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

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
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...
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...
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...
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...

You might also read

Related Articles

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

Sort by
Same author

Oxytocin modulates the neurocomputational mechanisms engaged in learning rank relationships in social networks.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Learning to adaptively cooperate through social interactions during childhood and adolescence.

NPJ science of learning·2026
Same author

Intention-Outcome Trade-Off in Moral Character Learning.

Annals of the New York Academy of Sciences·2026
Same author

Neurocomputational mechanisms of adaptive mentalization in humans.

Trends in cognitive sciences·2026
Same author

Neurocomputational mechanisms underlying how social status affects learning of trust behavior.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

Toward a computational understanding of bribe-taking behavior.

Annals of the New York Academy of Sciences·2025

Related Experiment Video

Updated: May 15, 2026

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

Neural coding of computational factors affecting decision making.

Jean-Claude Dreher1

  • 1Reward and decision making group, Cognitive Neuroscience Center, CNRS, Lyon 1 University, Lyon, France. dreher@isc.cnrs.fr

Progress in Brain Research
|January 16, 2013
PubMed
Summary
This summary is machine-generated.

This study reveals how the brain processes rewards, identifying distinct neural signals for prediction errors and uncertainty. Genetic factors also influence reward processing and behavior.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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

Related Experiment Videos

Last Updated: May 15, 2026

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

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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

Area of Science:

  • Neuroscience
  • Decision Science
  • Computational Psychiatry

Background:

  • Value-based decision-making involves complex computations of reward magnitude, probability, delay, effort, and uncertainty.
  • Understanding the neural coding of these computational factors is crucial for deciphering brain function in decision-making.

Purpose of the Study:

  • To investigate how neural signals encode computational factors influencing reward processing.
  • To explore the brain networks involved in representing primary and secondary rewards.
  • To examine the role of genetic variations in dopamine pathways on reward system function.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) in healthy humans.
  • Intracranial recordings in patients with epilepsy.
  • Analysis of brain signals modulated by reward parameters (magnitude, probability, delay, effort, uncertainty).

Main Results:

  • Prediction error (PE), salient PE, and uncertainty signals are computed in overlapping brain circuits.
  • Both transient and sustained uncertainty signals are present in the brain.
  • A common reward network (ventromedial prefrontal cortex, ventral striatum) and orbitofrontal cortex organization by reward type were identified.
  • Separate valuation systems exist for delay and effort costs.
  • Dopamine-related gene variations impact reward system response and individual differences in behavior.

Conclusions:

  • The brain employs distinct yet overlapping neural circuits for processing reward-related information, including prediction errors and uncertainty.
  • Reward valuation is modulated by specific computational factors like delay and effort, with genetic influences on individual differences.
  • These findings offer insights into reward processing, behavior, and predisposition to neuropsychiatric disorders.