Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Decision Making: P-value Method

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...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Expected Value

Expected Value

The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:

You might also read

Related Articles

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

Sort by

Same author

Author Correction: Plasticity and language in the anaesthetized human hippocampus.

Nature·2026

Same author

Distinct tasks engage a shared neural subspace in human hippocampus and anterior cingulate cortex.

bioRxiv : the preprint server for biology·2026

Same author

Plasticity and language in the anaesthetized human hippocampus.

Nature·2026

Same author

Accumulation of virtual tokens towards a jackpot reward enhances performance and value encoding in dorsal anterior cingulate cortex.

Nature communications·2026

Same author

MiniFAST: a sensitive and fast miniaturized microscope for <i>in vivo</i> neural recording.

Neurophotonics·2026

Same author

Author Correction: Intracranial directed connectivity links subregions of the prefrontal cortex to major depression.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Jun 29, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations.

James Webb^1,2, Paul Steffan¹, Benjamin Y Hayden³

¹Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.

Biorxiv : the Preprint Server for Biology

|April 8, 2024

Summary

Mice foraging behavior aligns with a modified marginal value theorem (MVT) in uncertain environments. They learn and adapt to changing reward rates, demonstrating sophisticated decision-making under volatility.

More Related Videos

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Published on: April 23, 2016

Related Experiment Videos

Last Updated: Jun 29, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Foraging Path-length Protocol for Drosophila melanogaster Larvae

Published on: April 23, 2016

Area of Science:

Behavioral Ecology
Neuroscience
Computational Biology

Background:

Foraging theory, particularly the marginal value theorem (MVT), explains optimal patch-leaving strategies in predictable environments.
Natural environments are often stochastic, requiring foragers to adapt to uncertainty, a limitation of the standard MVT.
Understanding animal decision-making in volatile conditions is crucial for behavioral ecology and neuroscience.

Conclusions:

Mice can efficiently forage in volatile environments by dynamically updating their internal models of reward availability.
The findings support a modified MVT that accounts for learning and adaptation in uncertain conditions.
This study provides a foundation for investigating the neural basis of foraging under uncertainty using systems neuroscience approaches.