论文标题
类型的人类学习和推论中的理论
Type theory in human-like learning and inference
论文作者
论文摘要
人类可以为新颖的疑问提供合理的答案（Schulz，2012）：如果我问您想在午餐时吃什么样的食物，您会用食物而不是时间回答。想到人们会“下午4点后”回应“您想吃什么”是个玩笑，要么是一个错误，而且作为午餐选项的认真娱乐可能永远不会发生。同时了解人们如何提出遵守新搜索空间基本限制的新想法，思想，解释和假设对认知科学至关重要，但这种推理没有同意的正式模型。我们建议，任何此类推理系统的核心组成部分都是一种类型的理论：对代理商可以执行的计算类型的结构形式强加以及它们的执行方式。我们通过三个经验观察来激发这一建议：对学习和推论的适应性限制（即产生合理的假设），人们如何在不可能和不可能之间划分不可能的能力，以及人们在不同水平的抽象水平上对事物进行推理的能力。
Humans can generate reasonable answers to novel queries (Schulz, 2012): if I asked you what kind of food you want to eat for lunch, you would respond with a food, not a time. The thought that one would respond "After 4pm" to "What would you like to eat" is either a joke or a mistake, and seriously entertaining it as a lunch option would likely never happen in the first place. While understanding how people come up with new ideas, thoughts, explanations, and hypotheses that obey the basic constraints of a novel search space is of central importance to cognitive science, there is no agreed-on formal model for this kind of reasoning. We propose that a core component of any such reasoning system is a type theory: a formal imposition of structure on the kinds of computations an agent can perform, and how they're performed. We motivate this proposal with three empirical observations: adaptive constraints on learning and inference (i.e. generating reasonable hypotheses), how people draw distinctions between improbability and impossibility, and people's ability to reason about things at varying levels of abstraction.
