IIT的意识科学理论能否解释意识的难问题
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摘要
IIT理论是由G.托诺尼(G. Tononi)领衔的团队发展出来的用"整体信息"(integrated information)来解释意识及其现象特质的理论,这一理论尝试在"内部视角"下看待信息,并在以"整体信息"为意识的本体的基础上,定性定量地刻画意识的量值和其中的现象层面的特征,在其多篇论文中,托诺尼都认为该理论能够很好的解释意识的现象层面的特质,即所谓"感受质"(qualia)究竟是什么的问题,并且能够解决困扰哲学界已久的"意识难问题",本文将给出如下理由来说明其并未解决意识难问题:其一,IIT理论的"内部视角"与需要理解"感受质"是什么的"主观视角"并不一致;其二,通过信息间关系及其相应的存在空间的构造,我们至多可以有一个无法验证的,同构于现象经验本身的对应物,构造出这样的对应物并无助我们理解"感受质"本身究竟是什么;其三,IIT的3.0理论中所要求的预设不仅蕴含着"意识的难问题",还蕴含着一个和"意识的难问题"同样困难的对偶命题,IIT理论本身的成立要求难问题已经得到解决。
IIT theory constructed by Tononi, G.'s research team is intended to account for consciousness and its phenomenonal aspects with the concept of integrated information, in some of their works, Tononi deems that this theory can be well enough to explain the characters of the phenomenonal aspects within consciousness,usually named "qualia" for short, and can also handle "the hard problem of consciousness" both, which, for a long time, has still been perplexing the area of philosophy of mind. In this paper, I'll list the following reasons to clarify this theory does not solve "the hard problem of consciousness": Firstly, "the intrinsic perspective" is not the same as the "subjective perspective" which is crucial for us to understand what is qualia; Secondly, through the construction of the relationship within information and its relevant existence space, we, at most, can only have an unverifiable counterpart which, probably, isomorphic to the phenomenon experiences itself, but this counterpart still cannot help us to understand what is the ontology of qualia; Thirdly, in the IIT 3.0, the presupposition required not only implies "the hard problem of consciousness", but also an antithetic proposition as hard as "the hard problem of consciousness", IIT is, actually, founded on the solution of the "hard problem".
IIT theory constructed by Tononi, G.'s research team is intended to account for consciousness and its phenomenonal aspects with the concept of integrated information, in some of their works, Tononi deems that this theory can be well enough to explain the characters of the phenomenonal aspects within consciousness,usually named "qualia" for short, and can also handle "the hard problem of consciousness" both, which, for a long time, has still been perplexing the area of philosophy of mind. In this paper, I'll list the following reasons to clarify this theory does not solve "the hard problem of consciousness": Firstly, "the intrinsic perspective" is not the same as the "subjective perspective" which is crucial for us to understand what is qualia; Secondly, through the construction of the relationship within information and its relevant existence space, we, at most, can only have an unverifiable counterpart which, probably, isomorphic to the phenomenon experiences itself, but this counterpart still cannot help us to understand what is the ontology of qualia; Thirdly, in the IIT 3.0, the presupposition required not only implies "the hard problem of consciousness", but also an antithetic proposition as hard as "the hard problem of consciousness", IIT is, actually, founded on the solution of the "hard problem".
引文
[1]Nagel,T.'What Is It Like to Be a Bat?'[J].The Philosophical Review,1974,83(4):435-450.
[2]Levine,J.'Materialism and Qualia:The Explanatory Gap'[J].Pacific Philosophical Quarterly,1983,64(4)354-361.
[3]Chalmers,D.J.'Facing Up to the Problem of Consciousness'[A],Shear,J.(Ed)Explaining Consciousness:The“Hard Problem”[C],London:The MIT Press,1997,9-30.
[4]Robinson,W.S.'The Hardness of the Hard Problem'[A]Shear,J.(Ed)Explaining Consciousness:The“Hard Problem”[C],London:The MIT Press,1997,149-161.
