Nidhi V. Banavar ^1* Sharon M. Noh ² Christopher N. Wahlheim ³ Brittany S. Cassidy ³ Brock Kirwan ⁴ Craig Stark ² Aaron Bornstein ^2*

• ¹ University of California, Berkeley, Berkeley, California, United States
• ² University of California, Irvine, Irvine, California, United States
• ³ University of North Carolina at Greensboro, Greensboro, North Carolina, United States
• ⁴ Brigham Young University, Provo, Utah, United States

The Mnemonic Similarity Task (MST; [1]) is a widely used measure of individual tendency to discern small differences between remembered and presently presented stimuli. Significant work has established this measure as a reliable index of neurological and cognitive dysfunction and decline. However, questions remain about the neural and psychological mechanisms that support performance in the task. Here, we provide new insights into these questions by fitting seven previously-collected MST datasets (total N = 519) using a three-choice evidence accumulation model (the Linear Ballistic Accumulator; LBA [2]). The model decomposes choices into automatic and deliberative components. We show that these decomposed processes both contribute to the standard measure of behavior in this task, as well as capturing individual variation in this measure across the lifespan. We also exploit a delayed test/re-test manipulation in one of the experiments to show that model parameters exhibit improved stability, relative to the standard metric, across a one week delay. Finally, we apply the model to a resting-state fMRI dataset [3], finding that only the deliberative component corresponds to off-task co-activation in networks associated with long-term, episodic memory. Taken together, these findings establish a novel mechanistic decomposition of MST behavior, and help to constrain theories about the cognitive processes that support performance in the task.