Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Decision Making: P-value Method01:09

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 have a...
Decision Making: Traditional Method01:14

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 Making01:20

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...
Reason and Intuition01:37

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 brain can only use...
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
Timing and Consequences on Behavior01:08

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 factor...

You might also read

Related Articles

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

Sort by
Same author

Just how goal-directed are hippocampal theta sweeps, anyway?

Nature neuroscience·2026
Same author

Coordinated Representational Drift Across the Mouse Cortex.

bioRxiv : the preprint server for biology·2026
Same author

Hippocampal representations differentiate reactive and anticipatory responses during foraging under threat.

bioRxiv : the preprint server for biology·2026
Same author

Technological <i>folie à deux</i>: feedback loops between AI chatbots and mental health.

Nature. Mental health·2026
Same author

Functional reorganization of motor cortex connectivity during learning.

bioRxiv : the preprint server for biology·2026
Same author

Reasoning with programs in replay.

bioRxiv : the preprint server for biology·2025
Same journal

Assessing circuit function in the developing <i>Xenopus</i> tadpole: a survey of the behavioral toolkit and underlying neural substrates.

Frontiers in behavioral neuroscience·2026
Same journal

Dawn of the dread: threatening cinematic virtual reality environments enhance general but not specific pavlovian-instrumental transfer.

Frontiers in behavioral neuroscience·2026
Same journal

Transcranial alternating current stimulation improves cognitive functions in healthy subjects through modifying frontoparietal and dorsal attention networks based on personalized individual theta frequency analysis.

Frontiers in behavioral neuroscience·2026
Same journal

Functional loss of PKMζ in the dorsal hippocampus potentiates the time-dependent increase in false contextual fear memory and impairs spatial recognition memory in mice.

Frontiers in behavioral neuroscience·2026
Same journal

Distinct orbitofrontal circuits with dorsal and ventral CA1 differentially regulate spatial memory and emotional behaviors.

Frontiers in behavioral neuroscience·2026
Same journal

Towards a neurophysiological model of kundalini: a theoretical framework informed by preliminary clinical observations.

Frontiers in behavioral neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

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

Published on: September 10, 2018

A reinforcement learning model of precommitment in decision making.

Zeb Kurth-Nelson1, A David Redish

  • 1Department of Neuroscience, University of Minnesota Minneapolis, MN, USA.

Frontiers in Behavioral Neuroscience
|December 24, 2010
PubMed
Summary
This summary is machine-generated.

Precommitment, a strategy to overcome impulsivity by choosing future rewards, is explained by a distributed hyperbolic discounting model. This model predicts precommitment likelihood depends on reward ratios and discount curve shape.

Keywords:
addictiondecision makingdelay discountinghyperbolic discountingimpulsivityprecommitmentreinforcement learning

More Related Videos

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

Related Experiment Videos

Last Updated: Jun 5, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

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

Published on: September 10, 2018

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
05:48

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

Area of Science:

  • Behavioral Economics
  • Neuroscience
  • Decision Science

Background:

  • Impulsivity, characterized by preferring immediate suboptimal choices, is linked to various disorders.
  • Precommitment offers a strategy to mitigate impulsivity by restricting future choices.

Purpose of the Study:

  • To investigate the theoretical underpinnings of precommitment behavior.
  • To identify a computational model that accurately predicts precommitment in decision-making.

Main Methods:

  • The study employed a distributed model of hyperbolic discounting.
  • Mathematical modeling was used to analyze precommitment decisions under varying conditions.

Main Results:

  • Most hyperbolic discounting models fail to account for precommitment.
  • A distributed hyperbolic discounting model successfully predicts precommitment.
  • Precommitment probability is sensitive to precommitment delay, reward ratios, and discount curve shape.

Conclusions:

  • The distributed hyperbolic discounting model provides a framework for understanding precommitment.
  • Factors like diet and context, which alter discount curves, may influence precommitment behavior.