Animation: Dual-Task Interference

I made an animation for my recent neuroscience article on dual-task interference. It was nice to finally visualize a scientific idea, after making it my new year’s resolution for so long. Graphical summary of the paper in my Twitter thread.

Reflection / ๋ฐ˜์ถ”

I originally planned the mobile as a piece on metacognition, as it apparently is. Then I was about to accompany it with an essay about how we deliberate and reach a decision, which I studied during my PhD.

But while I was making it, I also realized that I had ideas like this, and others, from when I was little. I felt like I’ve only made it now because now I have more experience executing such ideas.

In that sense, the mobile could be also about growth: a larger self looking back on the little selves. Indeed, metacognition, or evaluation of one’s own thinking, can be one way for learning and growth for humans and machines. For example, if you were sure you would be invited to a friend’s birthday and if you were not, you would wonder about the reason and might learn more about what happened to the friend or to your friendship. That’s different from when you were unsure about getting the invitation to begin with, in which case being not invited wouldn’t mean much.ย Therefore, the sense of being sure, or confidence, is a form of metacognition that can help learning. Machines use confidence to learn as well: agreement between the graded sense of confidence and the all-or-none outcomes can beย mathematically expressed as “cross entropy“, which is a standard measureย used in training machines.

Back to the mobile, I debated whether to use wires or paper, but chose paper because each figure is planar. I glued several sheets together to reinforce them. If someone wants it in a more permanent form, I’d like to try 3D printing it.

์›๋ž˜๋Š” ๋ฉ”ํƒ€-์ธ์ง€์— ๊ด€ํ•œ ๋ชจ๋นŒ๋กœ ๊ณ„ํšํ–ˆ๋‹ค. ์ง€๊ธˆ๋„ ๊ทธ๋ ‡๊ฒŒ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์„ค๋ช…์œผ๋กœ๋Š” ์šฐ๋ฆฌ๊ฐ€ ์–ด๋–ป๊ฒŒ ๊ณ ๋ฏผํ•˜๊ณ  ๊ฒฐ๋ก ์— ๋„๋‹ฌํ•˜๋Š”์ง€์— ๋Œ€ํ•ด, ๋‚ด๊ฐ€ ๋ฐ•์‚ฌ๊ณผ์ • ๋•Œ ์—ฐ๊ตฌํ–ˆ๋˜ ๋‚ด์šฉ์„ ๊ณ๋“ค์—ฌ ์“ธ ์ƒ๊ฐ์ด์—ˆ๋‹ค.

ํ•˜์ง€๋งŒ ๋ชจ๋นŒ์„ ๋งŒ๋“œ๋Š” ๋™์•ˆ, ์–ด๋ฆด ๋•Œ๋„ ์ด๋Ÿฐ ์•„์ด๋””์–ด๋ฅผ ํฌํ•จํ•ด ์—ฌ๋Ÿฌ ์•„์ด๋””์–ด๋“ค์ด ์žˆ์—ˆ๋˜ ๊ฒƒ์ด ๊ธฐ์–ต๋‚ฌ๋‹ค. ์ด์ œ์•ผ ์ด ๋ชจ๋นŒ์„ ๋งŒ๋“ค๊ฒŒ ๋œ ๊ฒƒ์€, ์ด์ œ์„œ์•ผ ๊ทธ ์•„์ด๋””์–ด๋ฅผ ์‹คํ˜„ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์–ด์„œ๋ผ๋Š” ์ƒ๊ฐ์ด ๋“ค์—ˆ๋‹ค.

