NeuraCache offers two static algorithms and two adaptive algorithms at the moment. You can switch between them at any time, either for a single note or for a group of notes.
STATIC ALGORITHMS
Static algorithms resurface your notes on a fixed schedule. The retention score does not have any effect on the next review date.
- 1, 2, 3, 4, 5 days
The reviews will happen on the 1st, 2nd, 3rd, 4th, and 5th day from the start date (5 consecutive days). This pattern is best used for Cramming (Education))
Usage example: You have a known deadline (like exam next week). You would like to review your set of notes every day for the next five days, no matter how well you remember each note.
- 1, 5, 15, 30, 60 days
This pattern is a simple approximation of the Ebbinghaus forgetting curve. It has larger gaps between reviews — up to a month at the end. It is therefore not suitable for cramming, but it works better for non-urgent knowledge retention that does not have a deadline.
Usage example: Reading an article online that has a fascinating insight you would like to retain for much longer. You could capture/start spaced repetition using Evernote WebClipper as in this example
ADAPTIVE ALGORITHMS
Adaptive algorithms resurface your notes based on the latest retention score.
- Adaptive (SM2 based)
This algorithm is an adaptation of the popular SuperMemo2 algorithm.
In most cases, this is the best choice for all types of notes. The algorithm chooses the next review date based not only on your last review score but also on the history of your previous reviews. It is optimized for storing learnings in your memory forever.
Usage example: Things you have learned or understand enough to express as a set of atomic Flashcards.
- Adaptive Simple (exponential)
This is a very basic algorithm that considers only the latest review score. It does not include the review history when calculating the next review date. Based on the score (0%–100%), it calculates the next review date from an exponential function in the range of "today – 6 months". So if your review score is 100%, the note will resurface in 6 months; if it's ~80%, the next review will be in ~3 months; at 0% — the same day, and so on.
Usage example: Book insights, lectures, article insights, thoughts, quotes, etc.
Whatever you would like to put into your long-term memory with somewhat lower effectiveness (compared to SM2) but without feeling overwhelmed by the number of reviews.
Which algorithm to select:
Although SM2 is the best default choice for results, it is worth experimenting with the other algorithms to see how they work for you.
A typical situation is when you feel there are too many reviews in Today's Queue. If this happens, try adjusting the algorithm to "Adaptive Simple" or "1, 5, 15, 30, 60" for some notes so that they come back for review much later in the future.
You can also use the Today's Queue Limit feature.
See integrations:
How To Use💜ðŸ§
Marcin