随着时间的推移,模拟个人审美判断.
Aenne A Brielmann1,2, Max Berentelg1, Peter Dayan1,3
1Department of Computational Neuroscience, Max-Planck Institute for Biological Cybernetics, Tübingen, 72076, Germany.
概括
个人感官体验值是通过调整感官认知系统来学习的. 使用深度神经网络的计算模型准确地预测了人类对狗图像的喜好判断.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 心理学 心理学 心理学
背景情况:
- 感官体验价值的个体差异是显著的和可变的.
- 现有的理论表明,这些值是通过优化感官认知系统来学习的,以满足当前和未来的输入.
研究的目的:
- 为了测试一种理论,即感官体验的特异性价值来自于调整感官认知系统.
- 开发和验证一个预测主观评估的计算模型.
主要方法:
- 使用了通过NeuralCrossbreed算法生成的55个狗图像的数据集.
- 使用深度神经网络 (例如VGG-16) 来提取图像特征.
- 收集了来自59名参与者的每个图像的喜欢评分.
主要成果:
- 运用深度神经网络特征的计算模型准确地预测了试验对试验的喜欢判断 (中位数r = 0.65).
- 模型预测显著超过了人口平均预测 (中位数r = 0.01).
- 该模型的学习组件解释了依赖序列的评级变化,捕获了比随机试验订单多17%的差异.
结论:
- 这项研究验证了一种理论,该理论将个人价值判断与感官认知系统调整联系起来.
- 这项工作是迈向全面了解个人评估差异的基本步骤.
- 这些发现突出了计算模型在解释主观美学体验方面的潜力.
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