Human and machine learning in non-Markovian decision making

• Published in: PloS one Vol. 10; no. 4; p. e0123105
• Main Authors: Clarke, Aaron Michael, Friedrich, Johannes, Tartaglia, Elisa M, Marchesotti, Silvia, Senn, Walter, Herzog, Michael H
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.04.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Bayes Theorem; Bayesian analysis; Computational neuroscience; Computer Simulation; Decision Making; Decision Support Techniques; Dopamine; Experiments; Feedback; Firing pattern; Formative Feedback; Human performance; Humans; Learning algorithms; Machine learning; Mapping; Markov Chains; Models, Neurological; Neural Networks (Computer); Neurons; Neurosciences; Physiology; Spiking; Upper bounds; New York; France; United States > US; Switzerland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0123105

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little is known about non-Markovian decision making, where the next state depends on more than the current state and action. Learning is non-Markovian, for example, when there is no unique mapping between actions and feedback. We have produced a model based on spiking neurons that can handle these non-Markovian conditions by performing policy gradient descent [1]. Here, we examine the model's performance and compare it with human learning and a Bayes optimal reference, which provides an upper-bound on performance. We find that in all cases, our population of spiking neurons model well-describes human performance.