How the brain makes decisions [news release]
How the brain makes decisions.
From the 25 May 2015 MedicalExpress news release
Some types of decision-making have proven to be very difficult to simulate, limiting progress in the development of computer models of the brain. EPFL scientists have developed a new model of complex decision-making, and have validated it against humans and cutting-edge computer models, uncovering fascinating information about what influences our decision-making and ability to learn from it.
Decision-making comes in two major into two types: Markovian and non-Markovian, named after the mathematician Andrey Markov (1856-1922). Simply put, in Markovian decision-making, the next decision step depends entirely on the current state of affairs. For example, when playing backgammon, the next move depends only on the current layout of the board, and not on how it got to be like that. This relatively straightforward process has been extensively modeled in computers and machines.
Non-Markovian decision-making is more complex. Here, the next step is affected by other factors, such as external constraints and previous decisions. For example, a person’s goal might be to travel on the train. But what will happens when he arrives at the door to the train depends on whether or not he has previously visited the ticket booth to buy a ticket. In other words, the next step depends on how he got there; without a ticket, he cannot proceed to the desired goal. In neuroscience, the “buy-ticket” step is referred to as a “switch-state”.
The results of the study drew three major conclusions. First, that human decision-making can perform just as well as current sophisticated computer models under non-Markovian conditions, such as the presence of a switch-state. This is a significant finding in our current efforts to model the human brain and develop artificial intelligence systems.
Secondly, that delayed feedback significantly impairs human decision-making and learning, even though it does not impact the performance of computer models, which have perfect memory. In the second experiment, it took human participants ten times more attempts to correctly recall and assign arrows to icons. Feedback is a crucial element of decision-making and learning. We set a goal, make a decision about how to achieve it, act accordingly, and then find out whether or not our goal was met. In some cases, e.g. learning to ride a bike, feedback on every decision we make for balancing, pedaling, braking etc. is instant: either we stay up and going, or we fall down. But in many other cases, such as playing backgammon, feedback is significantly delayed; it can take a while to find out if each move has led us to victory or not.
Finally, the researchers found that the spiking neurons model matches and describes human performance very well. The significance of this cannot be overstated, as non-Markovian decision-making has proven to be very challenging for computer models. “This is a proof-of-concept study,” says Michael Herzog. “But the study makes an important contribution toward understanding, and accurately modeling, the human brain – and even surpassing its abilities with artificial intelligence.”
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