10000 People System

10000 people memory system

Here are 2 ideas for making a 10,000 person system. The first is the more complex and will likely never happen because I would need collaboration to build it...

Many Mnemonics are built on visual imagery. Imagining people can be useful because a story can be imagined where that person does an action to an object or person and thus the visualised story grows; that story can represent data such as the digits of numbers or points for an essay plan. In short, people are good for mnemonics.

I have a plan for building a system of 10,000 people. So, if someone asked, "Who is person 8347?" then I should be able to provide the answer. This is done by representing 8347 as 83;47 where the 83 is going to give me a first name and the 47 is going to give me a surname. The following tables make this idea clearer:

This table concerns first names:

Digits	Syllable	Name	Gender	Ethnicity	Features
0	WI	William	Male	European	Young and Hair Short
1	A	Adam	Male	European	Young and Hair Short
2	AL	Al	Male	European	Young and Hair Short
3	AN	Andrew	Male	European	Young and Hair Short
4	AR	Arnold	Male	European	Young and Hair Short
5	BA	Barry	Male	European	Young and Hair Short
6	BE	Ben	Male	European	Young and Hair Short
7	BI	Bill	Male	European	Young and Hair Short
8	BL	Blair	Male	European	Young and Hair Short
9	BO	Bob	Male	European	Young and Hair Short
10	BR	Brian	Male	European	Young and Hair Short
11	BU	Buck	Male	European	Young and Hair Short
12	CA	Cary	Male	European	Young and Hair Short
13	CE	Cecil	Male	Euro 2	Young and Hair Long
14	CI	Cieran	Male	Euro 2	Young and Hair Long
15	CO	Colin	Male	Euro 2	Young and Hair Long
16	CR	Craig	Male	Euro 2	Young and Hair Long
17	CU	Curtis	Male	Euro 2	Young and Hair Long
18	DA	Dan	Male	Euro 2	Young and Hair Long
19	DE	Dennis	Male	Euro 2	Young and Hair Long
20	DI	Dillon	Male	Euro 2	Young and Hair Long
21	DO	Don	Male	Euro 2	Young and Hair Long
22	DR	Drew	Male	Euro 2	Young and Hair Long
23	DU	Duncan	Male	Euro 2	Young and Hair Long
24	E	Eamon	Male	Euro 2	Young and Hair Long
25	EL	Elton	Male	Euro 2	Young and Hair Long
26	EN	Enrico	Male	Euro 2	Young and Hair Long
27	ER	Eric	Male	Euro 2	Young and Hair Long
28	ES	ErneSt	Male	Euro 2	Young and Hair Long
29	FA	Fabian	Male	Euro 2	Young and Hair Long
30	FE	Felix	Male	Oriental	Middle Aged and Hair Short
31	FI	Fidel	Male	Oriental	Middle Aged and Hair Short
32	FL	Florien	Male	Oriental	Middle Aged and Hair Short
33	FO	Forrest	Male	Oriental	Middle Aged and Hair Short
34	FU	Fu	Male	Oriental	Middle Aged and Hair Short
35	GA	Gary	Male	Oriental	Middle Aged and Hair Short
36	GE	Gerald	Male	African	Old
37	GI	Gideon	Male	African	Old
38	GL	Glen	Male	African	Old
39	GO	Gordon	Male	African	Old
40	GU	Guss	Male	African	Old
41	HA	Harry	Male	African	Old
42	HE	Henry	Male	African	Old
43	HI	Hillary	Male	Indian/South East Asian	Middle Aged and Hair Long
44	HO	Horace	Male	Indian/South East Asian	Middle Aged and Hair Long
45	HU	Hugh	Male	Indian/South East Asian	Middle Aged and Hair Long
46	I	Ira	Male	Indian/South East Asian	Middle Aged and Hair Long
