Lau Hakwan C
Wellcome Trust Functional Imaging Laboratory, University College London, 12 Queen Square, London, UK.
Prog Brain Res. 2008;168:35-48. doi: 10.1016/S0079-6123(07)68004-2.
It is usually taken as given that consciousness involves superior or more elaborate forms of information processing. Contemporary models equate consciousness with global processing, system complexity, or depth or stability of computation. This is in stark contrast with the powerful philosophical intuition that being conscious is more than just having the ability to compute. I argue that it is also incompatible with current empirical findings. I present a model that is free from the strong assumption that consciousness predicts superior performance. The model is based on Bayesian decision theory, of which signal detection theory is a special case. It reflects the fact that the capacity for perceptual decisions is fundamentally limited by the presence and amount of noise in the system. To optimize performance, one therefore needs to set decision criteria that are based on the behaviour, i.e. the probability distributions, of the internal signals. One important realization is that the knowledge of how our internal signals behave statistically has to be learned over time. Essentially, we are doing statistics on our own brain. This 'higher-order' learning, however, may err, and this impairs our ability to set and maintain optimal criteria for perceptual decisions, which I argue is central to perception consciousness. I outline three possibilities of how conscious perception might be affected by failures of 'higher-order' representation. These all imply that one can have a dissociation between consciousness and performance. This model readily explains blindsight and hallucinations in formal terms, and is beginning to receive direct empirical support. I end by discussing some philosophical implications of the model.
通常认为，意识涉及更高级或更复杂的信息处理形式。当代模型将意识等同于全局处理、系统复杂性或计算的深度或稳定性。这与强大的哲学直觉形成鲜明对比，即有意识不仅仅是具备计算能力。我认为这也与当前的实证研究结果不相容。我提出了一个模型，该模型摆脱了意识预示卓越表现这一强烈假设。该模型基于贝叶斯决策理论，信号检测理论是其一个特例。它反映了这样一个事实，即感知决策的能力从根本上受到系统中噪声的存在和数量的限制。因此，为了优化表现，人们需要根据内部信号的行为，即概率分布，来设定决策标准。一个重要的认识是，关于我们内部信号如何进行统计行为的知识必须随着时间的推移而习得。本质上，我们在对自己的大脑进行统计。然而，这种“高阶”学习可能会出错，这会损害我们设定和维持感知决策最优标准的能力，我认为这对于感知意识至关重要。我概述了“高阶”表征失败可能影响有意识感知的三种可能性。这些都意味着意识和表现之间可能存在分离。这个模型很容易从形式上解释盲视和幻觉，并且开始得到直接的实证支持。最后我讨论了该模型的一些哲学含义。