๊ทธ๋Ÿฐ ์˜๋ฏธ์—์„œ, ์ด ๋ชจ๋นŒ์€ ์„ฑ์žฅ์— ๊ด€ํ•œ ์ž‘ํ’ˆ์ผ ์ˆ˜๋„ ์žˆ๊ฒ ๋‹ค: ํฐ ์ž์‹ ์ด ์ž‘์€ ์ž์‹ ๋“ค์„ ๋ฐ˜์ถ”ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ฉ”ํƒ€-์ธ์ง€, ํ˜น์€ ์Šค์Šค๋กœ์˜ ์ƒ๊ฐ์— ๋Œ€ํ•œ ํŒ๋‹จ์€ ์‹ค์ œ๋กœ ์‚ฌ๋žŒ์ด๋‚˜ ๊ธฐ๊ณ„๊ฐ€ ๋ฐฐ์šฐ๊ณ  ์„ฑ์žฅํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋œ๋‹ค. ์˜ˆ์ปจ๋Œ€ ์–ด๋–ค ์นœ๊ตฌ์˜ ์ƒ์ผํŒŒํ‹ฐ์— ์ดˆ๋Œ€๋  ๊ฑฐ๋ผ๊ณ  ํ™•์‹ ํ–ˆ๋Š”๋ฐ ์ดˆ๋Œ€๋ฐ›์ง€ ๋ชปํ–ˆ๋‹ค๋ฉด, ๊ทธ ์นœ๊ตฌ๋‚˜ ์นœ๊ตฌ์™€์˜ ์šฐ์ •์— ๋Œ€ํ•ด ๋‹ค์‹œ ์ƒ๊ฐํ•ด๋ณด๊ณ  ๋ญ”๊ฐ€๋ฅผ ๋” ๋ฐฐ์šธ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ํ•˜์ง€๋งŒ ์ดˆ๋Œ€๋ฐ›์„์ง€ ์—ฌ๋ถ€๊ฐ€ ์• ์ดˆ์— ๋ถˆํ™•์‹คํ–ˆ๋‹ค๋ฉด, ์ดˆ๋Œ€๋ฅผ ๋ชป ๋ฐ›์•„๋„ ๋ณ„ ๋œป์ด ์—†์—ˆ๋‹ค๊ณ  ์—ฌ๊ธธ ๊ฒƒ์ด๋‹ค. ์ด๋ ‡๊ฒŒ ์ž์‹ ๊ฐ์€ ๋ฐฐ์›€์„ ๋„์šธ ์ˆ˜ ์žˆ๋Š” ๋ฉ”ํƒ€์ธ์ง€์˜ ํ•œ ํ˜•ํƒœ์ด๋‹ค. ์ž์‹ ๊ฐ์€ ๊ธฐ๊ณ„๋“ค์˜ ํ›ˆ๋ จ์—๋„ ์“ฐ์ธ๋‹ค:ย  ์ž์‹ ๊ฐ๊ณผ ์‹ค์ œ ๊ฒฐ๊ณผ ์‚ฌ์ด์˜ ์ผ์น˜๋„๋Š” ์ˆ˜ํ•™์ ์œผ๋กœ “ํฌ๋กœ์Šค ์—”ํŠธ๋กœํ”ผ“๋กœ ํ‘œํ˜„๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ๊ธฐ๊ณ„๋“ค์„ ํ›ˆ๋ จํ•  ๋•Œ ์ผ์ƒ์ ์œผ๋กœ ์“ฐ์ด๋Š” ์ฒ™๋„์ด๋‹ค.

๋ชจ๋นŒ๋กœ ๋Œ์•„์™€์„œ, ์ฒ ์‚ฌ๋ฅผ ์“ธ์ง€ ์ข…์ด๋ฅผ ์“ธ์ง€ ๊ณ ๋ฏผํ•˜๋‹ค๊ฐ€, ๊ฐ ์ธ๋ฌผ์˜ ๋””์ž์ธ์ด ํ‰๋ฉด์ ์ด๊ธฐ ๋•Œ๋ฌธ์— ์ข…์ด๋ฅผ ์“ฐ๊ธฐ๋กœ ํ–ˆ๋‹ค. ์ข…์ด ๋ช‡ ์žฅ์„ ํ•จ๊ป˜ ๋ถ™์—ฌ์„œ ๋‹จ๋‹จํ•˜๊ฒŒ ์„ธ์šธ ์ˆ˜ ์žˆ๋„๋ก ๋งŒ๋“ค์—ˆ๋‹ค. ๋” ํŠผํŠผํ•œ ๋ฒ„์ „์„ ์›ํ•˜๋Š” ์‚ฌ๋žŒ์ด ์žˆ๋‹ค๋ฉด 3D ํ”„๋ฆฐํŒ…์œผ๋กœ ๋งŒ๋“ค์–ด ๋ณด๊ณ  ์‹ถ๋‹ค.

YK 2019.

Fixinโ€™ ResNet / ๋ ˆ์Šค๋„ท ๊ณ ์น˜๊ธฐ

Diggin’ into neural nets.

