Memory out of context: Spacing effects and decontextualization in a computational model of the medial temporal lobe

Abstract

Some neural representations change slowly across multiple timescales. Here we argue that modeling this "drift" could help explain the spacing effect (the long-term benefit of distributed learning), whereby differences between stored and current temporal context activity patterns produce greater error-driven learning. We trained a neurobiologically realistic model of the entorhinal cortex and hippocampus to learn paired associates alongside temporal context vectors that drifted between learning episodes and/or before final retention intervals. In line with spacing effects, greater drift led to better model recall after longer retention intervals. Dissecting model mechanisms revealed that greater drift increased error-driven learning, strengthened weights in slower-drifting temporal context neurons (temporal abstraction), and improved direct cue-target associations (decontextualization). Intriguingly, these results suggest that decontextualization -- generally ascribed only to the neocortex -- can occur within the hippocampus itself. Altogether, our findings provide a mechanistic formalization for established learning concepts such as spacing effects and errors during learning.

Benefits of spaced learning are predicted by re-encoding of past experience in ventromedial prefrontal cortex

“…An additional nuance to spacing effects is that the optimal amount of spacing (the ‘peak’ in the non-monotonic function) has been shown to increase as a function of the retention interval (RI; here, the RI is the E2-E3 lag) 7,26,27 . For the sake of comparison with this line of work, we report spacing effects as a function of RI for our initial behavioral analyses.…”

“…While a re-encoding account provides a parsimonious explanation for our findings—and of spacing effects more generally—a recent computational model suggests a slightly different interpretation that also aligns well with our findings. Namely, Antony et al argue that variability triggers the abstraction of similarities across stimulus exposures 26 . That is, when a stimulus is re- encountered after a relatively long delay, the encoding context is more likely to be different and this difference triggers error-driven learning that strengthens common elements across encounters at the expense of unique elements (i.e., abstraction of similarities).…”

“…In the current study, because memory was measured at each encounter (E1, E2, E3), we were able to directly test whether the relationship between E1-E2 spacing (hereafter referred to as spacing) and E3 recognition memory (hereafter referred to as subsequent memory) depended on successful recognition at E2. An additional nuance to spacing effects is that the optimal amount of spacing (the 'peak' in the non-monotonic function) has been shown to increase as a function of the retention interval (RI; here, the RI is the E2-E3 lag) 7,26,27 . For the sake of comparison with this line of work, we report spacing effects as a function of RI for our initial behavioral analyses.…”

A complementary learning systems model of how sleep moderates retrieval practice effects

“…This setup was designed to mimic the elapsed time between the initial learning phase and the final test, effectively removing the immediate study context to simulate the delay. This approach aligns with the procedure used by Antony et al (2022), where a similar methodology was employed to understand the effects of context change over time on memory retrieval. It is important to note that the inputs during the final test were kept consistent across both the wake and sleep conditions to ensure that any observed differences in performance could be attributed to the statedependent processes rather than the passage of time alone.…”

The influence of emotion on temporal context models

“…A growing body of neurophysiological evidence and work in theoretical cognitive neuroscience has refined the conception of temporal context relative to the initial proposal of exponentially-decaying traces. These developments solve several of the conceptual problems present in classical TCMs (Antony, Liu, Zheng, Ranganath, & O'Reilly, 2022;Howard et al, 2015). The strong connection between this more sophisticated representation of temporal context and neurophysiology, especially in the hippocampus, would open up avenues for the study of emotional modulation on memory.…”