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Description: The goal of the OMNI project is to develop a computational framework for the prediction and optimization of human memory which is informed by indirect measurements of brain activity in humans. Simply put, we aim to record people's brains while they learn things, predict which things they learned successfully and which they didn't, and then devise better ways to teach them. The project applies state of the art in machine learning and cognitive modeling methods to the design of next-generation intelligent tutoring systems.

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Knowledge Tracing Using the Brain

Knowledge tracing is a popular and successful approach to modeling student learning. In this paper, we investigate whether the addition of neuroimagin...

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Modeling Second-Language Learning from a Psychological Perspective

Psychological research on learning and memory has tended to emphasize small-scale laboratory studies. However, large datasets of people using educatio...

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Data Release 1: Forgetting curves


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A neurocognitive model for predicting the fate of individual memories

One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for in...

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A Hierarchical Bayesian Approach to Inferring Mnemonic Status from the Brain

Abstract for Computational Cognitive Neuroscience Conference 2017 (CCN2017) at Columbia University.

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Predicting memory performance using a joint model of brain and behavior

abstract for Computational Cognitive Neuroscience conference (CCN2018)

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