Active inductive inference in children and adults: A constructivist perspective.
Active inductive inference in children and adults: A constructivist perspective.
复制标题
儿童和成人的主动归纳推理：建构主义观点。
DOI：
10.1016/j.cognition.2023.105471
复制
发表时间：
2023
期刊：
影响因子：
3.4
通讯作者：
Bramley NR
中科院分区：
文献类型：
--
作者：
Bramley NR
作为人类的一个决定性方面是通过产生和适应想法和假设来推理世界的能力。在这里，我们探讨这种能力是如何发展的比较儿童和成人的主动搜索和明确的假设生成模式在一个任务，模仿开放式的过程中的科学归纳。在我们的实验中，54名儿童（8岁。97±1。11)50名成年人通过主动测试对一系列因果规则进行归纳推理。孩子们在测试行为上更加精细，对潜规则的猜测也更加复杂。我们采取“计算建构主义”的角度来解释这些模式，认为这些推论是由思维（生成和修改符号概念）和探索（发现和调查物理世界中的模式）的组合驱动的。我们展示了这个框架和丰富的新数据集如何回答关于假设生成，主动学习和归纳概括的发展差异的问题。特别是，我们发现儿童的学习是由较少微调的结构机制比成人的驱动，导致更大的想法的多样性，但不可靠的发现简单的解释。
A defining aspect of being human is an ability to reason about the world by generating and adapting ideas and hypotheses. Here we explore how this ability develops by comparing children’s and adults’ active search and explicit hypothesis generation patterns in a task that mimics the open-ended process of scientific induction. In our experiment, 54 children (aged 8. 97±1. 11) and 50 adults performed inductive inferences about a series of causal rules through active testing. Children were more elaborate in their testing behavior and generated substantially more complex guesses about the hidden rules. We take a ‘computational constructivist’perspective to explaining these patterns, arguing that these inferences are driven by a combination of thinking (generating and modifying symbolic concepts) and exploring (discovering and investigating patterns in the physical world). We show how this framework and rich new dataset speak to questions about developmental differences in hypothesis generation, active learning and inductive generalization. In particular, we find children’s learning is driven by less fine-tuned construction mechanisms than adults’, resulting in a greater diversity of ideas but less reliable discovery of simple explanations.