A ResNet is a relatively new type of deep artificial neural networks that can recognize objects – the kind that finds objects in your images on Google Photos.

This illustration mixes images of a stack of sandwiches with a ResNet. The protagonist is adding a “skip connection” (blue wire) between neural layers (slices of bread). Such skip connections, inspired by biology, are the characteristic of ResNets. The skip connections allowed ResNets to be deeper than its predecessors (have a tall stack), and helped them to recognize more complex images.

Why is it good for a network to be deep? And why do skip connections help? I find it a stretch to explain them with the analogy of a sandwich, so I defer the answers to later posts. Let’s say for now that the ResNet has skip connections repeated every 2-3 layers, so it’s easy to make it deeper by stacking the same structure multiple times. That makes it look like the stacked sandwiches (see Figure 3 of the original paper).

Well, this was my first attempt at a neuroscience/AI-inspired illustration. The analogy leaves a lot to be desired, but hopefully it will get better as I try more. At least this fulfills my new year’s resolutionโ€”to post about neuroscienceโ€”on the new year’s day! ๐Ÿ™‚

์‹ ๊ฒฝ๋ง ์† ํŒŒ๊ณ ๋“ค๊ธฐ.

๋ ˆ์Šค๋„ท์€ ๋น„๊ต์  ์ƒˆ๋กœ์šด ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์œผ๋กœ, ๊ตฌ๊ธ€ ํฌํ† ์—์„œ์ฒ˜๋Ÿผ ๋ฌผ๊ฑด๋“ค์„ ์•Œ์•„๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

๊ทธ๋ฆผ์—์„œ๋Š” ๋†’์ด ์Œ“์€ ์ƒŒ๋“œ์œ„์น˜์™€ ๋ ˆ์Šค๋„ท์˜ ์ด๋ฏธ์ง€๋ฅผ ํ•ฉํ–ˆ์Šต๋‹ˆ๋‹ค. ์ฃผ์ธ๊ณต์€ “๊ฑด๋„ˆ๋›ฐ๋Š” ์—ฐ๊ฒฐ”(ํŒŒ๋ž€ ์ „์„ )์„ ์‹ ๊ฒฝ๋ง ์ธต (๋นต) ์‚ฌ์ด์— ์ถ”๊ฐ€ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ฑด๋„ˆ๋›ฐ๋Š” ์—ฐ๊ฒฐ์€ ์‹ค์ œ ๋‡Œ์—์„œ ์˜๊ฐ์„ ์–ป์€ ๊ตฌ์กฐ๋กœ, ๋ ˆ์Šค๋„ท์˜ ๊นŠ์ด๋ฅผ ๋”ํ•˜๋Š” ๋ฐ ๋„์›€์„ ์ฃผ์—ˆ์Šต๋‹ˆ๋‹ค. (์ธต์„ ๋†’์ด ์Œ“์„ ์ˆ˜ ์žˆ๊ฒŒ ํ•ด์ฃผ์—ˆ์Šต๋‹ˆ๋‹ค.)

์™œ ์‹ ๊ฒฝ๋ง์ด ๊นŠ์œผ๋ฉด ์ข‹์€์ง€, ์™œ ๊ฑด๋„ˆ๋›ฐ๋Š” ์—ฐ๊ฒฐ์ด ๋„์›€์ด ๋˜๋Š”์ง€, ์ƒŒ๋“œ์œ„์น˜์˜ ๋น„์œ ๋กœ ์„ค๋ช…ํ•˜๊ธฐ์—๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์–ด์„œ, ๋Œ€๋‹ต์€ ์ดํ›„ ๊ผญ์ง€๋“ค๋กœ ๋ฏธ๋ฃจ๋„๋ก ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์„œ๋Š” ๋ ˆ์Šค๋„ท์ด ๊ฑด๋„ˆ๋›ฐ๋Š” ์—ฐ๊ฒฐ์„ 2-3์ธต๋งˆ๋‹ค ๋ฐ˜๋ณตํ•ด์„œ ๊ฐ€์ง€๊ณ  ์žˆ์–ด์„œ, ์ƒŒ๋“œ์œ„์น˜๋ฅผ ์Œ“๋“ฏ์ด ๊ฐ™์€ ๊ตฌ์กฐ๋ฅผ ๋ฐ˜๋ณต๋งŒ ํ•˜๋ฉด ์‰ฝ๊ฒŒ ๊นŠ์–ด์ง€๋„๋ก ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ๋งŒ ์–ธ๊ธ‰ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค. (์›๋ž˜ ๋…ผ๋ฌธ์˜ ๊ทธ๋ฆผ 3์„ ์ฐธ๊ณ ํ•˜์„ธ์š”.)

