Some thoughts on using "modifiers" to expand a 2-digit system to a 3-digit one

I’ve been thinking about flexibility in word / phrase construction and the use of “modifiers” when trying build out a large system.

Lots of times we see people suggest modifiers in the context of taking a 2-digit system and “easily” converting it into a 3-digit system by taking their two-digit images and adding something to them like a texture, color, physical status, emotion etc. I played around with this idea for a while myself while working on my LPAAO major system idea. I had colors as modifiers, physical states, textures, but ultimately abandoned that approach as it didn’t actually accomplish what I thought it could.

Here’s a couple examples of how modifiers are commonly suggested to be used:

37 is my friend Mike.

Lets say to modify the two-digit Mike 37 into 037, we add “frozen” as our 0xx modifier. 037 would still be Mike, but all frozen and stiff and frosty.

1xx is maybe “dead” or zombified… so 137 is a zombie version of Mike.

3xx is “yellow,” so 337 is Mike but basically completely yellow, like he’s been dipped in paint or something.

The issue I see with this is twofold.

First, you have the dreaded “problem of sameness.” With 10 different versions of Mike in your list, (and 10 Bobs and 10 Tims and 10 ducks and 10 bears) you’re setting yourself up for tons of confusion and frustration when you go to recall your images. It’s all well and good in theory to consider the 10 different versions of the toad as “easy to differentiate,” but in practice it can be very challenging.

The second issue here is one of the number of visual elements needed to encode information. If you have to specifically remember both that the person is Mike and that he is Frozen, that is two elements that you have to actively consider, encode into your image, and decode when you recall. This means you need two elements for three digits. This is less efficient than a basic 2-digit PO where you get 4 digits for 2 elements. The point of expanding from 2 digits to 3 digits is to compress more information into a single element, adding a modifier element is a step backwards in compression ratio per element.

So, is there a way to still use “modifiers” that avoids these problems?

I think so. Sort of.

In building out my 3-digit system there were a bunch of phonetics that I couldn’t find great single words for. I had to use a “modifier.” This happened for some people like 826 “FuNNy Jon” and 825 “Funny Lewis.”

You could consider something like “funny” to be a modifier, but in actuality it’s used to differentiate a person. Lets say I have a friend named JoN and I use him for an image in my system. My “funny jon” is not that same friend Jon just being funny in order to represent an additional number, its this guy:

He’s a very specific Jon who is funny who only appears once in my list. He’s not a modification of my previously used friend Jon to make that Jon into “funny jon.”

So lets say you want to use emotional modifiers to turn your 2-digit words into 3-digit ones, and you have “funny,” “angry,” " sad," “scary,” etc as your modifiers. Its fine to modify your WORDS so you get 10 different modified Mikes… “funny mike 037”, “sad mike 137,” “scary mike 237,” etc. But you should try to find specific DIFFERENT Mikes that individually fit each of these modifiers.

(This goes the same for object lists. It’s extremely tough to tell the difference between the same doll that is just modifed, but if you have distinctly different “dolls” for each of your modifiers, they become unique TRUE 3-digit images.)

So modifiers can help you easily fill in your word list, but when it comes to the images those new phrases bring to mind, ideally you would find unique, singular images that fit those modifiers. If you know or know of 10 different people named Mike, you can fit each into one of the modifier categories. Funny Mike is one mike that you know who is always cracking jokes. Or maybe its comedian Mike Myers as Austin Powers. Your “scary” Mike may be Michael Myers from the Halloween movies. So instead of just taking your friend Mike and giving him 10 different modifiers that have to be actively considered, now Funny Mike and Scary Mike are two completely distinct people.

Now you could make “sad mike” just your friend mike being sad, as long as thats the only way you picture him when memorizing and the only time you use him in your system. Because, if you have a sad mike and an angry mike, those are two elements needed per image to distinguish between the two versions of the same Mike. This is slightly different than using the same actor in different roles. I have 476 as RiCKy Gervais, and I have 147 as DeReK (a character played by Ricky Gervais.) These two characters, even though the same actor are two vastly different “people” in terms of look, demeanor, and even voice. I don’t consider this to be a repetition of people within the system. One could argue that 10 different versions of the same Mike could be looked at the same way, but I think its a pretty clear distinction. It could work if the 10 “characters” that the person is representing are VERY different from each other.

