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HOW DO WE USE COMPUTATIONAL MODELS OF COGNITIVE PROCESSES?

机译:我们如何使用认知过程的计算模型?

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Previously I outlined a scheme for understanding the usefulness of computational models ·~1 This scheme was accompanied by two specific proposals. Firstly, that although models have diverse purposes, the purposes of individual modelling efforts should be made explicit. Secondly, that the best use of modelling is in establishing the correspondence between model elements and empirical objects in the form of certain 'explanatory' relationships: prediction, testing, existence proofs and proofs of sufficiency and insufficiency. The current work concerns itself with empirical tests of these two claims. I survey highly cited modelling papers and from an analysis of this corpus conclude that although a diverse range of purposes are represented, neither being accompanied by an explicit statement of purpose nor being a model of my 'explanatory' type are necessary for a modelling paper to become highly cited. Neither are these factors associated with higher rates of citation. The results are situated within a philosophy of science and it is concluded that computational modelling in the cognitive sciences does not consist of a simple Popperian prediction-and-falsification dynamic. Although there may be common principles underlying model construction, they are not captured by this scheme and it is difficult to imagine how they could be captured by any simple formula.
机译:之前,我概述了一种了解计算模型的有用性的方案·〜1该方案伴随着两个具体的建议。首先,尽管模型具有不同的目的,但应该明确各个建模工作的目的。其次,建模的最佳用途是以某些“解释性”关系的形式建立模型元素与经验对象之间的对应关系:预测,测试,存在性证明以及充分性和不足性的证明。当前的工作涉及对这两个主张的经验检验。我调查了被高度引用的建模论文,并从对该语料库的分析得出的结论是,尽管代表了各种各样的目的,但对于建模论文而言,既不需要附带明确的目的说明,也不必成为我的“解释性”类型的模型受到高度重视。这些因素都与较高的引用率无关。结果位于科学哲学之内,并且得出的结论是,认知科学中的计算模型并不包含简单的Popperian预测和伪造动力学。尽管可能存在一些共同的原理来构建模型,但是它们不能被该方案捕获,很难想象如何用任何简单的公式来捕获它们。

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