Adaptive Design Optimization for the Mnemonic Similarity Task

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings, where it has been used to identify hippocampal dysfunction associated with healthy aging, dementia, schizophrenia, depression, and other disorders. As with any test used in a clinical setting, it is especially important for the MST to be administered as efficiently as possible. We apply adaptive design optimization methods (Myung et al., 2013) to optimize the presentation of test stimuli in accordance with previous responses.This optimization is based on a novel signal-detection model of an individual’s memory capabilities and decision-making processes. We demonstrate that the cognitive model is able to describe people’s behavior and measure their ability to separate patterns. We also demonstrate that the adaptive design optimization approach generally significantly reduces the number of test stimuli needed to provide these measures.