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Anil Ananthaswamy 2017-12-19
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How to prove controversial consciousness theory? Ask a physicist
Anil Ananthaswamy
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一种整合的体验 Machine Headz/Getty
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K;][;_:Lv1G%`1P-U0Take a theory of consciousness that calculates how aware any information-processing network is – be it a computer or a brain. Trouble is, it takes a supercomputer billions of years to verify its predictions. Add a maverick cosmologist, and what do you get? A way to make the theory useful within our lifetime.心理学空间$}rM%^,e,v;af

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f$V+n!U4|m#?0Integrated information theory (IIT) is one of our best descriptions of consciousness. Developed by neuroscientist Giulio Tononi of the University of Wisconsin at Madison, it’s based on the observation that each moment of awareness is unified. When you contemplate a bunch of flowers, say, it’s impossible to be conscious of the flower’s colour independently of its fragrance because the brain has integrated the sensory data. Tononi argues that for a system to be conscious, it must integrate information in such a way that the whole contains more information than the sum of its parts.

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整合信息理论(Integrated information theory ,IIT)是我们描述意识的最佳方法之一。该方法由麦迪逊威斯康辛大学神经科学朱利奥·托诺尼(Giulio Tononi)发展而成,这一理论基于这样的观察,每一刻的知晓都是统整的。当你凝视一束花的时候,不可能独立于花的芳香而单独意识到花的颜色,因为大脑已经对这些感觉数据进行了集成。托诺尼认为,信息整合必须以这样的方式完成:系统的整体的信息必须大于各部分之和。心理学空间5Z[d0A N*l


The measure of how a system integrates information is called phi. One way of calculating phi involves dividing a system into two and calculating how dependent each part is on the other. One cut would be the “cruellest”, creating two parts that are the least dependent on each other. If the parts of the cruellest cut are completely independent, then phi is zero, and the system is not conscious. The greater their dependency, the greater the value of phi and the greater the degree of consciousness of the system.心理学空间Q c"q9d {'}.p

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+A-dJ.F e0The cruellest cut 最残酷的切割


Finding the cruellest cut, however, is almost impossible for any large network. For the human brain, with its 100 billion neurons, calculating phi like this would take “longer than the age of our universe”, says Max Tegmark, a cosmologist at the Massachusetts Institute of Technology.心理学空间 tW'E4l$vG lu

ZyC:\K0然而,任何的大网络系统要发现这个最残酷的切割几乎都是不可能的。麻省理工学院的一位宇宙学家特德马克(Tegmark)说,人类大脑有1000亿个神经元,计算如此之大的φ(phi)值所耗费的时间将会“超过我们宇宙的年龄”。心理学空间r}:[1PJ U


Tegmark has come up with a fast way of approximating phi. He treats each neuron in the network as a node and their interconnections as links. He assigns a thickness to each link, proportional to the strength of the interconnection. Now, imagine turning a knob so that the thinnest links fade out. The picture will look somewhat different. Try again, fading out the next-thinnest links. Continue until the single interconnected web breaks into two. Tegmark has shown that this configuration approximates the cruellest cut.




%k+vp0zY%Xl0Just a second 刚好一秒


,m ^H#__R;g0Crucially, this dramatically cuts down the time it takes to find phi. “It goes from being above the age of the universe to something quite manageable,” says Tegmark – he estimates it would take less than a second for a human brain.心理学空间2I$AL;h!r O+[rV4I

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最关键的是,这大大减少了发现φ(phi)值的时间。“它从超过宇宙的年龄走到了相当容易管理的地步。”特德马克说。他估计,计算人类大脑φ值用不了一秒的时间。心理学空间)@)MA!S1s G E

$hHJa z?j!h0“It’s cool stuff,” says Christof Koch of the Allen Institute for Brain Science in Seattle. “It’s essential if we are ever to measure phi for real systems and not just toy models with 10 or 20 binary neurons.”心理学空间/im+ml8H(JrD~

6z6X9^3JD)N&A'Y0“这是很酷的发现,”西雅图亚伦大脑科学学院的克里斯多佛·考齐(Christof Koch)说,“如果我们要测量真实系统的φ值,而不是仅仅有着10个或20个二进制神经元的玩具模型,这是非常必要的”。

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The reduced computation time means we should be able to test the theory. This can be done using things like fMRI scans to map the neural interconnections of people in varying states of consciousness – people in a coma, in dreamless sleep or looking at a bunch of flowers, for example. Applying Tegmark’s model to approximate phi in each case, we should find that it scales with the person’s level of awareness.




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The theory could also be tested in people whose brain hemispheres have been surgically separated as a treatment for epilepsy. Michael Gazzaniga of the University of California, Santa Barbara, has discussed with Tononi the idea of measuring the phi of each half of the brain. His work shows that each hemisphere retains its own consciousness and is unaware of the other half. IIT predicts that the phi for each individual hemisphere should be less than the phi for the unseparated brain. “The idea of making the measurement easier is very appealing,” says Gazzaniga.

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这个理论也能在大脑两个半球被切开的癫痫病人哪儿进行检验。圣芭芭拉加州大学的迈克尔·加扎尼加Michael Gazzaniga已经与托诺尼讨论了测量这种大脑每个一半球φ(phi)值的想法。他的研究表明,每个半球都保留着它自己的意识,而且另一半对此一无所知。IIT预言,每个单独半脑φ(phi)值应该比未分开的大脑的φ(phi)值更少。加扎尼加说:“让这个测量更加容易的想法是非常吸引人的”。心理学空间*O^XE|1rp


Once IIT is verified, phi could be used to identify people with consciousness disorders who have been misdiagnosed. For example, someone who is conscious but completely paralysed may be diagnosed as being in a minimally conscious state. Their high phi value would alert doctors to the mistake. Steven Laureys, a neurologist at the University of Liege in Belgium has worked with Tononi to find ways of calculating phi for his patients. He is excited that Tegmark is on board to share a physicist’s perspective on the project. “This is wonderful, this is what we need,” he says. “This is the problem for science—the origin of the universe and matter and how we are conscious in that universe.”心理学空间y^(Q"h&V&]!k

)c%n]$W"V,@Pek/f5{'L0一旦IIT得到验证,φ(phi)的值就可以用来识别出那些被误诊的意识障碍患者。例如,也许会将一些有意识但完全麻痹之人的意识状诊断为最小值。他们的高φ(phi)值会警告医生的错误。比利时列日大学的一位神经学家史蒂文·劳雷(Steven Laureys)已经与托诺尼(Tononi)一起找到了计算他病人φ(phi)值的方法。令他激动的是,特德马克为这个项目共享一个物理学的观点。“非常美妙,这是我们需要的。”他说,“宇宙和物质的起源,以及我们在那个宇宙中的意识是怎样的。这是科学的问题所在”。


rf|*Ljp/sN0But Laureys cautions that IIT is a long way from solving the so-called hard problem of consciousness: explaining how the neural activity of a material brain gives rise to seemingly immaterial mental life. “Truly, nobody at present understands how we go from matter to mind,” he says.心理学空间,Y#sU bQzR


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Journal reference: pre-print,arxiv.org/abs/1601.02626心理学空间.O(xx9_$_lo

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A shorter version of this article was published inNew Scientist magazineon 20 February 2016



标签: IIT 集成信息理论

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