The theory of quantitative cognition explains why humans make stupid decisions.

Scientists at the University of Science and Technology in China have an answer to the ultimate question based on quantum physics: if humans are so smart, why do we all make so many stupid choices?

Psychologists and casinos have invested incalculable money trying to figure out why human beings do not always make the right choices, even when the results are clear. Theoretically, we are all capable of making easy, intelligent choices–but who in their lives hasn't made a regrettable decision, or two? Why aren't people perfect?
The answer is uncertainty, as in the type of uncertainty which drives theories of quantum mechanics. The researchers implemented a problem-solving method called Quantum Reinforcement Learning (QRL), based on a methodology used both in psychology and in the development of artificial intelligence called Classical Reinforcement Learning (CRL).
Classic reinforcement learning is a simple concept that involves punishment and reward. Whether you are teaching a baby or a robot, the idea is to reward him for completing a task effectively and punish him (by withdrawing the reward) for the opposite. The concept, when applied to cognition, is to make all our decisions on the basis of presumed reward versus punishment.

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Quantum reinforcement learning involves the interaction of a quantum agent with a classical environment to adjust its parameters through reward or punishment. Essentially the amplitudes of probabilities (the big-brain math parameters used by scientists to measure the probability of something happening) used in QRL are based on quantum physics and not on binary, classical physics. This should in theory make any predictions made using QRL more robust than those made using CRL. That's, apparently, because these models account for the uncertainty that exists in the quantum universe— you can never really predict the outcome of an event at the quantum level.
However, the researchers needed a method to prove that this was the case by which they could verify that the QRL models outperformed the CRL models in measuring actual human behaviour. To this end, the scientists solicited test subjects who submitted to fMRI scans while playing a game called the "Iowa Gambling Task." The Iowa Gambling Task was developed to determine how easily participants learned from their mistakes. The gist of this is that users will be faced with four decks of cards and asked to draw a card from any deck they choose, depending on the value of the drawn card they will lose or win "money" in the game.
Some of the decks are "evil" and will drain money from the players if they keep drawing from them and others are "good" and pay off if the players continue drawing.

All things being equal, when a deck isn't panning out, most players will catch on pretty soon. Most players tend to draw a few cards from each deck over the course of different studies throughout the years, until they land on one that pans out. However, as many studies have shown, including the one referred to in this article, participants with a history of addiction or abnormal brain activity tend more often to make the "wrong" decisions.
This knowledge has been leveraged by scientists at the University of Science and Technology in China to make it easier to determine if QRL-based probability models have advantages over CRL by comparing the results and brain scans of both "good" test-takers and those showing signs of addiction, i.e.: cigarette smokers.

Compared to "healthy" individuals, the cigarette smokers underperformed at the Iowa Gambling Task but, astonishingly, QRL models predicted results better than CRL models for both groups.

This would seem to support the idea that "quantum cognition" describes human behavior and decision making better than models of binary probability.And if the best way to measure and predict human behavior is to use models of quantum learning, then it makes sense for human cognition to be quantum itself.

Perhaps the only way human memory, intellect, and consciousness can be explained is through the lens of quantum mechanics.

LiveScience writer Nicoletta Lanese, writing about the same research, says: Quantum mechanics often recognizes that people's beliefs about the outcome of a given decision— whether it's good or bad — frequently reflect what ends up being their final choice. In this way, people's beliefs interact with their eventual actions, or become "entangled."

To the best of our knowledge, this work represents the first formal study in human decision-making which investigates physiological evidence for the theory of quantum reinforcement.

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