47	SH	Shane	Male	Indian/South East Asian	Middle Aged and Hair Long
48	IN	Indie	Male	Indian/South East Asian	Middle Aged and Hair Long
49	IS	Izzie	Male	Indian/South East Asian	Middle Aged and Hair Long
50	JA	Jane	Female	European	Young and Hair Short
51	JI	Jill	Female	European	Young and Hair Short
52	JO	Joanna	Female	European	Young and Hair Short
53	KA	Kate	Female	European	Young and Hair Short
54	KE	Kerry	Female	European	Young and Hair Short
55	KI	Kitty	Female	European	Young and Hair Short
56	LA	Laura	Female	European	Young and Hair Short
57	LE	Leslie	Female	European	Young and Hair Short
58	LI	Lilly	Female	European	Young and Hair Short
59	LO	Loren	Female	European	Young and Hair Short
60	LU	Lucy	Female	European	Young and Hair Short
61	MA	Mary	Female	European	Young and Hair Short
62	ME	Melanie	Female	European	Young and Hair Short
63	MI	Michelle	Female	Euro 2	Young and Hair Long
64	MO	Mona	Female	Euro 2	Young and Hair Long
65	MU	Muriel	Female	Euro 2	Young and Hair Long
66	NA	Natalie	Female	Euro 2	Young and Hair Long
67	NE	Nellie	Female	Euro 2	Young and Hair Long
68	NI	Nicky	Female	Euro 2	Young and Hair Long
69	NO	Noreen	Female	Euro 2	Young and Hair Long
70	NU	Nu	Female	Euro 2	Young and Hair Long
71	O	Olivia	Female	Euro 2	Young and Hair Long
72	PA	Pam	Female	Euro 2	Young and Hair Long
73	PE	Penny	Female	Euro 2	Young and Hair Long
74	PH	Philippa	Female	Euro 2	Young and Hair Long
75	PI	Pippa	Female	Euro 2	Young and Hair Long
76	PL	PauLa	Female	Euro 2	Young and Hair Long
77	PO	Polly	Female	Euro 2	Young and Hair Long
78	PU	Pu	Female	Euro 2	Young and Hair Long
79	RA	Rachel	Female	Euro 2	Young and Hair Long
80	RE	Renata	Female	Oriental	Middle Aged and Hair Short
81	RI	Rita	Female	Oriental	Middle Aged and Hair Short
82	RO	Rose	Female	Oriental	Middle Aged and Hair Short
83	RU	Ruth	Female	Oriental	Middle Aged and Hair Short
84	S	Stella	Female	Oriental	Middle Aged and Hair Short
85	SA	Sally	Female	Oriental	Middle Aged and Hair Short
86	SE	Selena	Female	African	Old
87	SI	Simone	Female	African	Old
88	SO	Sophie	Female	African	Old
89	SU	Susan	Female	African	Old
90	TA	Tania	Female	African	Old
91	TE	Terry	Female	African	Old
92	TH	Theresa	Female	African	Old
93	TI	Tina	Female	Indian/South East Asian	Middle Aged and Hair Long
94	TO	Tori	Female	Indian/South East Asian	Middle Aged and Hair Long
95	TR	Tracy	Female	Indian/South East Asian	Middle Aged and Hair Long
96	TU	Tulip	Female	Indian/South East Asian	Middle Aged and Hair Long
97	U	Una	Female	Indian/South East Asian	Middle Aged and Hair Long
98	W	Wanda	Female	Indian/South East Asian	Middle Aged and Hair Long
99	WE	Wendy	Female	Indian/South East Asian	Middle Aged and Hair Long

'Hair Long' for a man is hair below the ears or hair which is several centimetres in length and perhaps unkempt.

'Hair Long' for a woman is hair at or below the shoulders (and so not hair in a bun shape).

First name 83 is Ruth. Surname 47 is Shane. So the person is Ruth Shane.

83, as a first name, is a female who is of ethnicity Oriental. So, the 83 tells me what the person vaguely looks like.

