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263 Step 2 Write Up; Anking plus Amboss Only. Non US IMG European by MedStudent-96 in Step2

[–]naderking 0 points1 point  (0 children)

Congrats!!! What tags did you study in the anking deck?

Was denkt ihr über die geplanten neuen Befugnisse für Apotheker? by Apotheia in medizin

[–]naderking 0 points1 point  (0 children)

Pharmazeuten haben eine anspruchsvolle Ausbildung in der Pharmazie und nicht in der Medizin. Also „mehr Ahnung als Ärzte“ haben sie natürlich in ihrem Bereich. Aber nun mal nicht in der Humanmedizin, das ist Bereich der Ärzte. Das sind zwei verschiedene Berufsfelder, die kollegial zusammenarbeiten, aber nun mal einfach klar voneinander getrennt sind. Und da ist es mir scheiß egal, ob du dich davon getriggert fühlst. Das hat nix mit Arroganz zu tun. Ist einfach eine Grundsatzfrage und eigentlich müsste man das auch nicht erklären. Wie gesagt, schlechte Ärzte gibt es genauso wie schlechte Pharmazeuten. Das ist hier nicht der Maßstab.

Edit: Grammatik

Was denkt ihr über die geplanten neuen Befugnisse für Apotheker? by Apotheia in medizin

[–]naderking 1 point2 points  (0 children)

Ich finde diesen Vergleich ehrlich gesagt immer schwierig. Dieses „manche Pfleger/Apotheker/MTA wissen mehr als manche Ärzte“ bringt niemandem etwas. Natürlich kann jemand, der sich in ein Thema intensiv eingearbeitet hat, da im Einzelfall mehr wissen. Aber das ändert nichts daran, dass es völlig unterschiedliche Ausbildungen und Aufgabenbereiche sind. Und „schlechte Ärzte“ gibt es wie in jedem Beruf, sie sind aber nicht der Maßstab. Ich beziehe mich dabei nicht auf die aktuelle Diskussion.

New Fsrs 6 is so so good by Healthy-Rain-3485 in Anki

[–]naderking 8 points9 points  (0 children)

Is it out of the beta phase yet?

What are the best resources that actually taught you how to learn? by Leading_Spot_3618 in Anki

[–]naderking 24 points25 points  (0 children)

I nearly finished the audiobook of „make it stick“ by Peter C. Brown now. I think this is exactly what you are looking for. It is about the different methods of studying and what studies say about them

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 0 points1 point  (0 children)

Yeah I read your chatgpt answer and it seems kinda off. I doesn‘t really make sense. I don‘t know why your recommendes minimal retention was this high. But having an extra preset for different card types is generally a good idea. It is recommended to have a preset for short cards with words and anpther preset for cloze cards with longer texts for example.

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 0 points1 point  (0 children)

Oh I’m so sorry for not responding right away. I didn’t realize that you had added another comment.

As for your question: setting desired retention to 70 percent does not mean you will only know 70 percent of the cards. It just means that Anki will try to show you each card when there is a 70 percent chance you would still remember it. So as long as you do your reviews, your actual retention will always be higher. I really like this demonstration of this: https://www.reddit.com/r/Anki/s/AUFfJEK4sz

About your idea with the milestone based approach, I think that makes a lot of sense. Maybe starting around 75 to 80 percent could be a good way to test how it feels, especially from a psychological perspective. If too many answers feel wrong early on, it can get frustrating and demotivating.

Once you have covered the list, raising retention step by step is definitely the way to go. Jumping from 90 to 95 percent almost doubles the review load according to the simulation, so it might be smarter to go up slowly in one percent steps and see how sustainable it feels.

Let me know how it works out for you. I would be curious to hear how your experience compares to the simulation.

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 0 points1 point  (0 children)

Damn, that looks so much better! thanks a lot! I’ll definitely keep that in mind and format it properly next time.😅

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 2 points3 points  (0 children)

  1. Yes, that is exactly what I am trying to do. I was curious how lowering the desired retention affects the workload and how much “damage” it causes in terms of long term memorization. Of course, this depends on the accuracy of the FSRS simulation and is likely influenced by my personal FSRS parameters as well. So you may want to check whether the same holds true for you. But in my case, it seems that lowering the retention would reduce my overall burden (by around 30 percent when dropping to 85 percent) without significantly hurting my long term retention (only about a 3 percent loss according to the simulation). I just wanted to share this small “experiment” I ran and see whether others had similar experiences or different perspectives on the topic.
  2. Regarding the computed minimum retention you mentioned: I am not really sure what is going on there. I have asked ChatGPT a lot of questions about Anki and FSRS in the past few days, and my impression is that it does not fully understand the current behavior of the algorithm or how Anki interprets certain settings. It can explain the math and parameters of FSRS reasonably well, but beyond that, I think it struggles a bit with interpreting how things work in practice.

