审美学客观性的清除?
Daniel H Lee1, Junichi Chikazoe1
1ARAYA Inc., Tokyo, Japan.
Frontiers in neuroimaging
|September 1, 2023
概括
新的计算和神经科学方法为了解美学体验,特别是美丽的脑基础提供了一条道路. 人工智能和神经科学的这些进步可能会为我们如何感知和处理美丽提供新的见解.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 审美学,美和艺术的研究,在历史上缺乏显著的研究进展,尽管它的主观的重要性.
- 了解审美体验的神经支仍然是认知科学的复杂挑战.
研究的目的:
- 探索与美学处理相关的最新计算和神经科学进展.
- 提出新的方法来研究美丽和审美判断的神经基础.
主要方法:
- 假设深度神经网络 (DNN) 的实用性用于建模美学表示.
- 强调主要梯度作为研究美学处理的关键神经轴.
- 将这些进步与已建立的神经成像技术相结合.
主要成果:
- 深度神经网络显示出对美学体验至关重要的复杂表示模型的潜力.
- 主梯度为大脑中局部化审美处理提供了一个框架.
- 结合人工智能和神经科学的协同方法可以推动审美学的研究.
结论:
- 最近的计算 (DNN) 和神经科学 (主要梯度) 进展为美学研究提供了一个有希望的新方向.
- 将这些工具与神经成像相结合,可以显著加深我们对大脑如何处理美丽的理解.
- 这种跨学科的方法可能会开启美学科学的新时代.
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