Computational learning noise in human decision-making.

Summary In uncertain and changing environments, making decisions requires the analysis and weighting of past and present information. To model human behavior in such environments, computational approaches to learning have been developed based on reinforcement learning or Bayesian inference. In order to better account for behavioral variability, these approaches assume noise in the selection of the action. In the first part of my work, I argue that the noise in the action selection is insufficient to explain the behavioral variability and I show the presence of learning noise reflecting computational inaccuracies. To this end, I introduce noise into the learning algorithm by allowing for random deviations from the noise-free update rule. The addition of this noise provides a better explanation of human behavioral performance (Findling C., Skvortsova V., et al., 2018a, in preparation). In the second part of my work, I show that this noise has virtuous adaptive properties in learning processes elicited in changing (volatile) environments. Using the Bayesian modeling framework, I show that a simple learning model, assuming stable external contingencies, but with noise in the learning, performs as well as the optimal Bayesian model that infers the volatility of the environment. Furthermore, I establish that this noise model better explains human behavior in changing environments (Findling C. et al., 2018b, in preparation).