Originally published on May 25, 2015
Here are two excerpts:
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.
The entire article is here.
Clarke AM, Friedrich J, Tartaglia EM, Marchesotti S, Senn W, Herzog MH. Human and Machine Learning in Non-Markovian Decision Making. PLoS One 21 April 2015.