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Inferring latent learning factors in large-scale cognitive training data

Authors
Prof. Mark Steyvers
University of California, Irvine ~ University of California, Irvine
Bob Schafer
Lumos Labs, San Francisco
Abstract

The flexibility to learn diverse tasks is a hallmark of human cognition. To improve our understanding of individual differences and dynamics of learning across tasks, we analyze the latent structure of learning trajectories from 36,297 individuals as they learned 51 different tasks on the Lumosity online cognitive training platform. Through a data-driven modeling approach using probabilistic dimensionality reduction, we investigate covariation across learning trajectories with few assumptions about learning curve form or relationships between tasks. Modeling results show significant covariation across tasks such that an entirely unobserved learning trajectory can be predicted by observing trajectories on other tasks. The latent learning factors from the model include a general ability factor that is expressed mostly at later stages of practice, and additional task-specific factors that carry information capable of accounting for manually defined task features and task domains such as attention, spatial processing, language and math.

Tags

Keywords

Bayesian Modeling
Large-scale data
Cognitive Training
Learning

Topics

Bayesian Modeling
Learning
Discussion
New
relation to more standard lab tasks? Last updated 2 months ago

very exciting work! I find the question of learning in more real-life situations fascinating, and also the idea that we can test people in more pleasurable tasks than the ones we have in the lab. I myself am working on various smartphone apps that run cognitive tasks. Now I was wondering whether there has been work on relating these tasks in Lumosi...

Marieke Van Vugt 1 comment