So yes, modifiers can be a really easy way to fill in a word list to go from a 2-digit to a 3-digit list, but to make it into a “true” 3-digit system, its crucial to find distinct images for them all. The bottom line is that a true 3-digit system requires 1000 distinct images that are a single element in nature. Modifiers can make the word and phrase building part go quicker, but you’ll still need to spend time determining your single element fits for them.

Note: the modifier system is different than a “category” system where each set is designated by a different attribute or category, like 0xx is Star Wars characters, 1xx is Superheroes, etc. Those systems by nature should generate fully unique single elements per 3-digit number.

Anybody have thoughts on this? Does anybody actually use an “adjective style” modifier system where they just have 10 different versions of the same person or object? If so, how effective is it for you?

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I haven’t ever thought deeply about this, but naturally incorporated it into my list. For instance, “man” is “homem” in portuguese, and we write the adjectives after the nouns, so most of my 33x people start with man: homem de lata (tin man from The Wizard Oz), homem aranha (spider man), homem calendário (calendar man), and homem fera (beast man from Masters of the Universe). I did the same thing for objects. For instance, my 33x are full of M&M products: M&M Mcflurry, M&M chocolate dispenser, M&M vending machine, and M&M pretzel.

Also, sometimes using words from my 1 or 2-digit lists was helpful to find 3-digit ones. I remember having trouble finding an image for 448, so I googled “hair” (44) and tried to find something followed by F/V, which led me to “hair velcro.” As another example, if I had trouble finding something to match 629, I could google “John” or “Johnny” and find “Johnny Bravo.”

Well said, as usual.

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There is a division within my company that works out to R.M. so I took R.M. Tom, R.M. Mike, R.M. Jake, R.M. Laura, etc to fill in most of that set of 10 people for (43x). I guess this is more category than modifier. I have several Toms and Mikes in my list, all different people, distinguished by their “modifiers.”

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I’m glad to see you discussing this, Tim - it’s something I think I’ve fell into potholes for, in trying to truncate and shorten the amount of information I need to memorise, in a way that, I’ve not been able to at the very least, make work. However, using these modifiers in the context of determining the person, rather than the inverse of taking the person and reflecting more information through a modifier, is a great use for it.

When I’m less swamped I’d like to come back to my 3-Digit system and look through your lense of “FuNNy ___” or other 2-digit modifier prefixes to see what I can come up with. It probably wouldn’t hurt to make a 2-digit adjective Major System on the back of it either.

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Mine is in here:

It was pretty tough to find easy to visualize adjectives that mapped to just the 2 consonants needed and also could be visualized. Not all of them would necessarily work well for finding a specific person but it might be a good starting point though!

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I tried something similar to what you mentioned Conceptual 2 digit -> 3 digit extension. It wasn’t a succes. I actually managed to find somewhat logical images for all 1.000 3 digit numbers, but there was so much overlap between the different images that learning them all wasn’t much fun.

One positive thing that came out this experiment is a desire for a much simpler system and because of this desire I now have 100 very strong images like a baseball pitcher or a gymnast doing a pommelhorse routine that for reasons I can’t explain I was able to learn very fast; like the 2nd day I could already translate 2 digits flash numbers at a rate of 1 second per image without any number being a problem.

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The explanations you have given above on “modifiers” apply equally as well to Tony Buzan’s (Sem cubed) too. I am not a fan of modifiers for the very reasons you’ve given, chief of which is sameness. That being said, I still don’t have a 000 - 999 system and my guess would be less than 0,1% on this forum do too. Well at least not counting the lists 000 - 999 containing modifiers?

I think, and staying with the idea of a peg list of 1 000 people, that the categorical system is the best route to take. So for numbers ending in 00 (e.g. 000, 100, 200, 300, 400, 500, 600, 700, 800, 900) the images could be soccer players. Pele = 900 (just using the first letter of his name). Ronaldo = 400 etc. For a peg list of 1 000 you would need 100 categories that make sense to you. I chose soccer players as 00 looks like 2 soccer balls. But if you think that 00 looks rather like spectacles (eyeglasses I think they are called in America), then: 000, 100, 200, 300, 400, 500, 600, 700, 800, 900 would all be famous/infamous people who wear eyeglasses. 600 could be John Lennon.