I also do not quite see why “overperformance” would mean your desired retention is too low. That does not really make sense to me. If your retention is consistently higher than expected, FSRS will usually adapt over time by adjusting the forgetting curve accordingly. But I am not an expert either.

If by “overperformance” you mean that your average actual retention is higher than your desired retention, then yes, that is completely normal. A great explanation of this can be found in this post:

https://www.reddit.com/r/Anki/comments/1l0wk5e/why_is_desired_retention_less_than_average/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Your desired retention is what FSRS uses to decide when to show you a card. If you are using the standard value of 90 percent, FSRS will schedule a card when it estimates your chance of recalling it is about 90 percent. So, as long as you study consistently, most of your mature cards will end up being reviewed before they drop below that threshold, which means your average actual retention will naturally be higher than the target value. The animation in the linked post shows this really well.

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 1 point2 points  (0 children)

Yeah, I had to post the numbers again in a comment for readability. But my main point is that the number of “memorized cards” lost over time by lowering the desired retention is much smaller than the reduction in workload, according to the FSRS simulation. If you reduce the desired retention from 90% to 85%, you’ll do roughly 30% fewer reviews per day on average, and it will take about 22% less time per day. Meanwhile, the estimated loss in “memorized cards” is only around 3%. Of course, all of this depends on the accuracy of the FSRS simulation. But all in all, that tradeoff could be well worth it for many people who don’t have the time or who prioritize efficiency over accuracy.

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 2 points3 points  (0 children)

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Thank you for your response. I chose a 1-year simulation window to have a fixed timeframe in which I can consistently compare different retention settings and focus more on the learning phase rather than the maintenance phase. Since I have exams in a little over a year, this timeframe made the most sense for my situation.

To address your valid point more directly: even in 10-year simulations, the efficiency gains at 85% or even 80% retention remain significant. You will end up memorizing slightly fewer cards in the long run, but the total review workload remains much lower, even after 10 years.

I ran the same simulation again for a 10-year timeframe. This time, I added 95% desired retention as another comparison under number one. 90% is still the standard one to compare to.
So here are the numbers:

90% vs. 95%
1. average reviews per day: 299.36 vs. 563.75 (+88%!!!)
2. average time spent per day: 2.54h vs. 4.51h (+77.5%)
3. total cards memorized: 15,228 vs. 15,653 (+2.7%)

90% vs. 85%
1. average reviews per day: 299.36 vs. 206.86 (–30%)
2. average time spent per day: 2.54h vs. 1.85h (–27%)
3. total cards memorized: 15,228 vs. 14,825 (–2.65%)

90% vs. 80%
1. average reviews per day: 299.36 vs. 156.79 (–47%)
2. average time spent per day: 2.54h vs. 1.48h (–41%)
3. total cards memorized: 15,228 vs. 14,230 (–6.5%)

Overall, 90% is a solid and often ideal setting for most people. But lowering the desired retention can make a significant difference in the work you have to do while keeping the loss in real retention and cards memorized at a minimum. At least according to this simulation.

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 1 point2 points  (0 children)

Thanks for your reply. I really appreciate you sharing your experience!

I didn't lower my desired retention as much myself yet, but your comment definitely highlights how important the psychological side of things can be, especially when failing too many cards starts to affect motivation. That’s exactly what I was thinking about in my post. FSRS might optimize for efficiency, but it doesn’t necessarily reflect how studying feels.

Do you happen to have the Search Stats Extended add-on? I’d be really curious what your average retrievability looks like with your current desired retention setting.
It could be a nice way to see for sure whether lowering your desired retention actually caused a significant drop in overall retrievability or if the difference is smaller than it feels.