๋‡Œ๊ณผํ•™/AI์— ๊ด€ํ•œ ์ฒซ ๊ผญ์ง€์˜€์Šต๋‹ˆ๋‹ค. ๋น„์œ ๊ฐ€ ์•„์ง ์–ด์ƒ‰ํ•˜์ง€๋งŒ, ๋” ์‹œ๋„ํ•˜๋ฉด์„œ ๊ฐœ์„ ํ•ด ๋ณด๊ฒ ์Šต๋‹ˆ๋‹ค. ์ ์–ด๋„ ๋‡Œ๊ณผํ•™์— ๊ด€ํ•ด ์จ ๋ณด๊ฒ ๋‹ค๋Š” ์ œ ์ƒˆํ•ด ๋‹ค์ง์„ ์ƒˆํ•ด ์ฒซ๋‚ ์— ์‹ค์ฒœํ•œ๋‹ค๋Š” ์˜๋ฏธ๋Š” ์žˆ๊ฒ ๋„ค์š”. ๐Ÿ™‚

A Forgetful Neuron & Ariadne’s Thread / ๊นœ๋ฐ•ํ•˜๋Š” ์‹ ๊ฒฝ ์„ธํฌ์™€ ์•„๋ฆฌ์•„๋“œ๋„ค์˜ ์‹คํƒ€๋ž˜

I need an aid to catch little lessons from drawing before they slip into oblivion. I feel like one of those tiny neurons, the cells that comprise the brain. Any one of them would get excited with an electrical pulse, but then would dissipate the charge and lose clue of what just happened, in just about a tenth of a second. A neuron is too short-sighted for the giant world its host lives in, where food or a mate is often not obtained with a subsecond deliberation.

I, its host, am no better though. I would be drawing one day and would be exuberant about finding a gem of an idea, but when I come back in a week or so, I would have lost the stone or its shine. Progress is often not found in my foggy horizon.

To have any hope, I will have to employ some tactics, perhaps borrowing from the neurons. A neuron fights its forgetfulness in at least two ways. It tells others what it just heard, like in a telephone game but with a more serious ambition, so that someone down the line would remember the message and remind it back when it has long forgotten what it said. It also gradually revises its phone book of synapses, the connection to other neurons, depending on who answers the call and what happens afterwards. So after enough revisions the neuron calls those neurons that answered its call or got its host goodies. The neuron thus patches its amnesia with the aid of its group and its phone book.

I may use such aids, social and material. I would discuss ideas with friends, so over time they may remind me where I was and re-excite me. They would even add their ideas on top. Attending a meet-up was a critical source of help for me to resume drawing after years of pause.

On the material side, I might look back on the trace I left to remember where I meant to go from there. I started posting works to leave the trace, but the far end of the trace gets out of sight soon. Hence a little retrospect, sorted by themes below – Ariadne’s thread I left in the labyrinth of growth.

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Expressing emotions without facial expressions
ํ‘œ์ • ์—†์ด ๊ฐ์ • ํ‘œํ˜„ํ•˜๊ธฐ

Bamboo and Morning Glory  /  ๋Œ€๋‚˜๋ฌด์™€ ๋‚˜ํŒ”๊ฝƒ
A Whole New World  /  ์ƒˆ๋กœ ์—ด๋ฆฐ ์„ธ์ƒ
Living Room Festival  / ๊ฑฐ์‹ค ์ถ•์ œ
Unlikely Allies: Squid and Whale  /  ๋œป๋ฐ–์˜ ๋™๋งน:  ๋Œ€์™•์˜ค์ง•์–ด์™€ ๊ณ ๋ž˜

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Holidays for Creatures
๋™๋ฌผ๋“ค์˜ ํœด์ผ