The ethnicities are vague as well. If someone is dark-skinned and could come from the Middle East then I might group that person in the same category as 'Indian'; because 'Indian' is just a short word to emphasise that the person is not a dark African or a European white person or an oriental person. It is always a rough approximate term and never meant to be precise.

I just want to speedily categorise the photos which I will encounter so that this system becomes a reality.

Another theme is to not disguise faces much. eg. Sunglasses obscure face detail and I would steer away from that sort of generalisation of a face.

These are the 5 'ethnicity' types:

European (black or dark brown hair).

European type 2 (NOT black hair and NOT dark brown hair).

Oriental (from countries like China or Thailand).

Indian or dark Middle Eastern.

African (from countries like Nigeria or DRC).

The surname is Shane and the features and age of a person with that surname are: 'Middle Aged and Hair Long' - you can see this in the table above.

So a name suggests how someone should look. What is then required is a way to find a photograph or image of a person who fits that loose description.

Since it is not based on the English language, this system should be easy to teach to anyone in the world.

Photofit effect

My aim is to use public domain images as inspiration for visualising faces. There is no point in me struggling to mentally recall an unmemorable face. I want to learn a photofit generalised version of the faces which I am going to use. If that photofit was shown on a crime reconstruction tv show then I would expect it to match a lot of face profiles. I do not know the correct term for this generalised face - a 'face type' perhaps. And if I showed that image to the person who inspired it then they might categorically protest that it looks nothing like them. And, if the public domain image was no longer online, I could perhaps find a similar type of face and use that as a rough model of the 'face type'.

Clothing and mathematics

Like with the 100 math people, the clothing of the people may be a code for some mathematical result. eg. 8347 might be a person whose clothes give part of the anser to '83 divided by 47' .

Simpler 10000 People System

There is a lot of interest, in the memory hobbyist community, in memorising playing cards.

One way of doing well at memorising cards is to use one visual image to represent 2 cards.

So, for motivation's sake, a 10,000 system of people would not necessarily be built by presenting image 0001, 0002, 0003, onwards but by learning the ones which would be a useful cross-section of a 10,000 system.

I believe that a 2-cards-to-1-image system of interest would be one where the image for, say, 9 of Hearts, 3 of Clubs is based on representing most of that data in a 52 x 26 system where the 9 of Hearts is memorised along with the fact that card 2 is a black '3 numeral' card. It would be the next aspect of the visual memorisation story which clarifies the identity of that card - if that is desired. The system is small enough to keep someone motivated to learn it; and could lead later to the undertaking of a 52 x 52 system of card pairs.

As for the people and their numbering, I am planning to strip the names from the people; the reason for choosing celebrities is principally because I could publish a list of celebrities and interested readers could easily find a library of images for those people by using a search engine.

I have a list of 100 celebrities already in my 'Numbers in Images' article.

Person 01 is Adam Sandler. Person 00 is Will Smith.

It seems like an organised idea to me that people 0000 to 0099 might be male people with dark skin and dark hair like some would describe Will Smith. People 0100 to 0199 might be male people with lighter skin because Adam Sandler might be said by people to tend towards a light skin colour.

In fact, I might have to modify my original 100 celebrity article if I find that I have spare 100 images of oriental male people and no celebrity left who is oriental for them to be associated with.

However, I would give each person in the system a brief alias built from the following idea:

each 2-digit number from 00 to 99 has a matching syllable. eg. BA = 05 and AL = 02 .

If I give someone the nickname BAAL then that person is person 0502 (BA.. = 05, ..AL = 02 ) .

Once the system is big enough to achieve a purpose such as memorising cards, I can 'broaden my horizons' and use public domain images of people in general to represent other values in the 10000 people system.

I should present the caveat that it is sometimes contentious to categorise someone as belonging to a category based on how they are perceived to look; and my categorisation method is rough and a just a mechanism for better recalling photos of people in association with their sequence number.