Another example of “categorical system” would be musicians as all the 45’s: 045, 145, 245, 345, 445, 545, 645, 745, 845, 945 The 45 represents a 7 single record that plays at 45rpm commonly, although no longer commonly found in jukeboxes. 645 could therefore be Johnny Cash etc.

Still coming up with 100 meaningful categories is the challenge in and of itself. Once you have them, filling them with people shouldn’t prove too difficult?

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Yeah, I think there’s an important distinction for category systems vs. modifier systems.

Category systems will (should anyway) make all your images totally unique. John Lennon would only appear once in your system. In a modifier system, usually people try to keep the same “base image” and modify it somehow, so John Lennon would appear 10 times, with 10 different modifications. Maybe there’s Bald John Lennon, or John Lennon On Fire, or Plaid John Lennon, etc. In a sense, these are “unique” images because there is a different modification for each of them, but the negatives remain with the problem of sameness, and the fact that it becomes a “non-true” 3-digit system due to the need to treat the modifiers as their own encoding elements.

One way to look at building a 1000 image category system could be to treat it as a double category system. Two digits give the broad category, then the third digit gives a more specific category that narrows down the person. If you used movies as an example, you could possibly find 100 movies or television shows or comic books that are familiar to you, and assign a number to each. It would be good obviously if there was a logical (to you) connection between the number assigned and the movie itself, but you could brute force learn the associations if needed. Then you could assign the final single digit numbers to both people and their objects in was sort of like:

0 - Protagonist
1 - Protagonist’s object
2 - Love Interest
3 - Love Interest’s object
4 - Friend/Sidekick
5 - Friend/sidekick’s object
6 - Major antagonist
7 - Major antagonist’s object
8 - Minor antagonist
9 - Minor antagonist’s object

So maybe for Star Wars, you set that movie as your 00 for some reason. You might come up with:

000 - Han Solo
001 (or 100 depending on how you want to read your category indicator numbers) - Millenium Falcon
002 (or 200) - Princess Leia
003 (or 300) - Hair Buns
004 (or 400) - Chewbacca
005 (or 500) - Ammo Bandolier
006 (or 600) - Vader
007 (or 700) - Lightsaber
008 (or 800) - Stormtrooper
009 (or 900) - Blaster rifle

Rinse and repeat for other movies or tv shows. Check your lists against each other to make sure your objects are distinct. Your people should naturally be completely different. You SHOULD be able to find unique object to associate with each person, but that may require some creativity. Like you wouldn’t want dozens of generic “guns” as villain objects. But with a little creativity and observation, this might not be too difficult.

This would give you a 1000 image list with a nice even split of 500 unique people and 500 unique objects. I like this idea if you are just looking to have a single element represent each number. Having a list with only people will make scene generation challenging. Having a mix of people and objects will allow for more vivid and memorable scene building opportunities. You may have some difficulty finding 100 movies or shows that you’re familiar with, but you only need 5 characters from each and their unique objects to fill in the grid. Obviously, if someone aspires to create a full 2000 element P/O list, this idea should maybe be avoided because it links a person and their related object across two numbers, half of which would need to be disassociated and replaced to fill in a full P/O grid.

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I see what you’re getting at but my stance of choosing a 1 000 person list and not a 50/50 split between people and objects is that you could PAO your 1 000 person pegs, hence effectively creating a 3 000 PAO list. With you suggested 50/50 split between People and Objects, how do you “turbo-charge” it to become a PAO?

You don’t, thats kinda what I was getting at:

As someone who just built and started working with a 3-digit PO list, I’m curious how you’d find 1000 actions that are unique enough to be usable. This is usually the main challenge that keeps people from a 3-digit PAO. In my opinion, PO is the way to go for such a sizable list, or possibly with 2-digit actions, so 3x2x3 PAO, although I’ve experimented with that and found that I’m MUCH faster and generate way more memorable scenes with just the P and the O as encoding elements.

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Using online resources you will use the 2-digit system you have to go to a 3-digit system using a sequence of only 10 images.


Artstation, midjourney, comics, manga, etc.

