Default Mode Network Connectivity Predicts Individual Differences in Long-Term Forgetting: Evidence for Storage Decay, not Retrieval Failure

Abstract

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade and, without such a measure, it is hard to identify the corresponding neural correlates. This study addresses this problem using a combination of individual differences, model-based inferences, and resting-state functional connectivity. The individual-specific values of rate of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to data from an adaptive fact learning task. Individual rates of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine-learning techniques to identify the most predictive and generalizable features. Consistent with the existing literature, our results identified a sparse, distributed network of cortical and subcortical regions that underlies forgetting in LTM. Cross-validation showed that individual rates of forgetting were predicted with high accuracy (r = .96) from this connectivity pattern alone. These results open up new opportunities for the study of individual differences in LTM function and dysfunction.

Large-scale evaluation of cold start mitigation in adaptive fact learning: Knowing "what" matters more than knowing "who"

“…The finding that learner-specific prediction can work, even when the learning task is not so much about skills but rather about declarative knowledge, is in line with the idea of a learner's general forgetting rate being an individual trait. Learners' SoF has been found to persist over time (Sense et al, 2016), and can be related to resting state measures of brain activity (Xu et al, 2021;Zhou et al, 2020). The larger the individual differences between learners, the more important learner-specific predictions are likely to be.…”

“…The rate of forgetting in particular appears to be a reliable individual trait with substantial variation between learners, that can be predicted from resting state neural activity and functional connectivity (Xu, Prat, Sense, van Rijn, & Stocco, 2021;Zhou, Sense, van Rijn, & Stocco, 2020). For adaptive fact learning systems, this inter-individual variability means that some learners will consistently require more frequent repetitions of a fact than others to maintain a desired level of performance.…”

Large-Scale Longitudinal Assessment of Declining Memory Function Through Individualized Modeling of Episodic Memory

“…Furthermore, by integrating the SoF model with neuroimaging data, it is possible to uncover the underlying neural mechanisms responsible for memory decline in aging and disease. Recent studies by Zhou et al (26) and Xu et al (27) have demonstrated this approach utilizing previous iterations of the SoF task to analyze differences in functional connectivity networks and pinpoint specific brain regions correlated with forgetting.…”

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

“…Sense et al found that speed of forgetting was characteristic of an individual, and was highly stable (r > 0.6) across time and materials (2016). Furthermore, using neuroimaging methods, Zhou et al (2021) and Xu et al (2021) found that not only does speed of forgetting capture individual differences in long-term memory function, but also correlates with, and can be decoded from, their spontaneous brain activity at rest.…”