感觉的感知优势:无影响的视觉机器解释了人类视觉唤起的影响的大部分差异
Colin Conwell1, Daniel Graham2, Chelsea Boccagno3,4
1Department of Psychology, Harvard University, Cambridge, MA 02139.
概括
在视觉数据上训练的深度神经网络可以预测人类的兴奋,价值和美感. 这表明,从自然图像中学习高效的表现驱动情感和美学体验.
科学领域:
- 计算神经科学是一种计算神经科学.
- 计算机视觉 计算机视觉 计算机视觉
- 情感科学是一种情感科学.
背景情况:
- 人类的感知包括看到和感觉.
- 机器视觉系统缺乏人类情感体验.
- 了解视觉感知和情绪之间的联系至关重要.
研究的目的:
- 调查机器视觉模型是否可以预测人类对图像的情感反应.
- 评估表达学习在视觉唤起的情感中所扮演的角色.
- 探索审美评价的计算基础.
主要方法:
- 部署了180个深度神经网络模型,训练了计算机视觉任务.
- 在没有额外学习的情况下使用模型特征来解码人类情感评级 (兴奋,价值,美丽).
- 在图像类别和模型架构中分析预测.
主要成果:
- 纯粹的感知模型解释了人类情感评级的大部分差异.
- 更深层的网络层和自我监督模型显示出更强大的预测能力.
- 从视觉数据中学到的概念前抽象驱动了情感预测.
结论:
- 视觉唤起的影响与从自然图像统计学中学习高效的表示有关.
- 感知计算模型可以捕捉情感和美学价值的各个方面.
- 情感和美学判断可能会出现在近乎感知处理的过程中.
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