Imagine you are in the studio of a memory magician, where each number is the door to a world of vivid images. To travel from the realm of two digits to the expansive universe of three digits, we will build visual bridges. Starting with the number 00, we will invoke a series of 10 magical scenes, transforming 00 into a 10-step ladder to the three-digit dimension: 00 becomes 010, then 020, 030, and so on until 090.

Now, if your mind wants to explore the last corner of the number 09, imagine a special path where each step takes you to a deepening of this category: 09 transforms into 019, 029, 039, advancing up to 099. Each combination of numbers is a unique painting, full of characters, colors and emotions.

Once our adventure takes us to the mystical 99, we will prepare for the great leap towards the legendary 999. This journey is not just a numerical expansion, but an invitation to explore a cosmos of connections, where each number is a story, and each History is a universe in itself.

This approach not only expands our numerical categories, but transforms the process into a creative odyssey, where memorizing becomes an act of limitless imagination.

You have a good point about coming up with 1 000 unique actions. Would a PO System with a 1 000 numbers (i.e. from 000 - 999), only allow you to put a PO image of XXX (for person) and YYY (for object). I can already do that with my 100 character PAO - Dominic System (i.e. 00 - 99), only coding the mental images at each loci as a 6 digit XX-YY-ZZ.

Does that not bring you back to the same thing you are doing with a 1 000 PO System? Just asking as I haven’t put that to the test but you obviously have.

In summary, a 1 000 larger PO system is neat once learnt to place a 6 digit number at a loci as a XXX-YYY PO combination, whilst the smaller 100 PAO system places the same 6 digits at a loci, only uses a XX-YY-ZZ PAO combination.

Kind of, but with a small and maybe important difference. Yes, the end result is the same 6 digits in one scene at one location. The difference is in how many “intentional elements” are needed to encode those. You have to intentionally consider and include three specific element details in your 2/2/2 PAO scene. I only have to intentionally consider and encode two with my 3/3 PO.

Now, with my scenes I always have some kind of action or interaction between/with the PO elements, so am I expending the same amount of effort as someone who uses a 2/2/2 PAO with the action as an intentional element?

I don’t think so…

I’ve been thinking about this a bit and it brings up a possibly interesting discussion topic related to the idea of “intentional elements” and “incidental details” within mnemonic scenes. I started to explore this HERE but there may be a chance to elaborate and think about it some more. I’ll start a new thread for it so as to give it some better visibility.

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I had a thought of Creating a 3 digit system based off 2 digit but id use the first digit as a cooler. However, I think this is a bad idea because id have to think on colours.

Somehow I got the idea of a more integrated use of modifiers as is demonstrated in the example below; the bicycle is a possible 2 digit number translation image and the modifiers are:

  • image flying over X (2 digit number image) and dropping …
  • object on X
  • female person attacking X with …
  • object
  • male person attacking X with …
  • object
  • male person in background with …
  • object
  • female person in background with …
  • object

As good an idea this may be for creating a 3 digit system, it may be also used to create a possibly even better 2 digit system. Let’s start by removing the background persons, since they are not the strongest part of the green square combined image.

Based on the idea that 10 digits per location is already very good and anything beyond that is overkill, I suggest using the following categories:

  • prime object (piano)

  • flying object (bird)

  • dropped object (person, could also be an object of course)

  • male attacker with object (dwarf with ax)

  • female attacker with object (female baseball batter)

So to summarize this method gives a possibly easy 10 digits per location method that requires 500 different images to be learned.

100 % sci fi version:

You’re halfway to the number of unique images needed for a true 3-digit system. Might as well just go get them and then you can get even more bang for your buck per scene.

For me it is always about the number of “intentional elements” per scene needed to encode X digits. I don’t really get the appeal of packing more elements or element types into a single scene unless you are trying to eliminate or reduce the number of loci you need to use to order them. But even still, the more complex the structure of your scene, the more complex it will be to create and recall. I don’t really see these expansively structured scenes as being useful if the goal is speed.

However, I do like them a lot for long term projects that don’t have a time crunch. This approach would work pretty well for something like memorizing a long sequence like Pi, but even there, and maybe especially there, you’d need a way of linking scenes to keep the sequence correct. Normally that would be via sequential loci, and again, sometimes its just easier to use more loci and simplify the scenes. But, not everyone visualizes the same way and some folks have a really hard time with spacial sequential visualization. So whatever works for you works!

