Free-energy minimization and the dark-room problem
Free-energy minimization and the dark-room problem
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DOI：
10.3389/fpsyg.2012.00130
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发表时间：
2012-01-01
影响因子：
3.8
通讯作者：
Clark, Andy
中科院分区：
文献类型：
--
作者：
Friston, Karl J.;Thornton, Christopher;Clark, Andy
近年来，出现了一个重要的新的脑功能基础理论。该理论将信息论、贝叶斯、神经科学和机器学习方法带入一个单一的框架中，其总体原则是最小化惊喜（或等效地，最大化期望）。最全面的这种处理是由Karl Friston提出的“自由能最小化”公式（参见例如，Friston and Stephan，2007; Friston，2010 a，B -另见Fiorillo，2010; Thornton，2010）。这些模型的批评者提出的一个反复出现的难题是，生物系统似乎并不避免意外。我们不是简单地寻找一个黑暗的，不变的房间，并呆在那里。这就是“暗室问题”。“在这里，我们描述了这个问题，并进一步揭示了它所涉及的问题。使用与爱丁顿的《空间、时间和引力》（爱丁顿，1920年）序言相同的格式，我们将我们的讨论呈现为信息理论家（桑顿）、物理学家（弗里斯顿）和哲学家（克拉克）之间的对话。
Recent years have seen the emergence of an important new fundamental theory of brain function. This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation).The most comprehensive such treatment is the "free-energy minimization" formulation due to Karl Friston (see e.g., Friston and Stephan, 2007; Friston, 2010a,b - see also Fiorillo, 2010; Thornton, 2010). A recurrent puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber, and stay there. This is the "Dark-Room Problem." Here, we describe the problem and further unpack the issues to which it speaks. Using the same format as the prolog of Eddington's Space, Time, and Gravitation (Eddington, 1920) we present our discussion as a conversation between: an information theorist (Thornton), a physicist (Friston), and a philosopher (Clark).