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

Probability Distributions01:32

Probability Distributions

6.9K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.9K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Probability Histograms01:17

Probability Histograms

11.3K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.3K
Central Limit Theorem01:14

Central Limit Theorem

14.6K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
14.6K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Sampling Distribution01:12

Sampling Distribution

12.4K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.4K

You might also read

Related Articles

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

Sort by
Same author

An equivalent illuminant analysis of lightness constancy with physical objects and in virtual reality.

Behavior research methods·2025
Same author

A comparison of human and GPT-4 use of probabilistic phrases in a coordination game.

Scientific reports·2024
Same author

Lightness constancy in reality, in virtual reality, and on flat-panel displays.

Behavior research methods·2024
Same author

Detecting visual texture patterns in binary sequences through pattern features.

Journal of vision·2023
Same author

Two sources of uncertainty independently modulate temporal expectancy.

Proceedings of the National Academy of Sciences of the United States of America·2021
Same author

The bounded rationality of probability distortion.

Proceedings of the National Academy of Sciences of the United States of America·2020

Related Experiment Video

Updated: Jun 27, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K

Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a

Keiji Ota1,2,3,4, Laurence T Maloney1,2

  • 1Department of Psychology, New York University, New York, New York, United States.

Plos Computational Biology
|May 1, 2024
PubMed
Summary

Human decision-making deviates from Bayesian decision theory (BDT) predictions, particularly in how sample information is weighted. Alternative models, like those using extreme sample points, better explain observed behavior in cognitive tasks.

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

10.9K
Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.2K

Related Experiment Videos

Last Updated: Jun 27, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

10.9K
Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat
11:18

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

15.2K

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Science

Background:

  • Bayesian decision theory (BDT) models normative performance in decision-making tasks involving uncertainty and value.
  • Normative models dictate optimal information encoding and combination to maximize expected reward.
  • Standard BDT computations involve probabilities, but real-world tasks often use probability density functions (PDFs) from samples.

Purpose of the Study:

  • To investigate human ability to perform individual computations within a BDT framework for visual cognitive tasks.
  • To assess human adherence to normative principles of accuracy, additivity, and influence when using sample-derived PDFs.
  • To compare human decision-making strategies against normative BDT predictions and explore alternative models.

Main Methods:

  • Deconstructing Bayesian decision theory (BDT) into sequential computations for isolated testing.
  • Evaluating human performance on tasks requiring the use of probability density functions (PDFs) derived from samples.
  • Measuring influence to quantify the weighting of individual sample points in decision-making and comparing it to normative standards.

Main Results:

  • Participants systematically violated normative accuracy and additivity principles in PDF-based decision tasks.
  • While accuracy and additivity deviations had minor impacts, sample point influence weighting significantly differed from BDT predictions.
  • Human decision-makers failed to utilize geometric symmetries of PDFs, unlike the normative BDT model.

Conclusions:

  • Human decision-making in tasks with sample-derived PDFs deviates from normative Bayesian decision theory (BDT) predictions.
  • The normative BDT model's assumption of utilizing geometric symmetries is not reflected in human behavior.
  • An alternative model, prioritizing decisions based on a single extreme sample point, offers a more accurate account of observed human data.