I’d really like to see you pick one or two of these ideas and run hard practicing them to get some comparitive info about how effective they are. Maybe do the first 500 digits with this one, compared to the next 500 as a more traditional PAO or PO, then 500 with one of your other system ideas.

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There is a big difference in the effort in trying to come up with and learning 500 images as opposed to 1.000 images. The low hanging fruit images are going to be in those first 500 collection. I mean there is a reason for the shadow system; it requires half of the images of the Ben system, but almost with the same advantages.

But of course if you succeed in learning 1.000 images and you can translate them fast, you have a big advantage over anyone who has a 2 digit system.

But even still, the more complex the structure of your scene, the more complex it will be to create and recall. I don’t really see these expansively structured scenes as being useful if the goal is speed.

In the method I suggested there is not much complexity (depending on how I define it) as everything is pretty much predetermined, however the problem will likely be in getting to translate the 2 digit numbers fast into the various categories.

I think we agree on the idea that having multiple images in one location is not going to make you faster, but only allows for needing less locations. Since I am only interested in being fast at 100 or so numbers (or 1 deck of cards) I personally have not much motivation to try such a system right now.

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I see your last example as a template for maybe something like:

Base object being assaulted
Person 1 drops object 1 on base object like in a bombing run
Person 2 attacks base object with object 2
Person 3 rescues object 3 from base object.

This differentiation between attacking and rescuing could provide some needed distinction to the scene. That way each active person is doing a different thing to the base object. Bombing, attacking, rescuing.

It could end up like “captain kirk is bombing the obelisk with a teddy bear while seven-of-nine attacks it with a shopping basket, while predator reaches into it and rescues a microwave oven from inside of it.”

Thats pretty memorable. If you have 2-digit location pegs you could even have that set the scene to give you 14 digits per scene…

“At a drive-in movie theater there is an obelisk…”

At the end of the scene in, order to connect your sequences, you could have the “rescuing person” escape to the next representational location, where the next scene would begin.

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Sounds like it might work really well. Your version is better than mine. If you want to make things more divers and perhaps more complicated :grinning:, you could use different screen plays for different locations, but with the same elements. So for example first location is your attack and rescue play and then we get an attack and revenge play or a the attackers being attacked play.

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Good thinking Tim. I think your version of making each object or person as different as you can should help decreased interference. But I also think a “duck” is a “duck” no matter how you color or visualize it (daffy duck vs a wild duck), so I think even with your improved ideas there will be some interference that would make the 3 digit system less efficient (than a 2 digit system) in terms of making three digits into a single person or object, the latter being the best outcome.

So why do we have this problem with the Major system and making one word out of three digits? It is because the Major system is imperfect. Incidentally, the three digit examples you gave of where you had to turn three digits into more than one word, I had no trouble turning several of your examples into one visualizable word. But I suspect you could also help me on some of the ones that I have trouble converting. I keep saying for the last forty years or so that we should try to create a number- consonant system, or number-letter code that is more perfect in making single words. I have tried to get CHat Bing to help me with that but it is oblivious. The prompt might be to ask GPT or CHat GPT to make up a consonant, or letter: number system that would allow the converting of the largest number of English words. One could have it use a list of highly concrete words as described by Pavio, Madigan, and Yueille where they give 625 noun rated according to concreteness and several other dimensions. Better to have a list of 10K. THe best system would be the one that could code the largest number of these rated Nouns. In case you have not noticed I am not 100% convinced that persons are easier to recall than objects, at least for me.

Though I have been obsessing about a new Letter-Number system for years that would fit three digits better than the present one, I have made little progress. The archives may reflect some earlier posts by me on this subject though I sensed at the time a few folks did not understand what I meant because they are so enmeshed in the current consonant-number system.

ALso in the past hasn’t someone tried to make a list of 1000 Major system pegs with objects on this forum? I know people have asked about certain 3 digit combos and did anyone have a word for those, and I have always tried to chip in on these questions. So if you are not stuck on the person theme, I would suggest 137 tomahawk, 037 Smack, or smuck, smug, 147 truck, track, 476 rickshaw. These came to me immediately, but 467 gives me a little trouble, maybe Rorschach-does not quite fit. Maybe “recheck.” Or “reject,” the latter not being ideal since not concrete.