Computational Model for Detecting Memory Impairments Wins Applied Modeling Award

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Holly Hake’s paper was awarded the Applied Modeling Award at the Cognitive Science Conference. Selected from over 2,000 submissions, the paper demonstrated how the SGMA Index computational model could transform the detection of memory impairments.

This award-winning paper marked the first introduction of the SGMA Index model. In collaboration with mentor Andrea Stocco, Holly Hake found that the model could identify memory impairments with an 8-minute test, replacing the need for traditional 3-hour clinical assessments.

The research involved elderly participants, both with and without mild cognitive impairment (MCI)—a condition that can precede Alzheimer’s disease and other dementias. Participants completed weekly MemoryLab lessons over the course of a year. By analyzing their rates of forgetting, the model revealed that individuals with MCI had significantly higher forgetting rates compared to healthy controls. The model achieved over 80% accuracy in identifying MCI after a single 8-minute learning session.

Read the full paper: Breaking New Ground in Computational Psychiatry: Model-Based Characterization of Forgetting in Healthy Aging and Mild Cognitive Impairment

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Researcher at MemoryLab

Thomas Wilschut

Researcher & Partnership Coordinator

anais-capik

Anais Capik

Project Coordinator

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