Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

Reason and Intuition

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...

Decision Making: Traditional Method

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...

Decision Making

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

Timing and Consequences on Behavior

Timing and Consequences on Behavior

In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective.
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...

Self-Discrepancy Theory

Self-Discrepancy Theory

One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Bridging Computation and Representation in Associative Learning.

Computational brain & behavior·2026

Same author

Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks.

Nature communications·2026

Same author

Human-level learning of complex novel tasks as theory-based modelling, exploration and planning.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026

Same author

Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules.

Advances in neural information processing systems·2026

Same author

Probabilistic forecasting guides dynamic decisions.

Psychological review·2026

Same author

Phasic dopamine drives conditioned responding beyond its role in learning.

bioRxiv : the preprint server for biology·2026

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

Human decision making balances reward maximization and policy compression.

Lucy Lai^1,2, Samuel J Gershman³

¹Program in Neuroscience, Harvard University, Cambridge, Massachusetts, United States of America.

Plos Computational Biology

|April 26, 2024

Summary

Humans simplify decision-making by compressing policies, trading reward for cognitive ease. This behavior aligns with capacity-limited reinforcement learning, showing bias towards probable actions under memory and time constraints.

More Related Videos

Studying Food Reward and Motivation in Humans

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

Errors as a Means of Reducing Impulsive Food Choice

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

Related Experiment Videos

Last Updated: Jun 27, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

Studying Food Reward and Motivation in Humans

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

Errors as a Means of Reducing Impulsive Food Choice

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

Area of Science:

Cognitive Science
Computational Neuroscience
Reinforcement Learning

Background:

Policy compression is a framework explaining how limited-capacity agents simplify actions to reduce cognitive load.
Understanding human decision-making under constraints is crucial for cognitive modeling.

Purpose of the Study:

To provide behavioral evidence for policy compression in humans.
To test predictions of a capacity-limited reinforcement learning model against human behavior.
To investigate how cognitive load and time pressure affect policy simplification.

Main Methods:

Behavioral experiments across various tasks.
Analysis of human policy choices in relation to state-action-reward structures.
Comparison of human data with capacity-limited reinforcement learning models.

Main Results:

Humans exhibit policy compression, favoring simpler action policies.
Policy compression involves a bias towards high-probability actions, discarding state information.
This bias increases with reward policy redundancy and memory load.
Time pressure further reduces policy complexity and increases action bias.

Conclusions:

Human decision-making under cognitive constraints is accurately described by policy compression.
Cognitive resource limitations drive the adoption of simplified, albeit potentially suboptimal, strategies.
Findings support a model where actions are decoded from a compressed code under time pressure.