Memory out of context: Spacing effects and decontextualization in a computational model of the medial temporal lobe
James W. Antony · 2026
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Some neural representations change 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 t
Abstract by James W. Antony (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.21428/8e6ba8ef.d9f5dd19