Increase in risk-taking behavior triggered by unexpected victories in humans and monkeys

How do humans make decisions when outcomes are uncertain? One possible method is to calculate the expected value of each option by multiplying each possible outcome amount by the probability and then choosing the option with the highest expected value. While this strategy will maximize return in anticipation, that is not what people tend to do. In particular, people seem to be irrationally influenced by the past outcomes of their decisions when making later choices.

Researchers from the University of Tsukuba have developed and validated a model (“dynamic probability theory”) that integrates the most popular model in behavioral economics for describing decision-making under uncertainty — probability theory, and a well-established paradigm for learning from neuroscience — reinforcement learning theory. This model accurately describes the decisions that people and monkeys make while facing risks from probability theory or reinforcement learning theory alone.

Specifically, the researchers asked 70 people to repeatedly choose between two lotteries in which they could win some reward with some odds. Lotteries differed in the size of the reward, the probability of obtaining it, and the amount of risk involved. The results showed that immediately after experiencing an outcome greater than the expected value of the given option, the participants acted as if the probability of winning the next lottery increased.

“This behavior is surprising because the odds of winning were clearly described to the participants (the participants did not have to learn it from experience) and these odds were also completely independent of previous outcomes,” says study senior author Associate Professor Hiroshi Yamada. Using a dynamic probability theory model, the researchers were able to determine that change in behavior is driven by a change in the perception of probabilities rather than by a change in the evaluation of rewards.

Yamada also says, “Such learning from unexpected events is the basis of reinforcement learning theory and is a well-known algorithm that occurs when people need to learn rewards from experience. Interestingly, it occurs even if the learning is not necessary.”

In similar experiments with macaques, whose brains are similar to humans, essentially the same results were observed. The researchers commented that the similarity in human and monkey behavior was remarkable in this study.

Based on the results of this research, it is expected that the investigation of the monkey brain will lead to an understanding of the brain mechanisms involved in the perception of rewards and possibilities that we all use when making risky decisions, as well as the joy we feel when we succeed.

The paper has been published in the journal Science advances.