概念发展的贝叶斯模型:作为构建世界模型的学习

T. Ullman, J. Tenenbaum
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引用次数: 43

摘要

贝叶斯框架有助于从计算的角度解决儿童从什么知识开始,以及他们在童年时期如何构建和适应世界模型的问题。在这个框架内,对概率生成程序层次结构的推理尤其为儿童的模型构建提供了规范和描述性的描述。我们考虑了两种经典的认知发展模式:(a)婴儿期的核心知识和(b)孩子作为科学家。我们将这两种情况下的学习解释为资源受限、具有不同原语和约束的分层贝叶斯程序归纳。我们研究了儿童可以使用什么机制来应对在潜在模型的大空间中导航的算法挑战,特别是儿童作为黑客的提议,以及如何利用最近的计算进步来实现这一提议。我们还讨论了在科学理论和直觉理论以及更广泛的生物和文化进化中统一模型构建的前景。
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Bayesian Models of Conceptual Development: Learning as Building Models of the World
A Bayesian framework helps address, in computational terms, what knowledge children start with and how they construct and adapt models of the world during childhood. Within this framework, inference over hierarchies of probabilistic generative programs in particular offers a normative and descriptive account of children's model building. We consider two classic settings in which cognitive development has been framed as model building: ( a) core knowledge in infancy and ( b) the child as scientist. We interpret learning in both of these settings as resource-constrained, hierarchical Bayesian program induction with different primitives and constraints. We examine what mechanisms children could use to meet the algorithmic challenges of navigating large spaces of potential models, in particular the proposal of the child as hacker and how it might be realized by drawing on recent computational advances. We also discuss prospects for a unifying account of model building across scientific theories and intuitive theories, and in biological and cultural evolution more generally.
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