A neurocognitive model for predicting the fate of individual memories

Abstract

One goal of cognitive science is to build theories of mental function that predict individual behavior. In this project we focus on predicting, for individual participants, which specific items in a list will be remembered at some point in the future. If you want to know if an individual will remember something, one commonsense approach is to give them a quiz or test such that a correct answer likely indicates later memory for an item. In this project we attempt to predict later memory without explicit assessments by jointly modeling both neural and behavioral data in a computational cognitive model which captures the dynamics of memory acquisition and decay. In this paper, we lay out a novel hierarchical Bayesian approach for combining neural and behavioral data and present results showing how fMRI signals recorded during the study phase of a memory task can improve our ability to predict (in held-out data) which items will be remembered or forgotten 72 hours later.

Model-based fMRI analysis of memory

“…Inferring latent state is more complex when researchers aim to characterise complex mental operations that unfold through time [39]. One popular approach is to use hidden Markov models (HMMs) to infer what operations people are currently undertaking and using this characterisation to interpret the BOLD response [40,41].…”

Linking Models with Brain Measures

“…Inferring latent state is more complex when researchers aim to characterise complex mental operations that unfold through time (Wijeakumar et al, 2017). One popular approach is to use hidden Markov models (HMMs) to infer what operations people are currently undertaking and using this characterisation to interpret the BOLD response (Anderson et al, 2018;Tubridy et al, 2018).…”