ReviewCurrent opinion in neurobiology2025
Curriculum effects in multitask learning through the lens of contextual inference.
Review in Current opinion in neurobiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Bridging the divide in motor learning research.Nature human behaviour · 2026Review
- Contextual cues and transition statistics drive expression of competing motor memories.iScience · 2026Article
- A minimal recurrent neural network models the robustness of interleaved practice on motor sequence learning.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
When learning multiple tasks, the structure of practice, or curriculum, profoundly influences learning outcomes across domains, including motor learning, rule learning, perceptual learning, and machine learning. In multitask learning settings, there is often a trade-off between the speed of acquisition and long-term retention. For example, in motor learning, acquisition appears faster, but retention is substantially reduced with blocked training compared to randomly interleaved training. In machine learning, this effect is known as catastrophic forgetting. In contrast, perceptual and cognitive learning benefit from structured, predictable curricula such as blocked training. We propose contextual inference as a unifying framework to explain these effects, emphasizing the integration of task transition dynamics, contextual cues and observation noise during learning. Insights from this framework may allow mitigating catastrophic interference in machine learning by leveraging principles inspired by biological learning.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.