Selection for Representation in Higher-Order Adaptation
Selection for Representation in Higher-Order Adaptation
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DOI：
10.1007/s11023-015-9360-3
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发表时间：
2015-02-01
影响因子：
7.4
通讯作者：
Arita, Takaya
中科院分区：
文献类型：
--
作者：
Arnold, Solvi;Suzuki, Reiji;Arita, Takaya
如果不解释认知是如何成为表象的，那么心智进化的理论就不可能是完整的。认知进化的人工近似一般不会产生表征认知。我们把这作为一个迹象，有一个差距，我们的理解是什么驱动进化代表性的解决方案，并提出了一个理论来填补这一空白。我们建议选择学习和选择二阶学习的因果因素驱动先天和后天形式的代表，分别出现。认知通常被视为一个“黑箱“，”欧元“选择只对外部可见的行为起作用，很少考虑执行结构。然而，即使实现结构不受行为选择的约束，实现结构也会影响对行为进行特定修改的难易程度。因此，学习的选择可以影响行为的执行结构。同样，学习能力本身的实现结构也不受直接选择的影响，但二阶学习的选择会影响一阶学习的实现结构。我们认为，这些间接的选择效应引导发展代表性的实现，实现结构和环境结构之间的结构对齐保证了环境中的简单变化，可以满足简单的变化，实现。我们用计算研究的例子来说明这一理论，并讨论这一理论如何有助于将表征认知置于纯粹连接主义人工智能的范围内。
A theory of the evolution of mind cannot be complete without an explanation of how cognition became representational. Artificial approximations of cognitive evolution do not, in general, produce representational cognition. We take this as an indication that there is a gap in our understanding of what drives evolution towards representational solutions, and propose a theory to fill this gap. We suggest selection for learning and selection for second order learning as the causal factors driving the emergence of innate and acquired forms of representation, respectively. Cognition is commonly viewed as a "black box"aEuro"selection works on externally visible behaviour alone, with little regard for implementation structure. Yet even if implementation structure is not constrained by selection on behaviour, implementation structure does affect how easy or difficult it is to make specific modifications to the behaviour. Hence selection for learning can affect the implementation structure of behaviour. Similarly, the implementation structure of learning ability itself is not under direct selection, but selection for second order learning can affect the implementation structure of first order learning. We argue that these indirect selection effects guide evolution towards representational implementations, as structural alignment between implementation structure and environment structure guarantees that simple changes in the environment can be met with simple changes in implementation. We illustrate the theory with examples of computational investigations, and discuss how the theory may help put representational cognition within reach of purely connectionist AI.