Reflections of Idiographic Long-Term Memory Characteristics In Resting-State Neuroimaging Data

Abstract

Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individual's biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting state EEG data. A statistical analysis revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials, in agreement previous results. Importantly, both rates correlated with power levels in the alpha (8-13 Hz) and low beta (13-15 Hz) frequency bands, with the correlation between verbal rate of forgetting and low beta power over the right parietal site being significant even when accounting for multiple comparisons. The results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings.

Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates

“…We were also able to make rate of forgetting predictions at the level of the individual learner which were about as accurate as fact-level predictions. The fact that a learner's previously observed rates of forgetting are predictive of their rate of forgetting in a future session (albeit on a relatively short timescale in the experiments reported here) is in line with the view of rate of forgetting as a stable, learner-specific trait (Sense et al 2016;Zhou et al 2020).…”

“…The current study attempts to alleviate this cold start by harnessing known individual differences in rate of forgetting between facts and between learners-some facts are consistently easier than others, and some learners are consistently better at memorisation than others (see e.g. Sense et al 2016;Zhou et al 2020)-for changing the initial estimates of the memory model, creating a "warm start" instead. Informed initial estimates are derived from observations of other learners studying the same fact (a collaborative filtering approach), from an earlier session in which the same learner studied different facts (a content-based approach), or from a combination of the two (a hybrid approach).…”

When Fear Shrinks the Brain: A Computational Model of the Effects of Posttraumatic Stress on Hippocampal Volume

“…Each successive encoding, re‐encoding, or retrieval of a memory m results leaves a new trace of the memory. The odds of retrieving any trace decay exponentially over time with a decay rate d , which represents an individual‐specific rate of forgetting (Sense, Behrens, Meijer, & van Rijn, 2016; Zhou et al., 2021). Thus, the odds of retrieving m correspond to the sum of the odds of retrieving any of its individual decaying traces, and the base‐level activation B ( m ) can be expressed as the log of this sum: where t i represents the time elapsed since the encoding of the i th trace.…”

Memory, Interrupted: A Retrieval Model of Intrusive Memories, Recovery Trajectories, and Neurobiological Effects in Post-Traumatic Stress Disorder

“…Perhaps most remarkably, the parameter d, which controls the degree of memory decay and forgetting, was kept constant not only within but also across simulations. This choice was motivated by the fact that little is still known about individual differences in forgetting (although work quantifying such differences has recently been done: Sense et al, 2018;Zhou et al, 2021) and, as a consequence, not much is known about the interaction between forgetting and intrusive memories. Nonetheless, because of its critical role in determining the frequency of intrusive memories, future work should include careful examinations of this parameter's role.…”