Estimating internal variables of a decision maker's brain: A model-based approach for neuroscience

Abstract

A major problem in search of neural substrates of learning and decision making is that the process is highly stochastic and subject dependent, making simple stimulus- or output-triggered averaging inadequate. This paper presents a novel approach of characterizing neural recording or brain imaging data in reference to the internal variables of learning models (such as connection weights and parameters of learning) estimated from the history of external variables by Bayesian inference framework. We specifically focus on reinforcement leaning (RL) models of decision making and derive an estimation method for the variables by particle filtering, a recent method of dynamic Bayesian inference. We present the results of its application to decision making experiment in monkeys and humans. The framework is applicable to wide range of behavioral data analysis and diagnosis. © 2008 Springer-Verlag Berlin Heidelberg.