Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
Journal
Advances in Neural Information Processing Systems
Journal Volume
2018-December
Pages
2781-2790
Date Issued
2018
Author(s)
Abstract
Understanding how humans and animals learn about statistical regularities in stable and volatile environments, and utilize these regularities to make predictions and decisions, is an important problem in neuroscience and psychology. Using a Bayesian modeling framework, specifically the Dynamic Belief Model (DBM), it has previously been shown that humans tend to make the default assumption that environmental statistics undergo abrupt, unsignaled changes, even when environmental statistics are actually stable. Because exact Bayesian inference in this setting, an example of switching state space models, is computationally intensive, a number of approximately Bayesian and heuristic algorithms have been proposed to account for learning/prediction in the brain. Here, we examine a neurally plausible algorithm, a special case of leaky integration dynamics we denote as EXP (for exponential filtering), that is significantly simpler than all previously suggested algorithms except for the delta-learning rule, and which far outperforms the delta rule in approximating Bayesian prediction performance. We derive the theoretical relationship between DBM and EXP, and show that EXP gains computational efficiency by foregoing the representation of inferential uncertainty (as does the delta rule), but that it nevertheless achieves near-Bayesian performance due to its ability to incorporate a "persistent prior" influence unique to DBM and absent from the other algorithms. Furthermore, we show that EXP is comparable to DBM but better than all other models in reproducing human behavior in a visual search task, suggesting that human learning and prediction also incorporates an element of persistent prior. More broadly, our work demonstrates that when observations are information-poor, detecting changes or modulating the learning rate is both difficult and thus unnecessary for making Bayes-optimal predictions. © 2018 Curran Associates Inc.All rights reserved.
Other Subjects
Bayesian networks; Behavioral research; Computational efficiency; Forecasting; Heuristic algorithms; Inference engines; State space methods; Bayesian inference; Bayesian performance; Bayesian predictions; Environmental statistics; Human behaviors; State - space models; Statistical regularity; Volatile environments; Learning algorithms
Type
conference paper