在概念学习过程中感知和模拟
Erik Weitnauer1, Robert L Goldstone1, Helge Ritter2
1Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University.
Psychological review
|July 13, 2023
概括
人类在解决问题上的创造力包括为新概念创造新的描述. 物理Bongard问题 (PBPs) 通过要求对空间动态的感知来测试这一点,新的模型 (PATHS) 显示了与人类相似的学习模式.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 人类在解决问题上的创造力依赖于为新概念构建新的描述.
- 庞加德问题 (BPs) 通过要求以有限的例子对视觉场景进行基于规则的分类来评估这种能力.
- 物理Bongard问题 (PBPs) 通过结合空间动态的感知和预测来扩展这一点.
研究的目的:
- 介绍和评估用于解决PBPs的感知和测试结构假设 (PATHS) 计算模型.
- 将PATHS模型的性能与PBP上的人类类别学习进行比较.
- 研究场景排序对人类和PATHS模型类别学习的影响.
主要方法:
- 开发了PATHS计算模型来解决物理Bongard问题.
- 向人类参与者和PATHS模型进行比较分析.
- 分析了空间和时间场景排序对类别学习表现的影响.
主要成果:
- PATHS模型证明了解决大量PBP的能力.
- 人类和PATHS模型都受到PBP中的场景排序的影响.
- 将相似的场景叠加在一起有助于类别不同时的类别学习,但当类别相同时会阻碍它.
结论:
- PATHS模型的核心原则 - - 连续感知,上下文依赖处理,假设构建和双向交互 - - 可能反映了开放式的人类类类别学习.
- 场景顺序是影响类别学习效率的关键因素,无论是在计算模型还是人类认知中.
- 了解这些动态可以让我们深入了解创造性解决问题和概念形成背后的机制.
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