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Related Experiment Video

Updated: Dec 10, 2025

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

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

Published on: June 30, 2020

Inferring latent learning factors in large-scale cognitive training data.

Mark Steyvers¹, Robert J Schafer²

¹Department of Cognitive Sciences, University of California, Irvine, CA, USA. mark.steyvers@uci.edu.

Nature Human Behaviour

|September 2, 2020

Summary

Human cognition allows learning diverse tasks. Analyzing learning trajectories from 36,297 individuals reveals predictable patterns across tasks, indicating underlying general and specific learning abilities.

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Area of Science:

Cognitive science
Psychology
Data science

Background:

Human cognition is characterized by the ability to learn diverse tasks.
Understanding individual differences in learning dynamics is crucial.
Previous research often assumes specific learning curve forms or task relationships.

Purpose of the Study:

To analyze the latent structure of learning trajectories across numerous individuals and tasks.
To investigate covariation across learning trajectories with minimal assumptions.
To identify underlying factors influencing learning dynamics.

Main Methods:

Probabilistic dimensionality reduction modeling was applied to learning data from 36,297 individuals.
Analysis of learning trajectories across 51 different cognitive tasks.
Data-driven approach to uncover latent structures without pre-defined assumptions on learning curves or task relationships.

Main Results:

Substantial covariation was found across learning trajectories, enabling prediction of unobserved learning.
A general ability factor, prominent in later practice stages, was identified.
Task-specific factors were discovered, correlating with defined task features and domains (e.g., attention, spatial processing, language, math).

Conclusions:

Learning trajectories across diverse tasks are not independent and exhibit predictable patterns.
A hierarchical structure of learning exists, comprising general and specific abilities.
The findings provide insights into individual differences in cognitive learning and task-specific skill acquisition.