Foundational Research

The goal of this project was to predict and track memory decline in subjectively or mildly cognitively impaired (MCI) individuals by using our model-based, adaptive fact-learning system. Here we present data demonstrating that these tools can diagnose mild memory impairment with over 80% accuracy after a single 8-minute learning session.

Adaptive learning systems have been successfully applied to word learning using keyboard-based input, but they have seen little application in spoken word learning. We show that typing and speech-based learning result in similar behavioral patterns that can reliably estimate individual memory processes. This is particularly beneficial for individuals with impaired typing skills, such as elderly learners.

Here, the individual speed of forgetting in long-term memory is correlated with a readily available, task-free neuroimaging measure: the resting-state EEG spectrum. Statistical analyses revealed that individual rates of forgetting were significantly correlated across verbal and visual materials. These findings suggest that model parameters that reliably characterize an individual's performance, such as speed of forgetting can be observed in that individual's neurophysiological activity at rest.

This study investigates whether domain-general individual differences, such as working memory capacity (WMC) and general cognitive ability (GCA), can inform the selection of initial parameters in adaptive fact-learning systems. These systems typically begin with default parameters, which are adjusted based on learners' responses during the learning process. The goal was to determine if WMC and GCA, measured prior to learning sessions, could improve the accuracy of initial model parameters, specifically those that affect repetition schedules. The study found no significant relationship between WMC, GCA, and learning outcomes, suggesting that executive functioning and attentional control do not significantly predict delayed recall.

Our adaptive algorithm model is tailored to individual learners and outperforms traditional systems by continuously updating the estimated speed of forgetting for each item based on learners' accuracy and reaction time. In this paper, we investigate whether the speed of forgetting remains stable over time and across different materials. We demonstrate that they are stable over time but not across materials.