Halloween for Beans / ์ฝฉ์˜ ํ• ๋กœ์œˆ
Sharks Booed! / ์ƒ์–ด์˜ ํ• ๋กœ์œˆ
Cookies for Rudolph / ๋ฃจ๋Œํ”„์—๊ฒŒ ์ฟ ํ‚ค๋ฅผ

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Crosswalk Series
ํšก๋‹จ๋ณด๋„ ์‹œ๋ฆฌ์ฆˆ

Crosswalk (for Benches and Containers) / ๋ฒค์น˜์™€ ์ปจํ…Œ์ด๋„ˆ์˜ ํšก๋‹จ๋ณด๋„
Crosswalk on a Foggy Day / ์•ˆ๊ฐœ ๋‚€ ๋‚ ์˜ ํšก๋‹จ๋ณด๋„
Stroller on a Rainy Crosswalk / ๋น„ ์˜ค๋Š” ํšก๋‹จ๋ณด๋„๋ฅผ ๊ฑด๋„ˆ๋Š” ์œ ๋ชจ์ฐจ

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Studies for “Return”
“๊ท€ํ™˜”์„ ์œ„ํ•œ ์Šต์ž‘

Study 1 / ์Šต์ž‘ 1
Study 2 / ์Šต์ž‘ 2
Study 3 / ์Šต์ž‘ 3

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Taking photos as I would paint
๊ทธ๋ฆผ ๊ทธ๋ฆฌ๋“ฏ ์‚ฌ์ง„ ์ฐ๊ธฐ

Wind Chime / ํ’๊ฒฝ
Snow in Harlem / ํ• ๋ ˜์— ๋‚ด๋ฆฌ๋Š” ๋ˆˆ
Snow on Hudson River / ํ—ˆ๋“œ์Šจ ๊ฐ•์— ๋‚ด๋ฆฌ๋Š” ๋ˆˆ
Out of the Night / ๋ฐค์œผ๋กœ๋ถ€ํ„ฐ
Low Lights can Elevate / ๋†’์—ฌ์ฃผ๋Š” ๋‚ฎ์€ ๋น›
Out of the Old Window / ๋‚ก์€ ์ฐฝ ๋ฐ–์œผ๋กœ

๊ทธ๋ฆผ์„ ๊ทธ๋ฆฌ๋ฉด์„œ ์–ป์€ ์ž‘์€ ๊นจ๋‹ฌ์Œ๋“ค์ด ๋ง๊ฐ์˜ ์ €ํŽธ์œผ๋กœ ์‚ฌ๋ผ์ง€๊ธฐ ์ „์— ์žก์•„๋‘๋ ค๋ฉด ๋„์›€์ด ํ•„์š”ํ•˜๋‹ค. ๋‡Œ ์•ˆ์˜ ์กฐ๊ทธ๋งŒ ์‹ ๊ฒฝ ์„ธํฌ๊ฐ€ ๋œ ๊ธฐ๋ถ„์ด๋‹ค. ์‹ ๊ฒฝ ์„ธํฌ๋Š” ์ „๊ธฐ ์ž๊ทน์„ ๋ฐ›์œผ๋ฉด ๋“ค๋œจ์ง€๋งŒ, ์‹ญ๋ถ„์˜ ์ผ ์ดˆ๋„ ์•ˆ ๋˜์–ด ์ „๊ธฐ๊ฐ€ ์ƒˆ์–ด๋‚˜๊ฐ€๋ฉด ๋ฌด์Šจ ์ผ์ด ์žˆ์—ˆ๋Š”์ง€ ๊นŒ๋งฃ๊ฒŒ ์žŠ๊ณ  ๋งŒ๋‹ค. ์‹ ๊ฒฝ ์„ธํฌ ํ•˜๋‚˜๋Š” ๊ทธ ์ฃผ์ธ์ด ์‚ฌ๋Š” ๊ฑฐ๋Œ€ํ•œ ์„ธ์ƒ์— ๋น„ํ•˜๋ฉด ๋„ˆ๋ฌด ๊ทผ์‹œ์•ˆ์ด๋‹ค. ์ผ ์ดˆ๋„ ์•ˆ ๋˜๋Š” ๊ถ๋ฆฌ๋กœ๋Š” ์Œ์‹์ด๋‚˜ ์ง์„ ์ฐพ๊ธฐ ์–ด๋ ต๋‹ค.

