Domain-Creating Constraints
Domain-Creating Constraints
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DOI：
10.1111/j.1551-6709.2010.01131.x
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发表时间：
2010-09-01
影响因子：
2.5
通讯作者：
Landy, David
中科院分区：
文献类型：
--
作者：
Goldstone, Robert L.;Landy, David
对这一关于认知发展的特别问题的贡献共同提出了学习涉及形成影响后续学习的限制的方法。一个学习系统必须受到有效学习的约束，但其中一些约束本身是可学习的。要知道某样东西将如何表现，学习者必须知道它是什么类型的东西。尽管这导致以前的研究人员主张将特定于领域的约束绑定到不同的种类/领域，但一个令人兴奋的可能性是，类型/领域本身是可以学习的。当与丰富的输入相结合时，一般的认知限制可以建立领域，而不是这些领域在学习之前就一定已经存在。知识是结构化的，具有丰富的差异性，但它的“骨架”不能总是预先确定的。取而代之的是，框架可能会进行调整，以适应同时出现的模式、任务要求和目标。最后，我们认为，要让开发模型展示真正的认知新颖性，它将有助于它们超越限制灵活性的高度预处理和象征性编码。我们考虑两个学会辨别音调的物理模型。它们是保存丰富的空间、感知、动态和具体信息的机械模型，允许它们形成令人惊讶的新的假设和编码类别。
The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be learned. General cognitive constraints, when combined with rich inputs, can establish domains, rather than these domains necessarily preexisting prior to learning. Knowledge is structured and richly differentiated, but its "skeleton" must not always be preestablished. Instead, the skeleton may be adapted to fit patterns of co-occurrence, task requirements, and goals. Finally, we argue that for models of development to demonstrate genuine cognitive novelty, it will be helpful for them to move beyond highly preprocessed and symbolic encodings that limit flexibility. We consider two physical models that learn to make tone discriminations. They are mechanistic models that preserve rich spatial, perceptual, dynamic, and concrete information, allowing them to form surprising new classes of hypotheses and encodings.