A model for learning based on the joint estimation of stochasticity and volatility

Abstract

Previous research has stressed the importance of uncertainty for controlling the speed of learning, and of how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a new learning model, in which both factors are learned simultaneously from experience. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence both complicates and enriches the interpretation of previous results, such as those thought to implicate volatility in psychiatric conditions. This provides a novel mechanism for understanding pathological learning in amygdala damage and anxiety disorders.