The Development of Categorization

项目摘要 建议的项目旨在提高我们对类别学习发展的理解- 人类智能的基本组成部分。该研究基于以下假设： 多种机制辅助类别学习：（a）早期发展机制（机制1）是 基于类内统计的分布式注意和学习;（B）后期开发机制 （机制2）基于对类别相关信息的选择性注意。在机制1下， 相关和不太相关的维度被编码，学习的表示与输入相似 刺激结构（即，没有基于学习的表征变化）。在机制2下，学习 表征与输入刺激结构不同（我们将这种差异称为基于学习的 代表性变化）。机制1和机制2之间的差异将在许多方面发生 性能指标，包括学习过程中的注意力分配，特征记忆，学习形状 曲线、准确度和响应时间。为了验证这些假设，我们将 在4-9岁的儿童和成人中进行一系列实验。拟议项目包括以下内容 具体目标。具体目标1是进行横断面实验和纵向研究， 跨发展的类别学习机制。机制1和机制2定性预测 不同的模式（1）注意力分配，分类和记忆（2）学习前和学习后 类别的表示。具体目标2是将计算模型应用于研究1中收集的数据 以确定类别学习的两种假设机制之间的差异。实现这一 目标，眼睛注视和分类，记忆选择和反应时间数据将提交给一套 模型捕捉假设的机制。拟议的项目将促进我们对联系的理解 在典型的认知发展的关键方面-选择性注意，类别学习和记忆。

Project Summary The proposed project is designed to advance our understanding of the development of category learning – a fundamental component of human intelligence. The proposed research is based on the hypothesis of multiple mechanisms sub-serving category learning: (a) an early developing mechanism (Mechanism1) is based on distributed attention and learning of within-category statistics and (b) later developing mechanism (Mechanism2) is based on selective attention to category-relevant information. Under Mechanism1, both relevant and less relevant dimensions are encoded, with learned representations being similar to the input stimulus structure (i.e., no learning-based representational change). Under Mechanism2, learned representations are different from input stimulus structure (we refer to this difference as learning-based representational change). Differences between Mechanism1 and Mechanism2 will transpire in many performance indicators, including attention allocation during learning, memory for features, shapes of learning curves, and accuracy and response times during training and testing. To test these hypotheses, we will conduct a series of experiments with 4-9-year-old children, and adults. The proposed project has the following Specific Aims. Specific Aim 1 is to conduct cross-sectional experiments and a longitudinal study examining mechanisms of category learning across development. Mechanism1 and Mechanism2 predict qualitatively different patterns of (1) attention allocation, categorization, and memory and (2) pre- and post-learning representations of categories. Specific Aim 2 is to apply computational models to data collected within Study 1 to identify the differences between the two hypothesized mechanisms of category learning. To achieve this goal, eye gaze and categorization and memory choice and response time data will be submitted to a suite of models capturing the hypothesized mechanisms. The proposed project will advance our understanding of links among critical aspects of typical cognitive development – selective attention, category learning, and memory.