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Related Experiment Video

Updated: Jun 16, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Hyperbolically discounted temporal difference learning.

William H Alexander¹, Joshua W Brown

¹Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA. wialexan@indiana.edu

Neural Computation

|January 27, 2010

Summary

This summary is machine-generated.

This study introduces hyperbolically discounted temporal difference (HDTD) learning, a new recursive algorithm. It models hyperbolic discounting observed in animal and neural choice behavior, unlike traditional exponential models.

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Area of Science:

Neuroscience
Behavioral Economics
Machine Learning

Background:

Hyperbolic discounting, where future rewards are valued less than immediate ones, is prevalent in animal and neural choice behavior.
Current dominant temporal discounting models, like temporal difference learning, use exponential discounting, which is mathematically recursive.
Hyperbolic discounting has lacked a recursive formulation, hindering its integration into computational models.

Purpose of the Study:

To develop a recursive computational model for hyperbolic discounting.
To bridge the gap between observed hyperbolic discounting behavior and existing exponential discounting models.
To introduce a novel learning algorithm that captures hyperbolic temporal discounting.

Main Methods:

Defined a new learning algorithm termed hyperbolically discounted temporal difference (HDTD) learning.
Formulated a recursive definition for hyperbolic discounting.
Presented HDTD learning as a recursive formulation of the hyperbolic discounting model.

Main Results:

Successfully developed a recursive algorithm (HDTD learning) for hyperbolic discounting.
Provided a mathematical framework that reconciles hyperbolic discounting with recursive learning principles.
Established a computational model that aligns with observed choice behaviors in animals and neural systems.

Conclusions:

Hyperbolic discounting can be recursively formulated, challenging previous assumptions.
The proposed HDTD learning algorithm offers a viable computational approach for modeling hyperbolic temporal discounting.
This work integrates behavioral observations with computational neuroscience and machine learning models.