I’m sorry for the unusually long post, but since the changes proposed by this feature are quite substantial, I wanted to provide a detailed explanation and analysis to ensure that the underlying ideas are clearly understood and properly evaluated. The key ideas are highlighted in bold, serving as a summary of the proposal.
DESCRIPTION OF THE IDEA
For clarity, I will refer to any individual member of a list as an item. A card can, in principle, contain any number of items.
My idea is that, within a single card listing multiple items, the user should be able to rate each item individually, using the usual scale from “again” to “easy”.
As an example: in a card listing the items neutrophils, lymphocytes, monocytes, eosinophils, and basophils, the user might recall neutrophils, monocytes, and basophils but forget lymphocytes and eosinophils. The system would then allow separate ratings for each item, reflecting the actual recall performance more accurately.
My method assumes that the user wants the full list, not the single items, to have a retrievability corresponding to the desired retention they have chosen. I will discuss the validity of this assumption later. The retrievability of a card with more items will generally be lower than the one of a basic card with only one item after the same interval, all other things being equal. In other words, lists are hard to remember and easy to forget. However, under the current system — and without using cloze deletions or creating multiple separate cards— there is no way to account for that a priori. As a result, the calculated intervals will often be excessively long, particularly in the early stages of learning the list and even more so when the list is very
large. Also, the likelihood of recalling correctly all items, let’s say five items, after a certain interval is higher if for example four out of five items were remembered in the previous review, compared to only one or two out of five. Currently, without cloze deletions or without multiple separate cards, the user must press “again” in both situations, if the user defines the threshold for “good” as recalling correctly all items.
Based on my basic knowledge of statistics I would suggest that the optimal interval to calculate through FSRS should be approximately the point at which the product of the retrievability of each individual item correctly matches the desired retention. Suppose a card has three items. Let’s call the desired retention of the list R, and the retrievability of each item at a given day in this way, R1, R2, R3. FSRS must find the day such that R1*R2*R3=R. Note that all the items will always be reviewed together on the same card, since the interval is one and is calculated for the entire list. I will return to this point later.
Since the overall retrievability is computed as the product of individual item probabilities, it decreases as the number of items on a card increases. This results in shorter intervals, which makes sense because recalling a larger list is inherently more difficult. The number of items recalled correctly also affects the interval calculation: for example, forgetting one item out of five (and recalling four correctly) may result in a calculated interval that is significantly longer than the previous one, rather than shortening. This reflects a more accurate adjustment based on partial success, rather than treating the card as completely failed.
Finally, I would like to add that the card would still progress even if only one item were rated, since updating the interval for a single item is sufficient to update the card’s overall interval. This feature is particularly useful when, for example, a user is struggling with just one item: they could choose to review and rate only that item without recalling the remaining ones, which would then be automatically treated as skipped. In FSRS, skipping an item would simply indicate that it was not reviewed, accurately reflecting the user’s action.
The “set due date” action could still be applied, but it would affect the entire list as a whole.
ADVANTAGES
-accurate retrievability calculation
I have already mentioned the first advantage, which is that it allows you to calculate the retrievability of an entire list accurately, so matching the desired retention. Using cloze deletions or manually creating multiple cards only enables the calculation of retrievability for individual items. if desired retention is 90%, a list with five items represented by five cloze deletions has an overall probability of being recalled in its entirety of 0.9^5=0.59, which is unacceptable for an exam where lists are asked. You can repeat the whole list right before the exam to make sure you remember it, but you may need to repeat several lists multiple times. This approach is demanding, risky, and easy to overlook.
-no interference
To approach the problem described above, one might consider creating ALSO a separate card that includes all items, as cloze overlapper add-on does automatically. However, this approach introduces significant interference, an issue common to all lists memorised using cloze deletions. By contrast, using a single card to review all items means there are no siblings to cause interference.
OPEN QUESTIONS
-optimizing for lists vs items
Besides the technical challenges, I think the most significant issue with this approach is that optimizing intervals for the retrievability of a list will result in suboptimally short intervals for the retrievability of the individual items. If the desired retention is 90%, then one item may have a probability of 97%, another 94%, and so on, but never 89% or lower.
So, should intervals be optimized so that the desired retention applies to the entire list or rather to the individual items?
My tentative answer is: it depends. If your goal is learning as many notions as possible, for example when you are learning a new language, then memorizing single-item cards, when feasible, is generally more efficient—Piotr Woźniak emphasizes this clearly in his minimum information principle—but if breaking down the list in single-item cards is impractical or not feasible, or if your goal is to recall the entire list accurately (e.g. for an exam), then optimizing for the whole list makes more sense, for the reasons I explained before(interference and too-low retention.
- substantial penalty for forgetting the entire list
The other important issue is that if you forget all the items in a list, let’s say five, you are strongly penalised, as if you had forgotten five separate cards in a row. Yet these two situations are not equivalent. In practice, it sometimes happens that seeing just one forgotten item allows me to recall the remaining four. In that sense, forgetting all the items may be closer to forgetting only one or a few of them. One possible solution could be to give the user the option to reveal the items one at a time, but I am not sure that it will solve the problem and I do not know how to implement this solution. It should also be considered that a so strong penalty is justifiable if you take into account that memorising lists is in general much harder than memorising single facts.
P.S. LMSherlock suggested a more immediate workaround to achieve, for example, a desired retention of 90% for a list of five items, without having to modify anki’s interface and core review process. You could create a note with five cloze deletions and set the desired retention for each individual cloze/item to 0.9^(1/5) ≈ 98%. This way, interference is still a concern, but the probability of recalling all five items correctly at the same time would approach the desired retention.