[5]Crick,F.,Koch,C.'A Framework for Consciousness'[J]Nature.Neuroscience,2003,6(2):119-126.
[6]Balduzzi,D.,Tononi,G.'Qualia:The Geometr y of Integrated Information'[J].PLoS Computational Biology2009,5(8):e1000462.
[7]Bateson,G.'The Cybernetics of“Self”:A Theory of Alcoholism'[J].Psychiatry,1971,34(1)1-18.
[8]Tononi,G.,Sporn,O.'Measuring Information Integration'[J].BMC Neuroscience,2004,4(1):31.
[9]Tononi,G.'Consciousness as Integrated Information:AProvisional Manifesto'[J].Biological Bulletin,2008215(3):216-242.
[10]Tononi,G.,Balduzzi,D.'Towards a Theory of Consciousness'[A],Gazzaniga,M.(Ed)The Cognitive Neurosciences[C],London:The MIT Press,2009,1201-1217.
[11]Oizumi,M.,Albantakis,L.,Tononi,G.'From the Phenomenology to the Mechanisms of Consciousness Integrated Information Theory 3.0'[J].P L o SComputational Biology,2014,10(5):e1003588.
(1)托诺尼在论文A Framework for Consciousness中给出了信息论中的“相对熵”的另一种数学形式,即用克罗内克δ函数表示两个概率分布之间的相对熵,设系统先前可能状态的概率分布为p(xi),之后的所有可能状态中的每一个状态都可视为一个系统发生的“惊异”(surprise),即一个关于状态xi的克罗内克δ函数的作用下系统产生的自信息,香农的信息即是系统所有状态的“惊异”的数学期望。
(2)这里信息熵是描述系统混乱程度,或者可能状态的分布状态的函数。
[2]Levine,J.'Materialism and Qualia:The Explanatory Gap'[J].Pacific Philosophical Quarterly,1983,64(4)354-361.
[3]Chalmers,D.J.'Facing Up to the Problem of Consciousness'[A],Shear,J.(Ed)Explaining Consciousness:The“Hard Problem”[C],London:The MIT Press,1997,9-30.
[4]Robinson,W.S.'The Hardness of the Hard Problem'[A]Shear,J.(Ed)Explaining Consciousness:The“Hard Problem”[C],London:The MIT Press,1997,149-161.
[5]Crick,F.,Koch,C.'A Framework for Consciousness'[J]Nature.Neuroscience,2003,6(2):119-126.
[6]Balduzzi,D.,Tononi,G.'Qualia:The Geometr y of Integrated Information'[J].PLoS Computational Biology2009,5(8):e1000462.
[7]Bateson,G.'The Cybernetics of“Self”:A Theory of Alcoholism'[J].Psychiatry,1971,34(1)1-18.
[8]Tononi,G.,Sporn,O.'Measuring Information Integration'[J].BMC Neuroscience,2004,4(1):31.
[9]Tononi,G.'Consciousness as Integrated Information:AProvisional Manifesto'[J].Biological Bulletin,2008215(3):216-242.
[10]Tononi,G.,Balduzzi,D.'Towards a Theory of Consciousness'[A],Gazzaniga,M.(Ed)The Cognitive Neurosciences[C],London:The MIT Press,2009,1201-1217.
[11]Oizumi,M.,Albantakis,L.,Tononi,G.'From the Phenomenology to the Mechanisms of Consciousness Integrated Information Theory 3.0'[J].P L o SComputational Biology,2014,10(5):e1003588.
(1)托诺尼在论文A Framework for Consciousness中给出了信息论中的“相对熵”的另一种数学形式,即用克罗内克δ函数表示两个概率分布之间的相对熵,设系统先前可能状态的概率分布为p(xi),之后的所有可能状态中的每一个状态都可视为一个系统发生的“惊异”(surprise),即一个关于状态xi的克罗内克δ函数的作用下系统产生的自信息,香农的信息即是系统所有状态的“惊异”的数学期望。
(2)这里信息熵是描述系统混乱程度,或者可能状态的分布状态的函数。