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So for example, if you put your hand under a table and try to localize it with your other hand, you can be off by several centimeters due to the noise in sensory feedback.
比如說，如果把一只手放在桌子底下，然后在桌子上面用另一只手去對準，最后位置可能相差好幾厘米，這就是因為感官回饋里面的雜音在起作用。
Similarly, when you put motor output on movement output, it's extremely noisy.
同樣，運動神經(jīng)輸出的肌肉動作和實際輸出之間也是有很多雜音的。
Forget about trying to hit the bull's eye in darts, just aim for the same spot over and over again.
且不談扔飛鏢的時候瞄準靶心去扔，只看重復瞄準同一點的時候發(fā)生什么情況。
You have a huge spread due to movement variability.
由于每次動作都有差異，最后瞄準的結果會形成一片散點。
And more than that, the outside world, or task, is both ambiguous and variable.
更何況外界環(huán)境和要執(zhí)行的任務常常模糊和變化著的。
The teapot could be full, it could be empty.
看這個茶壺，可能是滿的，也可能是空的。
It changes over time. So we work in a whole sensory movement task soup of noise.
每次都不一樣。所以我們其實是隨時處在一大堆感官動作雜音環(huán)繞之中做動作的。
Now this noise is so great that society places a huge premium on those of us who can reduce the consequences of noise.
這種雜音相當厲害，以至于我們社會會給那些能有效減少雜音帶來的后果的人巨額獎賞。
So if you're lucky enough to be able to knock a small white ball into a hole several hundred yards away using a long metal stick,
所以在座哪位能做到像老虎伍茲那樣，用一根長金屬桿把一個小白球打進幾百米開外的洞里，
our society will be willing to reward you with hundreds of millions of dollars.
我們的社會愿意獎勵你百萬千萬的錢。
Now what I want to convince you of is the brain also goes through a lot of effort to reduce the negative consequences of this sort of noise and variability.
好，我接下來想說明的是其實我們的大腦為了減少噪音和變化性的負面影響，也做了很多工作。
And to do that, I'm going to tell you about a framework which is very popular in statistics and machine learning of the last 50 years called Bayesian decision theory.
為此，我來介紹一個在過去50年里統(tǒng)計學和機器學習方面都很常用到的架構，叫做貝葉斯決策論。
And it's more recently a unifying way to think about how the brain deals with uncertainty.
近來這個理論常被用來從整體上理解大腦如何處理這種不確定性。
And the fundamental idea is you want to make inferences and then take actions.
基本思路是先做推斷，然后做出動作。
