FSRS is a modern spaced repetition algorithm
published on 2024/01/16
FSRS is a modern spaced repetition algorithm that was developed by Jarrett Ye. It aims to learn your memory patterns and schedule reviews more efficiently than Anki's legacy SM2 algorithm.
The goal of a spaced repetition algorithm is to calculate the optimal intervals between reviews. But what makes an interval "optimal"? In FSRS, an interval is considered optimal if it corresponds to a specific probability of recalling a card. For example, if you want to be 90% sure that you will successfully recall a card the next time you see it, the optimal interval is the one at which the probability of recall is 90%.
FSRS is based on the "Three Component Model of Memory". The model asserts that three variables are sufficient to describe the status of a unitary memory in a human brain. These three variables include:
Retrievability (R): The probability that the person can successfully recall a particular information at a given moment. It depends on the time elapsed since the last review and the memory stability (S).
Stability (S): The time, in days, required for R to decrease from 100% to 90%. For example, S = 365 means that an entire year will pass before the probability of recalling a particular card drops to 90%.
Difficulty (D): The inherent complexity of a particular information. It represents how difficult it is to increase memory stability after a review.