Application of Machine Learning Models for Tracking Participant Skills in Cognitive Training.

ABSTRACT: A key need in cognitive training interventions is to personalize task difficulty to each user and to adapt this difficulty to continually apply appropriate challenges as users improve their skill to perform the tasks. Here we examine how Bayesian filtering approaches, such as hidden Markov models and Kalman filters, and deep-learning approaches, such as the long short-term memory (LSTM) model, may be useful methods to estimate user skill level and predict appropriate task challenges. A possible advantage of these models over commonly used adaptive methods, such as staircases or blockwise adjustment methods that are based only upon recent performance, is that Bayesian filtering and deep learning approaches can model the trajectory of user performance across multiple sessions and incorporate

SUBMITTER: Sandeep S 

PROVIDER: S-EPMC7387708 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature