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: Traditional Method01:14

Decision Making: Traditional Method

4.0K
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...
4.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
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...
5.3K
Decision Making01:20

Decision Making

88
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...
88
Reason and Intuition01:37

Reason and Intuition

6.4K
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...
6.4K
Convenience Sampling Method00:55

Convenience Sampling Method

8.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
8.6K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

203
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
203

You might also read

Related Articles

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

Sort by
Same author

Different brain regions support deliberation during food choice in disordered and healthy eating.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Conscious and nonconscious thought: Insights from the neuroscience of decision-making.

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

Learning reinforces curiosity for related information.

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

Human exploration strategically balances approaching and avoiding uncertainty.

eLife·2026
Same author

Sequential sampling from memory underlies perceptual decisions unyoked from actions.

bioRxiv : the preprint server for biology·2026
Same author

The inferred value of unchosen options spreads to related items in memory.

Cognition·2026

Related Experiment Video

Updated: Jun 5, 2025

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

5.9K

Value construction through sequential sampling explains serial dependencies in decision making.

Ariel Zylberberg1, Akram Bakkour1,2,3, Daphna Shohamy1,4,5

  • 1Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.

Elife
|December 10, 2024
PubMed
Summary

Subjective values fluctuate during decision-making, influencing choices and brain activity. This study introduces a new algorithm, Reval, to track these dynamic value changes, offering a better explanation than static values.

Keywords:
choice-induced preference changedecision makingdrift-diffusion modelfood-choice taskhumanneurosciencevalue-based decisionsvmPFC

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

5.7K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

33.9K

Related Experiment Videos

Last Updated: Jun 5, 2025

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

5.9K
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

5.7K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

33.9K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Decision Science

Background:

  • Decisions between familiar items typically rely on comparing subjective values.
  • Similar values prolong decision times and can lead to inconsistent choices, often attributed to noisy evidence accumulation.

Purpose of the Study:

  • To investigate if apparent choice stochasticity stems from dynamic value fluctuations rather than just noise.
  • To develop and validate a method for tracking real-time changes in subjective value.

Main Methods:

  • Analysis of choice data from a snack item selection task.
  • Development of the Reval algorithm to detect within-session value fluctuations.
  • Modeling choice behavior and neural activity (BOLD signal) using dynamic values.

Main Results:

  • The Reval algorithm identified significant dynamic fluctuations in subjective item values.
  • These dynamic values more accurately predicted participant choices and response times than static, stated values.
  • Dynamic values provided a better explanation for brain activity in the ventromedial prefrontal cortex.

Conclusions:

  • Subjective value is not static but dynamically constructed during deliberation.
  • These value revaluations influence subsequent decisions, explaining choice variability.
  • The findings support a bounded-evidence accumulation model with temporally correlated evidence samples.