Quartz S R
Department of Cognitive Science, University of California, La Jolla 92186-5800.
Cognition. 1993 Sep;48(3):223-42. doi: 10.1016/0010-0277(93)90041-s.
Recent interest in PDP (parallel distributed processing) models is due in part to the widely held belief that they challenge many of the assumptions of classical cognitive science. In the domain of language acquisition, for example, there has been much interest in the claim that PDP models might undermine nativism. Related arguments based on PDP learning have also been given against Fodor's anti-constructivist position--a position that has contributed to the widespread dismissal of constructivism. A limitation of many of the claims regarding PDP learning, however, is that the principles underlying this learning have not been rigorously characterized. In this paper, I examine PDP models from within the framework of Valiant's PAC (probably approximately correct) model of learning, now the dominant model in machine learning, and which applies naturally to neural network learning. From this perspective, I evaluate the implications of PDP models for nativism and Fodor's influential anti-constructivist position. In particular, I demonstrate that, contrary to a number of claims, PDP models are nativist in a robust sense. I also demonstrate that PDP models actually serve as a good illustration of Fodor's anti-constructivist position. While these results may at first suggest that neural network models in general are incapable of the sort of concept acquisition that is required to refute Fodor's anti-constructivist position, I suggest that there is an alternative form of neural network learning that demonstrates the plausibility of constructivism. This alternative form of learning is a natural interpretation of the constructivist position in terms of neural network learning, as it employs learning algorithms that incorporate the addition of structure in addition to weight modification schemes. By demonstrating that there is a natural and plausible interpretation of constructivism in terms of neural network learning, the position that nativism is the only plausible model of acquisition can no longer be defended. Indeed, I briefly discuss a number of learning-theoretic reasons indicating that constructivist models so characterized uniquely possess a number of important learning characteristics.
近期对并行分布式处理（PDP）模型的关注，部分原因在于人们普遍认为它们对经典认知科学的许多假设提出了挑战。例如，在语言习得领域，人们对PDP模型可能会削弱先天论这一观点产生了浓厚兴趣。基于PDP学习的相关论点也被用来反对福多尔的反建构主义立场——该立场导致建构主义被广泛摒弃。然而，许多关于PDP学习的主张存在一个局限性，即这种学习背后的原理尚未得到严格界定。在本文中，我从瓦利安特的概率近似正确（PAC）学习模型框架内审视PDP模型，该模型如今是机器学习中的主导模型，并且自然适用于神经网络学习。从这个角度出发，我评估PDP模型对先天论和福多尔有影响力的反建构主义立场的影响。具体而言，我证明，与许多主张相反，PDP模型在一种强有力的意义上是先天论的。我还证明，PDP模型实际上很好地阐释了福多尔的反建构主义立场。虽然这些结果乍一看可能表明，一般来说神经网络模型无法进行那种反驳福多尔反建构主义立场所需的概念习得，但我认为存在一种神经网络学习的替代形式，它证明了建构主义的合理性。这种替代形式的学习是从神经网络学习角度对建构主义立场的一种自然解读，因为它采用的学习算法除了权重修改方案外，还包括结构的添加。通过证明从神经网络学习角度对建构主义存在一种自然且合理的解读，先天论是唯一合理的习得模型这一立场便无法再得到捍卫。事实上，我简要讨论了一些学习理论方面的原因，表明如此界定的建构主义模型独特地具备许多重要的学习特征。
