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...
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 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...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
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...
Confirmation Biases01:31

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?

You might also read

Related Articles

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

Sort by
Same author

Sex differences in activations to the sight of faces, scenes, body parts and tools in visual and non-visual cortical regions leading to the human hippocampus.

Biology of sex differences·2026
Same author

Visual Cortical Lateralization in Activations and Functional Connectivity to the Sight of Faces, Scenes, Body Parts, and Tools.

Human brain mapping·2026
Same author

Invariant visual object and face learning in the ventral cortical visual pathway: A biologically plausible model.

PLoS computational biology·2026
Same author

Hippocampal Revolutions.

Neuroscience and biobehavioral reviews·2025
Same author

Reward-specific satiety and reward-specific motivation: neural bases and significance.

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

Genetic risk-dependent brain markers of resilience to childhood Trauma.

Nature communications·2025
Same journal

Comprehensive Analysis of Auditory Nerve Fiber Responses using Fiber-Specific Modeling.

Journal of neurophysiology·2026
Same journal

HCN channels modulate the medium afterhyperpolarization and adjust the firing gain of fast alpha motoneurons in mice.

Journal of neurophysiology·2026
Same journal

Targeting intracranial electrical stimulation to network regions defined within individuals causes network-level effects.

Journal of neurophysiology·2026
Same journal

When "Noise" Isn't Simply Noise: Deterministic Postural Drive During Noisy Galvanic Vestibular Stimulation (nGVS).

Journal of neurophysiology·2026
Same journal

Abrupt Scene Onsets and Gradually Emerging Scene Information Produce Distinct EEG Decoding Dynamics.

Journal of neurophysiology·2026
Same journal

From discovery to translation: charting a course for the <i>Journal of Neurophysiology</i>.

Journal of neurophysiology·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

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

Confidence-related decision making.

Andrea Insabato1, Mario Pannunzi, Edmund T Rolls

  • 1Theoretical and Computational Neuroscience, Center for Brain and Cognition, Universitat Pompeu Fabra, and Institució Catalana de Recerca i Estudis Avancats, Barcelona, Spain.

Journal of Neurophysiology
|April 16, 2010
PubMed
Summary
This summary is machine-generated.

Neural networks exhibit emergent decision-making properties, with firing rates reflecting confidence. A novel model demonstrates how neuronal noise influences probabilistic choices and how confidence in decisions can be monitored in the brain.

Related Experiment Videos

Last Updated: Jun 13, 2026

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:

  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Neuronal firing rates have been observed to correlate with decision confidence.
  • Understanding the neural mechanisms underlying decision-making and confidence is crucial for cognitive science.

Purpose of the Study:

  • To model how confidence in decision-making emerges from neural network dynamics.
  • To investigate the role of neuronal noise in probabilistic decision-making.
  • To explore how the brain monitors confidence and makes meta-decisions.

Main Methods:

  • Developed an integrate-and-fire attractor network model for decision-making.
  • Simulated competing neuronal populations representing different choices.
  • Incorporated stochasticity (noise) from random neuronal spiking.
  • Modeled a hierarchical system with a second network for confidence-based decisions.

Main Results:

  • The attractor network model demonstrated emergent decision-making with firing rates reflecting confidence.
  • Neuronal noise was shown to introduce probabilistic elements into the decision process.
  • A secondary network successfully made decisions based on the confidence levels of the primary network.
  • The model accounts for existing neuronal recordings and predicts future experimental findings.

Conclusions:

  • Attractor network dynamics can explain emergent confidence representation in neuronal firing.
  • Neuronal noise plays a critical role in the probabilistic nature of decision-making.
  • The brain likely employs hierarchical systems for monitoring decision confidence and enabling meta-cognition.
  • This research offers insights into cognitive functions like monitoring and self-control.