Understanding error-driven learning in infancy and early childhood

人们每天无数次地预测接下来会发生什么。尽管这些预测通常是正确的，但有时它们最终会是错误的。预测与实际结果不匹配的情况是学习的成熟机会，因为学习者可以专注于建立一个更准确的世界心理模型。该项目探讨了这种“预测错误”如何影响人类早期的学习，重点关注学习特别重要的婴儿期。该项目测试了这样一个假设：注意到预测和观察之间的不一致可以提高幼儿在一系列环境中的学习能力，而对这种违反预期的反应的个体差异有助于后来的认知结果。尽管预测错误已经在动物模型和成人中进行了大量研究，但人们对预测错误如何影响幼儿的思维几乎一无所知，尽管理解这种重要的学习形式可能对理解学习缺陷和制定教育策略具有广泛的相关性。因此，该项目将描述个体婴儿如何从预测错误中学习的能力不同，并将利用婴儿的观察时间和探索行为以及标准化电池来索引记忆、词汇和智商等综合措施，绘制这些早期差异的纵向后果。此外，该项目将确定什么样的预测，什么样的错误，提高婴幼儿的学习能力。总的来说，这项研究将有助于揭示一种基本学习形式背后的机制，对理解儿童、成人和人工系统的知识获取具有重要意义。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Countless times each day people make predictions about what will happen next. Although these predictions are often right, sometimes they end up being wrong. Instances where predictions do not match actual outcomes are ripe opportunities for learning, because learners can focus on building a more accurate mental model of the world. This project asks how such “prediction errors” affect human learning early in life, focusing on infancy, when learning is especially important. The project tests the hypothesis that noticing an inconsistency between what was predicted and what was observed enhances young children’s learning across a range of contexts, and that individual differences in responses to such expectancy violations contribute to later cognitive outcomes.Although prediction errors have been much studied in animal models and in human adults, nearly nothing is known about how they affect thinking in very young children, even though understanding this important form of learning may have broad relevance for understanding learning deficits, and for developing educational strategies. Therefore, this project will characterize how individual infants differ from one another in their ability to learn from prediction errors, and will chart the longitudinal consequences of these early differences, using a combination of measures including infants’ looking time and exploratory behaviors, as well as standardized batteries to index memory, vocabulary, and IQ. In addition, the project will determine what kinds of predictions, and what kinds of errors, enhance learning in infants and young children. Overall, this research will help reveal the mechanisms behind a fundamental form of learning, with implications for understanding knowledge acquisition in children, adults, and artificial systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.