TED演讲：拥有大脑的真正原因(10)

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　　We bring in two adults. We tell them they're going to play a game.  我们找来两个成人。告诉他们来做个小游戏。

　　And so here's player one and player two sitting opposite to each other.  这里显示了玩家一和玩家二面对面坐着。

　　And the game is very simple. We started with a motor with a little lever, a little force transfuser.  游戏很简单。开始的时候这里有个电机，连着小杠杆，作为传力器。

　　And we use this motor to apply force down to player one's fingers for three seconds and then it stops.  小电机向下对玩家一的手指施力持续3秒钟后停止。

　　And that player's been told, remember the experience of that force and use your other finger to apply the same force down to the other subject's finger through a force transfuser -- and they do that.  我们告诉这名玩家一，记住这个力的感觉，再用另一个手指以同样的力，通过传力器下压玩家二的手指--玩家一这么做了。

　　And player two's been told, remember the experience of that force.  然后玩家二被告知记住这个力的感觉。

　　Use your other hand to apply the force back down.  用另一只手把同样的力施加回来。

　　And so they take it in turns to apply the force they've just experienced back and forward. 这两个玩家轮流来回施加方才所受的力。

　　But critically, they're briefed about the rules of the game in separate rooms.  不过关键的一点是这两个玩家是在不同房间里被介绍游戏规则的。

　　So they don't know the rules the other person's playing by.  所以他们互相不知道对方遵循的规则。

　　And what we've measured is the force as a function of terms.  而我们测量的是力的大小关于回合次数的函数。

　　And if we look at what we start with, a quarter of a Newton there, a number of turns, perfect would be that red line.  我们来看图，开始的时候力是四分之一牛顿，横轴是回合的次数，完美的结果应该是这条红线。

　　And what we see in all pairs of subjects is this -- a 70 percent escalation² in force on each go.  但是我们在所有实验配对中看到的都是这个--每一次施力中大小上扬70%。

　　So it really suggests, when you're doing this -- based on this study and others we've done -- that the brain is canceling the sensory³ consequences and underestimating the force it's producing.  所以确实表明，我们在运动的时候--根据这个实验和我们所做的其他实验--大脑一直在抵消感官带来的结果。

　　So it re-shows the brain makes predictions and fundamentally changes the precepts⁴.  而低估自己产生的力，所以再次说明大脑会做出预测，并且在根本上改变感知到的内容。

　　So we've made inferences, we've done predictions, now we have to generate actions.  到现在为止，我们做了推断，也做了预测，现在我们要生成动作了。

　　And what Bayes' rule says is, given my beliefs, the action should in some sense be optimal⁵. 贝叶斯法则说的是，基于置信值动作应该在某种意义上是最优的。

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