Layering palace data for each loci in a strategic way

General Memory Chat
21

That was a solid assist from ChatGPT. Well done.

There’s a few examples there that I do similar (sometimes nigh identical) structural ideas.

And there’s a few examples that clearly showcases a difference in style (due—perhaps—to preferences, interests or already established and potent mnemonic-associations).

When I was learning Brazilian Portuguese (I stopped adding words at around 300 or so, although over 150 were verbs—in 3 palaces (for 3 different word endings/conugations) that I still have associations in and a fourth palace I’ve since forgotten the scenes for but I remember the very specific neighborhood in Dallas—where I imagined the scenes—much clearer.
Also each palace organized by alphabetical order very similar to you.

You use a lot of details about characters to encode information (I think Doug Hoff refers to this as “subject elaboration”—whereas my tendency is towards encoding meaning via imagery derived from wordplay and linguistic license (often using “vowel traversal” as Doug also wrote about in his books) including my occasionally even using the shapes of certain letters/text-characters (related to the function definitions) interactively in the scenes.

For instance, in the Collections and Sequences related functions of Python:

bytearray()
dict()
frozenset()
list()
range()
reversed()
set()
slice()
tuple()
# ^that's purely from memory but I'm pretty sure they're the only functions I have there.
# Note that I ordered them alphabetically.

frozenset():

  • Elsa from Frozen is carrying a tea set (on a particular deck in Weatherford, that’s where the palace is).
    Elsa drops the tea set
    and right nearby
    range():
    Walker the Texas Ranger (played by Chuck Norris for those unfamiliar)
    is hanging up two Wanted lists: list()
    when someone
    reversed() reverses a motorcycle (ie by walking it backwards too quickly without looking) into the Texas Ranger—the POW sound of the bike smacking into him reminds me that there’s an ending plosive sound (d) at the end so i don’t confuse it with List’s similarly named .reverse() method.
    (no ending d plosive sound)

At the table adjacent them,
set() a game of chess is being played by a regally attired monarch (not a monarch butterfly)
—against some pirates who
slice() off
the monarches toupee: tuple()

sending it flying into the next group of functions guarded by
a_cow_with_udders: aiter() pronounced like “Hey, litter” without the H and L sounds.

the aiter is wielding a halberd—which just reminds me that the cow is guarding the area. The cow’s motive is to “guard”/keep “access” to this next area where I’ve logically grouped another 7 functions:

'Logical_Category': 'Iteration and Access Functions'

aiter()
iter()
map()
next()
sort()
zip()

It begins when the pirate of slice() launches the toupee/tuple() off of set() and directly into the cow face of aiter() on the nearby deck stairs—blinding aiter() and making her and the halberd she holds teeter-totter back and forth blindly in a sort of Diablo (video game)-esque Secret Cow demon way that was never animated doing in actual Diablo games (of which one could correctly assume that I played extensively back in 1999-2000).

I use a lot of the "vowel traversal" method to find and encode mnemonics—as Doug Hoff (@thinkaboutthebible) termed it in his wonderfully comprehensive, insightful—and well researched!—book, Memory Techniques: A systematic analysis of modern mnemonics (Memory Techniques and Toolkits)

Taking liberty with the concept of Doug’s SEA-IT Data Types, more or less I see them as the primitive data types of mnemonic structures:

  • Subject
  • Elaboration
  • Action
  • Item
  • Terrain

I’m imagining a semi-corollary concept of using defined ‘Abstract Data Types’, where we can encode meaning in the specific “arrangements” of SEA-IT types.

For instance, I use objects of certain shapes to provide a “checksum” of “how many” items are contained in a list, like using a Demon-summoning Pentagon-encapsulated Pentagram (with dripping lit candles and red glowing lines) to represent 5 items in a nested list (one item for each side of the Pentagon—and in some cases, the shape-object and its sides are the terrain for the items contained within the list.

In the Pentagram example, the shape is an Elaboration indicating a checksum value and a Terrain data type (where further SEA-IT data types might be encoded).

Having an intentionally “triangular” arrangement of mnemonics (I organized mnemonics this way for the sections/subsections of Chapter 4: Stacks and Queues in Data Structures the Fun Way by Jeremy Kubica)—allows me to indicate a checksum value for myself of 3 different sections/topic (contained in the chapter).

That chapter is encoded on my second-most left toe while looking at my own feet. Arbitrarily chosen because I wanted a memory palace to memorize a Table of Contents, and my feet were in my line of sight at the time. :man_shrugging: Good enough!, and now I’ve got ‘Abstract Data Types’ mnemonically encoded onto my toes I guess.

In the same book, Doug also notes @joereddington’s concept of Skip Lists from Joe’s book Advanced Memory Palaces which I’m very interested in reading (but shamefully haven’t yet! :grimacing:).

Since I haven’t read it, I’m not familiar with any of the other concepts, but in a (methinks) similar line of thought, I’m very interested to memorize “inverted indexes.” I have something akin to an inverted index in my relational database-esque Python functions memory palace.

And it’s addition to the other “indexes” in that overall palace structure improves my ability to cross-reference and double-check myself quickly.

…golly. My phone is at 1%.

Oof.

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