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 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...
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 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...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

You might also read

Related Articles

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

Sort by
Same author

Stimulus-to-stimulus learning in RNNs with cortical inductive biases.

PLoS computational biology·2025
Same author

Peripheral Visual Information Halves Attentional Choice Biases.

Psychological science·2023
Same author

Fixation patterns in simple choice reflect optimal information sampling.

PLoS computational biology·2021
Same author

Neuroanatomy of the vmPFC and dlPFC Predicts Individual Differences in Cognitive Regulation During Dietary Self-Control Across Regulation Strategies.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2018
Same author

Addiction and Cue-Triggered Decision Processes.

The American economic review·2017
Same author

The Attentional Drift Diffusion Model of Simple Perceptual Decision-Making.

Frontiers in neuroscience·2017
Same journal

Cichlid fish as a model for understanding social dysfunction.

Current opinion in neurobiology·2026
Same journal

On aims and methods in field neuroethology: Investigating neural mechanisms of behavior in semi-natural and natural contexts.

Current opinion in neurobiology·2026
Same journal

Neurobiological interfaces connecting environmental change to monarch butterfly migration.

Current opinion in neurobiology·2026
Same journal

Learning how to experience the world: From circuits to cell types to genes.

Current opinion in neurobiology·2026
Same journal

Editorial overview for neurobiology of disease 2026.

Current opinion in neurobiology·2026
Same journal

Optical voltage imaging: ready to spark systems neuroscience.

Current opinion in neurobiology·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 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 computations associated with goal-directed choice.

Antonio Rangel1, Todd Hare

  • 1HSS & CNS, California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA, USA. rangel@hss.caltech.edu

Current Opinion in Neurobiology
|March 27, 2010
PubMed
Summary
This summary is machine-generated.

Understanding goal-directed decision-making involves analyzing choices between actions with varying rewards and costs. This review highlights computational models and neuroimaging studies illuminating brain mechanisms for these choices.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 14, 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

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

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Decision Science

Background:

  • Goal-directed decision-making involves selecting actions based on anticipated rewards and associated costs.
  • Understanding the neural computations underlying these choices is crucial for deciphering complex behaviors.

Purpose of the Study:

  • To review recent advances in understanding the computations of goal-directed choices.
  • To explore how these computations are implemented in the brain.

Main Methods:

  • Computational modeling of decision-making processes.
  • Human functional Magnetic Resonance Imaging (fMRI) studies.
  • Monkey neurophysiology research.

Main Results:

  • Integration of computational, neuroimaging, and neurophysiological data provides insights into choice mechanisms.
  • Advances reveal how the brain evaluates potential rewards and costs.

Conclusions:

  • Significant progress has been made in elucidating the neural basis of goal-directed decision-making.
  • Future research integrating diverse methodologies will further refine our understanding.