์‹ ๊ฒฝ ์„ธํฌ์˜ ์ฃผ์ธ์ธ ๋‚˜๋„ ๊ทธ๋ฆฌ ๋‚ซ์ง€๋Š” ๋ชปํ•˜๋‹ค. ์–ด๋–ค ๋‚ ์—๋Š” ๊ทธ๋ฆผ์„ ๊ทธ๋ฆฌ๋‹ค ๋ณด์„๊ฐ™์€ ์•„์ด๋””์–ด๋ฅผ ์–ป์—ˆ๋‹ค๋ฉฐ ๊ธฐ๋ปํ•˜๋‹ค๊ฐ€, ์ผ ์ฃผ์ผ์ฏค ๋’ค์— ๋Œ์•„์™€ ๋ณด๋ฉด ๊ทธ ๋Œ์˜ ์–ด๋””์„œ ๊ด‘์ฑ„๊ฐ€ ๋‚ฌ๋Š”์ง€ ์žŠ์–ด๋ฒ„๋ฆฌ๊ธฐ ์ผ์‘ค๋‹ค. ๋ฟŒ์—ฐ ์‹œ์•ผ ์•ˆ์— ์•ž๊ธธ์€ ์ข€์ฒ˜๋Ÿผ ๋ณด์ด์ง€ ์•Š๋Š”๋‹ค.

์กฐ๊ธˆ์ด๋ผ๋„ ํฌ๋ง์„ ๊ฐ€์ง€๋ ค๋ฉด ๋ฌด์Šจ ์ˆ˜๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์‹ ๊ฒฝ ์„ธํฌ์—๊ฒŒ์„œ ๊ท€๋œธํ•  ์ˆ˜๋„ ์žˆ์ง€ ์•Š์„๊นŒ? ์‹ ๊ฒฝ ์„ธํฌ๋Š” ๋ง๊ฐ๊ณผ ์‹ธ์šฐ๋Š” ๋ฐฉ๋ฒ•์„ ์ ์–ด๋„ ๋‘ ๊ฐ€์ง€ ๊ฐ–๊ณ  ์žˆ๋‹ค. ํ•˜๋‚˜๋Š” ๋ฌด์–ธ๊ฐ€๋ฅผ ๋ฐฐ์šฐ์ž๋งˆ์ž ์ž์‹ ๊ณผ ์—ฐ๊ฒฐ๋œ ๋‹ค๋ฅธ ์‹ ๊ฒฝ ์„ธํฌ์—๊ฒŒ ์–˜๊ธฐํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ „ํ™”๊ธฐ ๊ฒŒ์ž„์—์„œ์ฒ˜๋Ÿผ – ํ•˜์ง€๋งŒ ๋” ์ง„์ง€ํ•œ ์ด์œ ๋กœ – ์—ฌ๋Ÿฌ ์„ธํฌ๋“ค์ด ๋‹ค๋ฅธ ์„ธํฌ์—๊ฒŒ ์–˜๊ธฐํ•˜๊ธฐ๋ฅผ ๋ฐ˜๋ณตํ•˜๋‹ค๋ณด๋ฉด, ์ž๊ธฐ๊ฐ€ ๋งํ•œ ๋‚ด์šฉ์„ ์žŠ์–ด๋ฒ„๋ฆฐ ๋’ค์—๋„ ๋‹ค๋ฅธ ์„ธํฌ๋“ค์ด ๋˜์•Œ๋ ค ์ฃผ๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” ๋ฉ”์‹œ์ง€๋ฅผ ๋ฐ›์„ ๋•Œ๋งˆ๋‹ค ์‹œ๋ƒ…์Šค๊ฐ€ – ๋‹ค๋ฅธ ์‹ ๊ฒฝ ์„ธํฌ๋กœ์˜ ์—ฐ๊ฒฐ์ ์ด – ๋‚˜์—ด๋œ ์ „ํ™”๋ฒˆํ˜ธ๋ถ€๋ฅผ, ๋ˆ„๊ฐ€ ์ „ํ™”๋ฅผ ๋ฐ›์•˜๊ณ  ๊ทธ ๋’ค์— ๋ฌด์Šจ ์ผ์ด ์ผ์–ด๋‚ฌ๋Š”๊ฐ€์— ๋”ฐ๋ผ ์กฐ๊ธˆ์”ฉ ๊ณ ์น˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฉด ๋‹ค์Œ์— ์ž๊ทน์ด ์™”์„ ๋•Œ ์‘๋‹ต์„ ํ–ˆ๊ฑฐ๋‚˜ ์ฃผ์ธ์—๊ฒŒ ์ข‹์€ ๊ฒฐ๊ณผ๋ฅผ ๊ฐ€์ ธ๋‹ค ์คฌ๋˜ ์‹ ๊ฒฝ ์„ธํฌ์—๊ฒŒ ์—ฐ๋ฝ์„ ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์‹ ๊ฒฝ ์„ธํฌ๋Š” ์ด๋ ‡๊ฒŒ ๋™๋ฃŒ๋“ค๊ณผ ์ „ํ™”๋ฒˆํ˜ธ๋ถ€์˜ ๋„์›€์œผ๋กœ ๊ฑด๋ง์ฆ์„ ๊ทน๋ณตํ•œ๋‹ค.

