Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Grounding mathematics in an integrated conceptual structure, part II: intervention study demonstrating robust learning and retention through a grounded curriculum.

Frontiers in psychology·2026
Same author

Learning to Decompose: Human-Like Subgoal Preferences Emerge in Neural Networks Learning Graph Traversal.

Open mind : discoveries in cognitive science·2025
Same author

Visual enumeration remains challenging for multimodal generative AI.

PloS one·2025
Same author

Artificial intelligence can emulate human normative judgments on emotional visual scenes.

Royal Society open science·2025
Same author

Reflections on David E. Rumelhart and the Rumelhart Prize.

Topics in cognitive science·2025
Same author

Grounding mathematics in an integrated conceptual structure, part I: experimental evidence that grounded rules support transfer that formal rules do not.

Frontiers in psychology·2025
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 Experiment Video

Updated: Dec 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience

Alberto Testolin1,2, Will Y Zou3, James L McClelland4

  • 1Department of General Psychology, University of Padova, Padova, Italy.

Developmental Science
|January 25, 2020
PubMed
Summary

Number sense may not be innate. Deep learning models show that experience and environmental statistics shape numerical acuity, suggesting domain-general learning mechanisms can explain number processing abilities in animals.

Keywords:
approximate number systemcomputational modelingdeep neural networksnumber sense developmentnumerosity perceptionvisual number sense

More Related Videos

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

7.9K
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.0K

Related Experiment Videos

Last Updated: Dec 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K
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

7.9K
A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.0K

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Sensitivity to approximate quantities (numerosity) is observed in humans and animals, often interpreted as an innate 'number sense'.
  • Recent computational models suggest that numerosity sensitivity can emerge in deep neural networks through experience, challenging the innate view.
  • Developmental increases in numerical acuity suggest a role for experience in shaping number sense.

Purpose of the Study:

  • To model the development of numerical acuity using a progressive unsupervised deep learning algorithm.
  • To investigate the influence of environmental statistical distributions on the emergence of numerosity representations.
  • To provide computational support for an emergentist perspective on number sense.

Main Methods:

  • Developed a progressive unsupervised deep learning algorithm to simulate developmental changes in numerical acuity.
  • Trained deep neural networks on sensory data with varying statistical distributions of numerical and non-numerical features.
  • Quantitatively characterized developmental patterns in computational models and compared them to human developmental data.

Main Results:

  • Deep networks demonstrated numerosity sensitivity even before training, with progressive refinement through experience.
  • The statistical structure of the learning environment significantly modulated the development of numerosity representations.
  • Simulations provided a refined characterization of developmental patterns observed in human children's numerical acuity.

Conclusions:

  • Domain-general learning mechanisms, rather than a specialized number system, can account for numerosity processing.
  • Experience and environmental statistics play a crucial role in shaping numerical acuity throughout development.
  • The findings support an emergentist perspective where number sense develops through interaction with the environment.