Simulating future value in intertemporal choice.

ABSTRACT: The laboratory study of how humans and other animals trade-off value and time has a long and storied history, and is the subject of a vast literature. However, despite a long history of study, there is no agreed upon mechanistic explanation of how intertemporal choice preferences arise. Several theorists have recently proposed model-based reinforcement learning as a candidate framework. This framework describes a suite of algorithms by which a model of the environment, in the form of a state transition function and reward function, can be converted on-line into a decision. The state transition function allows the model-based system to make decisions based on projected future states, while the reward function assigns value to each state, together capturing the necessary components for succe

SUBMITTER: Solway A 

PROVIDER: S-EPMC5320483 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature