Space your practice.
Memory research shows why returning to material across separate sessions matters. Spacing is a core principle in Paivio’s review schedule.
Cepeda et al., 2006 ↗We began with FSRS, the open-source scheduling algorithm also supported by Anki. On that foundation, we built our own concept-mapping, review-ordering and retrieval systems.
Memory research shows why returning to material across separate sessions matters. Spacing is a core principle in Paivio’s review schedule.
Cepeda et al., 2006 ↗Actively recalling information is a central finding in learning research. Paivio puts the question before the answer so recall is part of the routine.
Roediger & Karpicke, 2006 ↗FSRS estimates memory states from review history and uses them to set the next review. That is the scheduling foundation we build on.
Anki’s FSRS documentation ↗That is the scheduling question FSRS helps answer. Paivio uses your explicit ratings to advance that schedule, so review becomes a repeatable routine.
We built the rest of the experience around the material itself: its concepts, their relationships and the ways you practice recalling them.
An illustration of spaced review
FSRS models memory and schedules cards from your explicit recall ratings. It is the open-source scheduling foundation also supported by Anki.
Our concept-mapping system connects the material in your own decks. When the format your account is assigned uses the graph, on your own decks it orders reviews by prerequisite links within the due cards. It never adds a card or changes a due date.
Our retrieval system brings supported card presentations into the study session. Which study mechanisms are delivered depends on the format your account is assigned. Card presentations follow a fixed policy, not your individual mastery or past mistakes. Multiple choice and matching are held back while an account is being measured in the comparison.
Our proprietary development program extends this foundation with machine-learning methods and integrated learning systems.
We’re continuing development and completing our research trials while progressing the patent work around our proprietary methods.
We’ll make the research findings and technical details publicly available when that work is complete. The details of our ongoing development remain private for now.
This section describes ongoing research and development. The current study mechanisms are described above.
Review timing, ratings and due dates are identical across the comparison formats. Your explicit ratings remain the basis of the FSRS schedule.
Consenting accounts are assigned once to one of three fixed formats. The delivered study mechanisms vary by format.
Our research measures recall at seven days or later. Participation details are available in your learning profile, and ordinary study stays available.
FSRS is our open-source scheduling foundation, also supported by Anki. Paivio’s concept-mapping, review-ordering and retrieval systems are separate layers built around that foundation.
Paivio’s own learning systems bring concept relationships, review ordering and retrieval practice together. Our ongoing research and development includes machine-learning methods.
We’ll publish the research findings and share technical details after the current research trials and patent work are complete.
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