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

Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
Probability in Statistics01:14

Probability in Statistics

Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
Introduction to Statistics01:17

Introduction to Statistics

The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
Random Variables01:09

Random Variables

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...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
Central Limit Theorem01:14

Central Limit Theorem

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...

You might also read

Related Articles

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

Sort by
Same author

ManyNumbers 3: A Multi-Lab Study of Demographic Correlates of Early Number Knowledge.

Developmental science·2026
Same author

Natural counting and measuring: The role of linguistic and referential cues in determining which quantity is "More".

Cognitive psychology·2025
Same author

Is personal identity intransitive?

Journal of experimental psychology. General·2024
Same author

Substances as a core domain.

The Behavioral and brain sciences·2024
Same author

How do we regard fictional people? How do they regard us?

Psychonomic bulletin & review·2023
Same author

Possible Objects: Topological Approaches to Individuation.

Cognitive science·2020
Same journal

Evidence for abstract spatial concept learning in young animals.

Cognition·2026
Same journal

Blurred lines or clear boundaries? Synchrony and social dominance shape domain-specific self-other processing.

Cognition·2026
Same journal

Knowability predicts curiosity and learning.

Cognition·2026
Same journal

Throwing good effort after bad: Evidence for a sunk-cost effect in cognitive effort-based decision-making.

Cognition·2026
Same journal

Cross-linguistic differences in incremental planning under uncertainty.

Cognition·2026
Same journal

Sensory attenuation scales with the strength of action-outcome coupling: A psychophysical study.

Cognition·2026
See all related articles

Related Experiment Video

Updated: May 10, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Can statistical learning bootstrap the integers?

Lance J Rips1, Jennifer Asmuth, Amber Bloomfield

  • 1Psychology Department, Northwestern University, 2029 Sheridan Road, Evanston, IL 60208, USA. rips@northwestern.edu

Cognition
|June 11, 2013
PubMed
Summary
This summary is machine-generated.

This study critiques a model of children's number learning, finding it lacks true bootstrapping and may not accurately reflect how children acquire number concepts, potentially leading to ambiguous mathematical understanding.

Keywords:
Bayesian inferenceBootstrappingNumber knowledgeNumber learningStatistical learning

More Related Videos

Drosophila Courtship Conditioning As a Measure of Learning and Memory
09:29

Drosophila Courtship Conditioning As a Measure of Learning and Memory

Published on: June 5, 2017

Related Experiment Videos

Last Updated: May 10, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Drosophila Courtship Conditioning As a Measure of Learning and Memory
09:29

Drosophila Courtship Conditioning As a Measure of Learning and Memory

Published on: June 5, 2017

Area of Science:

  • Cognitive Science
  • Developmental Psychology
  • Computational Linguistics

Background:

  • Children's acquisition of number words and cardinalities is a key developmental milestone.
  • Piantadosi, Tenenbaum, and Goodman (2012) proposed a computational model for this learning process.
  • The model utilizes statistical learning to establish the number-cardinality relation.

Purpose of the Study:

  • To critically evaluate Piantadosi et al.'s (2012) model of children's number learning.
  • To examine the model's claim of "Quinian bootstrapping."
  • To assess the model's relevance and accuracy in simulating children's actual learning methods.

Main Methods:

  • Analysis of the computational mechanisms within the Piantadosi et al. (2012) model.
  • Comparison of the model's learning process with theories of conceptual change and bootstrapping.
  • Evaluation of the model's starting assumptions (primitives) and learned representations.

Main Results:

  • The model does not perform "Quinian bootstrapping" as it lacks conceptual discontinuity.
  • Learning involves recombining primitives and statistical confirmation, aligning with Fodor's views on expressive power.
  • The model's learned procedure and initial primitives differ from typical child development.

Conclusions:

  • The model's learning mechanism is not analogous to true conceptual bootstrapping.
  • The simulation's relevance to children's learning is questionable due to methodological differences.
  • The model's partial understanding of integers allows for non-standard interpretations, highlighting potential ambiguities.