology, many neuroscientists have co-opted his theory because it helps them predict the performance of neurons when the brain is performing tasks such as making a decision or recognizing patterns. In other words, they are using Bayesian analysis to predict the likelihood that the brain is using Bayesian analysis at the cellular level to interpret our world. This insight is being used to make great progress in the field of artificial intelligence, where a computer can learn how to perform in ways that seem remarkably “human” through the application of pattern recognition and predictive programs that continually refine parameters based on incoming data.

If all that gives you a little bit of a math-induced “brain freeze,” check out rapper Baba Brinkman’s work on the predictive brain and you’ll get the idea.

The Bayesian Debate

But all this is a little too simple for some neuroscientists. They point out that the ability to mimic human behavior is not the same as understanding it and are concerned that over-application of Bayesian principles to understanding the brain may be a dangerous case of reductionism. It seems to work extremely well to explain some of our unconscious learning processes, but there are serious gaps when we try to apply it to conscious thought. For example, in cases where we have a strong emotional connection to the outcome, we often reject new data and continue to use a model of the world that is no longer valid – even when it is less useful than a revised model would be. Take, for example, a survey of the UK voters who voted in favor of leaving the European Union, based on their beliefs that “Brexit” would benefit their country financially. Although the incoming data since (and during) the election strongly indicates that Brexit is having very negative consequences for the UK, most voters continue to hold to their original position. If your brain always operated as a “Bayesian,” wouldn’t we be seeing a change of heart now that the effects of self-imposed isolation are being felt?

So your neurons may be behaving in a highly logical manner, but the combined effect of all those neuron spikes, when articulated into conscious thought, may not.

What are we to make of all this? I’m not sure, but that’s OK. Even folks who study the brain for a living haven’t really decided yet. I guess you’ll just have to add take this information and make the best prediction that you can, for now.