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

Methods of Medium Optimization01:28

Methods of Medium Optimization

63
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
63
Optimal Foraging00:48

Optimal Foraging

14.4K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

415
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
415
Optimal Arousal Theory01:23

Optimal Arousal Theory

1.3K
The optimal arousal theory suggests that performance is maximized when an individual experiences a moderate level of arousal. This theory is closely tied to the Yerkes-Dodson law, which illustrates an inverted U-shaped relationship between arousal and performance. The law, formulated by psychologists Robert Yerkes and John Dodson, implies an ideal arousal level for optimal performance, and deviations from this level can lead to declines in effectiveness.
Inverted U-Shaped Performance Curve
The...
1.3K
Optimization Problems01:26

Optimization Problems

197
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
197
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

802
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...
802

You might also read

Related Articles

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

Sort by
Same author

Postoperative pain after joint replacement surgery : A randomized controlled trial of transcutaneous auricular vagus nerve stimulation and the role of depression and anxiety.

Orthopadie (Heidelberg, Germany)·2026
Same author

A thin line between conflict and reaction time effects on EEG and fMRI brain signals.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Decoding deception with the P300: A meta-analysis of the Concealed Information Test.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2025
Same author

Methamphetamine-induced adaptation of learning rate dynamics depend on baseline performance.

eLife·2025
Same author

Bidirectional modulation of reward-guided decision making by dopamine.

Psychopharmacology·2025
Same author

Cognitive impairment and associated neurobehavioral dysfunction in post-COVID syndrome.

Psychiatry research·2025
Same journal

Fast-conducting mechanonociceptors uniquely engage reflexive and affective pain circuitry to drive protective responses.

Neuron·2026
Same journal

Sparse component analysis: A method that uncovers separable computations within neural population activity.

Neuron·2026
Same journal

Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.

Neuron·2026
Same journal

TGF-β1-induced endothelial transcytosis drives blood-brain barrier leakage during aging.

Neuron·2026
Same journal

Image space opens up for visual neuroscience.

Neuron·2026
Same journal

Septal GLP-1 receptors control alcohol taking and seeking.

Neuron·2026
See all related articles

Related Experiment Video

Updated: Apr 20, 2026

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

2.3K

When is the time for a change? Decomposing dynamic learning rates.

Adrian G Fischer1, Markus Ullsperger2

  • 1Otto von Guericke University Magdeburg, Institute of Psychology II, 39106 Magdeburg, Germany.

Neuron
|December 3, 2014
PubMed
Summary
This summary is machine-generated.

Humans adjust beliefs based on new evidence through a common neural mechanism. This study reveals how distinct brain factors converge to drive belief updates and behavioral changes.

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.2K

Related Experiment Videos

Last Updated: Apr 20, 2026

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

2.3K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.2K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Decision-Making

Background:

  • Human belief updating and behavioral adjustment are flexible.
  • The neural underpinnings of evidence weighting are not fully understood.

Purpose of the Study:

  • To investigate the neuronal mechanisms of evidence weighting in belief updates.
  • To identify how distinct factors influencing weighting converge.

Main Methods:

  • The study by McGuire et al. (2014) utilized neuroimaging techniques.
  • Analysis focused on identifying neuronal correlates of evidence weighting.

Main Results:

  • Distinct neuronal factors contributing to evidence weighting were identified.
  • These distinct factors converge onto a shared mechanism.
  • This common mechanism drives belief updates.

Conclusions:

  • A unified neural mechanism underlies the flexible weighting of evidence.
  • Understanding this mechanism provides insight into belief formation and behavioral adaptation.