新兴的神经动力学和几何学在一个过渡性推理任务中进行概括.
Kenneth Kay1,2,3, Natalie Biderman1,4, Ramin Khajeh1,2
1Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America.
PLoS computational biology
|April 25, 2024
概括
神经网络可以学习关系认知,如过渡性推理 (TI),将其推广到新的情况. 人类在IT任务中的行为有时与这些网络模型有所不同,为大脑功能提供了新的见解.
科学领域:
- 认知神经科学 认知神经科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 关系认知,即推断概括关系的能力,对智力至关重要.
- 了解关系认知的神经基础,特别是过渡性推理 (TI),由于有限的预测模型,仍然是一个挑战.
- 假设工作记忆 (WM) 对于大脑中的关系推理至关重要.
研究的目的:
- 发现和分析能够执行过渡推理 (TI) 的神经网络 (NN).
- 调查NN如何在关系任务中将生物系统中观察到的行为概括和表现出来.
- 将NN性能和解决方案与WM依赖的TI任务中的人类行为进行比较.
主要方法:
- 在过渡性推理 (TI) 任务上训练的开发和分析神经网络 (NN).
- 测试了NN泛化功能,包括在工作内存 (WM) 负载下的性能.
- 进行了大规模的实验,人类受试者执行基于WM的TI.
- 将人类行为数据与来自不同 NN 模型的预测进行了比较.
主要成果:
- 在没有明确的过渡结构的情况下,NNs在TI任务中表现出完美的概括性.
- 尼恩表现出新兴的行为,包括依赖顺序的模式,反映了活体对象的行为.
- 在基于WM的TI任务中,人类受试者的行为与某些直观的NN解决方案不一致.
- 确定了具有明显行为和神经预测的替代性NN解决方案.
结论:
- 神经网络可以学习和概括关系认知,为大脑功能提供可测试的假设.
- 这项研究揭示了NN模型与TI中的人类行为之间的差异,表明了复杂的神经实现.
- 这些发现为基础关系认知的神经机制及其潜在的计算基础提供了新的见解.
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