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

Random Sampling Method01:09

Random Sampling Method

11.2K
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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K
Random Error01:04

Random Error

887
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
887
Genetic Drift03:33

Genetic Drift

39.8K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
39.8K
Random Variables01:09

Random Variables

12.0K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
12.0K
Randomized Experiments01:13

Randomized Experiments

7.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.0K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Agreements and disagreements with resource-rational contractualism.

The Behavioral and brain sciences·2026
Same author

The Political Psychology of Economic Inequality.

Psychological science in the public interest : a journal of the American Psychological Society·2026
Same author

Random generation is what comes to mind in naturalistic settings.

Cognition·2026
Same author

Generated outcomes in risky choice reveal biased sampling and sequential dependencies.

Communications psychology·2026
Same author

Imagining and building wise machines: the centrality of AI metacognition.

Trends in cognitive sciences·2026
Same author

The Lancet Commission on improving population health post-COVID-19.

Lancet (London, England)·2025

Related Experiment Video

Updated: Jul 6, 2025

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.2K

Explaining the flaws in human random generation as local sampling with momentum.

Lucas Castillo1, Pablo León-Villagrá2, Nick Chater3

  • 1Department of Psychology, University of Warwick, Coventry, United Kingdom.

Plos Computational Biology
|January 5, 2024
PubMed
Summary

Human random sequences are too predictable due to a local sampling algorithm. This algorithm, used for probabilistic inference, explains why people struggle to generate truly random behavior, even when trying.

More Related Videos

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.5K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

990

Related Experiment Videos

Last Updated: Jul 6, 2025

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

7.2K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.5K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

990

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Human Behavior Analysis

Background:

  • Human behavior often exhibits more noise than optimal levels in various tasks.
  • Despite this, individuals tend to be predictable when asked to generate random sequences.
  • These seemingly contradictory observations may stem from a unified cognitive mechanism.

Purpose of the Study:

  • To investigate the underlying cognitive processes governing human random sequence generation.
  • To test predictions of a local sampling algorithm for probabilistic inference in explaining human randomness.
  • To identify novel behavioral signatures in human random sequences.

Main Methods:

  • Two experiments were conducted to assess deviations from randomness.
  • Participants generated random sequences from uniform, non-uniform, and recently-learned distributions.
  • Computational modeling was employed to evaluate different local sampling algorithms.

Main Results:

  • Human deviations from randomness were consistent across different distributions, challenging previous accounts.
  • A novel signature of too few trajectory changes was observed in generated sequences.
  • Local sampling with maintained directionality across trials best explained the experimental data.

Conclusions:

  • A general-purpose local sampling algorithm, maintaining directionality, underlies human random sequence generation.
  • This mechanism explains both suboptimal noise in tasks and predictability in randomness.
  • The findings suggest this algorithm's broader application in other cognitive tasks.