๋‚˜๋„ ๊ทธ๋ ‡๊ฒŒ ์‚ฌํšŒ์ , ๋ฌผ์งˆ์ ์ธ ๋ฐฉ๋ฒ•๋“ค์„ ๋™์›ํ•ด ๋ณผ ์ˆ˜ ์žˆ๊ฒ ๋‹ค. ์นœ๊ตฌ๋“ค์—๊ฒŒ ์•„์ด๋””์–ด๋ฅผ ์–˜๊ธฐํ•ด์„œ, ๋‚˜์ค‘์— ๋‚ด๊ฐ€ ์–ด๋–ค ์ƒ๊ฐ์„ ํ•˜๊ณ  ์žˆ์—ˆ๋Š”์ง€, ์™œ ๋“ค๋–  ์žˆ์—ˆ๋Š”์ง€ ๊นจ์šฐ์ณ ์ค„ ์ˆ˜ ์žˆ๊ฒŒ ํ•˜๋Š” ๊ฒƒ์ด ํ•œ ๋ฐฉ๋ฒ•์ด๋‹ค. ์นœ๊ตฌ๋“ค์ด ์ž๊ธฐ ์•„์ด๋””์–ด๊นŒ์ง€ ๋”ํ•ด ์ค„ ์ˆ˜๋„ ์žˆ๋‹ค. ๋ช‡ ๋…„ ๋™์•ˆ ์ค‘๋‹จํ–ˆ๋˜ ๊ทธ๋ฆผ์„ ๋‹ค์‹œ ๊ทธ๋ฆฌ๊ฒŒ ๋œ ๋ฐ๋Š” ๊ทธ๋ฆผ ๋ชจ์ž„์— ๋‚˜๊ฐ€๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด ํฐ ์—ญํ• ์„ ํ–ˆ๋‹ค.

๋ฌผ์งˆ์ ์œผ๋กœ๋Š” ๋‚ด๊ฐ€ ๋‚จ๊ธด ๊ทธ๋ฆผ๋“ค์„ ๋˜๋Œ์•„๋ณด๋Š” ๋ฐฉ๋ฒ•์ด ์žˆ๊ฒ ๋‹ค. ์›๋ž˜ ๊ทธ ์ด์œ ์—์„œ ๊ทธ๋ฆผ๋“ค์„ ๋ธ”๋กœ๊ทธ์— ์˜ฌ๋ฆฌ๊ธฐ ์‹œ์ž‘ํ–ˆ์ง€๋งŒ, ์‹œ๊ฐ„์ด ์ง€๋‚˜๋ฉด ์˜› ๊ทธ๋ฆผ์€ ๊ณง ์žŠํ˜€์ง€๊ฒŒ ๋งˆ๋ จ์ด๋‹ค. ๊ทธ๋ž˜์„œ ์„œ๋กœ ๊ด€๋ จ๋œ ๊ทธ๋ฆผ๋“ค์„ ์œ„์— ๋ชจ์•„ ๋ณด์•˜๋‹ค. ๋ฐฐ์›€์˜ ๋ฏธ๋กœ์— ๋‚จ๊ธด ์•„๋ฆฌ์•„๋“œ๋„ค์˜ ์‹ค๊ฐ€๋‹ฅ์ด๋‹ค.