Department of Computer Science, Sapienza University of Rome.
Top Cogn Sci. 2013 Jan;5(1):56-88. doi: 10.1111/tops.12002.
A U-shaped curve in a cognitive-developmental trajectory refers to a three-step process: good performance followed by bad performance followed by good performance once again. U-shaped curves have been observed in a wide variety of cognitive-developmental and learning contexts. U-shaped learning seems to contradict the idea that learning is a monotonic, cumulative process and thus constitutes a challenge for competing theories of cognitive development and learning. U-shaped behavior in language learning (in particular in learning English past tense) has become a central topic in the Cognitive Science debate about learning models. Antagonist models (e.g., connectionism versus nativism) are often judged on their ability of modeling or accounting for U-shaped behavior. The prior literature is mostly occupied with explaining how U-shaped behavior occurs. Instead, we are interested in the necessity of this kind of apparently inefficient strategy. We present and discuss a body of results in the abstract mathematical setting of (extensions of) Gold-style computational learning theory addressing a mathematically precise version of the following question: Are there learning tasks that require U-shaped behavior? All notions considered are learning in the limit from positive data. We present results about the necessity of U-shaped learning in classical models of learning as well as in models with bounds on the memory of the learner. The pattern emerges that, for parameterized, cognitively relevant learning criteria, beyond very few initial parameter values, U-shapes are necessary for full learning power! We discuss the possible relevance of the above results for the Cognitive Science debate about learning models as well as directions for future research.
认知发展轨迹中的 U 型曲线是指一个三步过程:表现良好,然后表现不佳,然后再次表现良好。U 型曲线在各种认知发展和学习情境中都有观察到。U 型学习似乎与学习是一个单调的、累积的过程的观点相矛盾,因此对认知发展和学习的竞争理论构成了挑战。语言学习中的 U 型行为(特别是在学习英语过去时)已成为认知科学关于学习模型的争论中的一个核心话题。拮抗模型(例如,连接主义与先天论)通常根据其建模或解释 U 型行为的能力来进行评判。先前的文献主要致力于解释 U 型行为是如何发生的。相反,我们对这种看似低效的策略的必要性感兴趣。我们在(扩展的)Gold 风格计算学习理论的抽象数学环境中提出并讨论了一系列结果,以解决以下问题的数学精确版本:是否存在需要 U 型行为的学习任务?所有考虑的概念都是从正数据中进行极限学习。我们提出了在经典学习模型以及在学习者记忆有限制的模型中 U 型学习的必要性的结果。结果表明,对于参数化的、认知相关的学习标准,除了极少数初始参数值外,U 型曲线对于完全学习能力是必要的!我们讨论了上述结果对关于学习模型的认知科学争论以及未来研究方向的可能相关性。