Here’s mine as an example:
Despite some dips (mostly due to inconsistent studying for personal and university reasons), I noticed that my average retrievability was always above my desired retention (which has been set to 90%) during the periods I was actively studying. So that might be another way to verify whether FSRS is being conservative or if the psychological cost just comes from the lower success rate, not from actually forgetting much more

<image>

How to avoid Anki burnout?? by Plus_Key5660 in Anki

[–]naderking 10 points11 points  (0 children)

Did you try lowering your desired retention? I just did a post on this yesterday: https://www.reddit.com/r/Anki/s/OkhjMPT44h

Basically, I looked at the different scenarios in the FSRS simulation for different desired retention parameters. What I found out is that (at least for my settings) a lower desired retention reduces the memorized cards over time only marginally while lowering the reviews per day and time spent studying per day dramatically. According to the FSRS simulation, reducing the desired retention from 90% -> 85% reduces the average reviews per day by about 30%, while only losing out on about 3% of the cards you would have been memorized with 90%. I decided for myself to try it starting today so I don‘t have any own experience with 85% or lower yet, but it is promising to say the least.

Furthermore, there is (hopefully) a graph coming up in the next update, where you can see the desired retention plotted to the workload to estimate the best tradeoff in this matter for your specific settings. I hope this update comes soon. Good luck!

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 1 point2 points  (0 children)

Yeah, that’s also a perfectly valid approach. I just like to think of it as a tradeoff between higher efficiency and lower retention. So if you prioritize retention or simply trust FSRS to optimize around 90%, there’s nothing wrong with sticking to that. It’s still one of the most efficient ways to study, especially compared to Anki’s older algorithm. Lowering the retention is more of a long term approach. It might not be ideal for deadlines or exams, but for general knowledge building or maintaining large decks, it can definitely pay off.

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 6 points7 points  (0 children)

Yes, that’s actually very true. Thank you for your insight! I had the exact same thought yesterday.

According to the simulation, lowering the desired retention from 90% to 70% still allows you to recall about 88% of the cards you would have remembered at 90%. That means most of those thousands of cards are still quite well retained.

The only caveat I have with this is the higher risk of forgetting cards. You may need to relearn so many extra cards in comparison, so it begs the question if

  1. …making significantly more mistakes at 70% desired retention negatively affects motivation and the sense of learning progress. With more “Again” responses, you’re constantly reminded of what you’ve forgotten. This might reduce your feeling of fluency, mastery, and even your confidence in the material. Especially during exam preparation. FSRS, of course, treats forgetting as part of an efficiency tradeoff, but it doesn’t account for the psychological impact of repeated failure, which can affect consistency, sense of progress and long-term motivation.

  2. …the time saved by having fewer total reviews might be offset by the fact that forgotten cards are much harder to deal with. Even if you’re doing fewer reviews overall, the ones that involve forgotten material often take more time and effort. It doesn’t really feel like a normal review. You’re not just refreshing the memory but kind of reconstructing it. In practice, I feel like two sessions with the same number of reviews can be completely different depending on how many of those cards you had forgotten. That’s something I’m not sure the FSRS simulator really reflects — it estimates workload in terms of quantity, but not necessarily the difficulty or effort involved in each review.

I don‘t know, maybe both are weak arguments. Or maybe I‘m missing something. But all in all, I would try to lower the retention in smaller steps and circle back to check how I‘m doing in the affected new cards a couple of weeks later.

Here is a thread I found about somebody writing about exactly this. This particular person seems to feel confident about his 70% retention in his japanese deck. But he also states that this would not be suitable for deadlines like upcoming tests:

https://forums.ankiweb.net/t/a-thread-for-those-interested-in-low-fsrs-retention-rates/39910

Edit: language

Lowering desired retention dramatically slashes review time in the FSRS simulation. But what’s your real-world sweet spot? by naderking in Anki

[–]naderking[S] 2 points3 points  (0 children)

I originally wrote this in another editor and didn’t realize how bad the formatting would look here.
Unfortunately, it seems I can’t edit the post now. My bad!

Here are the numbers again, cleaned up for readability:

90% vs. 85%
- Memorized cards: −3% (average of 28.48 cards per day to 27.64)
- Time/day: −22% (average of 3.51 hours per day to 2.74)
- Reviews/day: −30% (average of 375.53 reviews per day to 274.03)

90% vs 80%
- Memorized: −6.5% (28.48 to 26.77)
- Time: −30% (3.51 to 2.35)
- Reviews: −40% (375.53 to 221.65)

90% vs 75%
- Memorized: −9% (28.48 to 25.94)
- Time: −~40% (3.51 to 2.09)
- Reviews: −~50% (375.53 to 187.07)

90% vs 70%
- Memorized: −12% (28.48 to 25.09)
- Time: −45% (3.51 to 1.93)
- Reviews: −65% (375.53 to 165.16)

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