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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.
比如说，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对准，最后位置可能相差好几厘米，这就是因为感官回馈里面的杂音在起作用。
Similarly, when you put motor output on movement output, it's extremely noisy.
同样，运动神经输出的肌肉动作和实际输出之间也是有很多杂音的。
Forget about trying to hit the bull's eye in darts, just aim for the same spot over and over again.
且不谈扔飞镖的时候瞄准靶心去扔，只看重复瞄准同一点的时候发生什么情况。
You have a huge spread due to movement variability.
由于每次动作都有差异，最后瞄准的结果会形成一片散点。
And more than that, the outside world, or task, is both ambiguous and variable.
更何况外界环境和要执行的任务常常模糊和变化着的。
The teapot could be full, it could be empty.
看这个茶壶，可能是满的，也可能是空的。
It changes over time. So we work in a whole sensory movement task soup of noise.
每次都不一样。所以我们其实是随时处在一大堆感官动作杂音环绕之中做动作的。
Now this noise is so great that society places a huge premium on those of us who can reduce the consequences of noise.
这种杂音相当厉害，以至于我们社会会给那些能有效减少杂音带来的后果的人巨额奖赏。
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兹那样，用一根长金属杆把一个小白球打进几百米开外的洞里，
our society will be willing to reward you with hundreds of millions of dollars.
我们的社会愿意奖励你百万千万的钱。
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，我接下来想说明的是其实我们的大脑为了减少噪音和变化性的负面影响，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.
为此，我来介绍一个在过去50年里统计学和机器学习方面都很常用到的架构，叫做贝叶斯决策论。
And it's more recently a unifying way to think about how the brain deals with uncertainty.
近来这个理论常被用来从整体上理解大脑如何处理这种不确定性。
And the fundamental idea is you want to make inferences and then take actions.
基本思路是先做推断，然后做出动作。
