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Adaptive learning systems that generate spacing intervals based on learner performance enhance learning efficiency and retention (Mettler, Massey & Kellman, 2016). Recent research in factual learning suggests that initial blocks of passive trials, where learners observe correct answers without overtly responding, produce greater learning than passive or active trials alone (Mettler, Massey, Burke, Garrigan & Kellman, 2018). Here we tested whether this passive + active advantage generalizes beyond factual learning to perceptual learning. Participants studied and classified images of butterfly genera using either: 1) Passive Only presentations, 2) Passive Initial Blocks followed by active, adaptive scheduling, 3) Passive Initial Category Exemplar followed by active, adaptive scheduling, or 4) Active Only learning. We found an advantage for combinations of active and passive presentations over Passive Only or Active Only presentations. Passive trials presented in initial blocks showed the best performance, paralleling earlier findings in factual learning. Combining active and passive learning produces greater learning gains than either alone, and these effects occur for diverse forms of learning, including perceptual learning.
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This content will become publicly available on November 1, 2025
Memory out of context: Spacing effects and decontextualization in a computational model of the medial temporal lobe.
Some neural representations gradually 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 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.
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- Award ID(s):
- 2122550
- PAR ID:
- 10595642
- Publisher / Repository:
- Psychological Review
- Date Published:
- Journal Name:
- Psychological Review
- Volume:
- 131
- Issue:
- 6
- ISSN:
- 0033-295X
- Page Range / eLocation ID:
- 1337 to 1372
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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