More real than reality: using deep learning networks to resolve how people make sense of other people's behaviour

比现实更真实：使用深度学习网络来解决人们如何理解他人行为的问题

• 批准号: 2889133
• 金额: --
• 依托单位: University of Aberdeen
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889133
• 关键词: More; real; than; reality; using

Social interactions rely on the ability to "see" the meaning in others' behaviour: our friend's excitement when opening a present, their disgust when brushing away a spider, or simply that they "want" the thing they're currently reaching for. And while such attributions of meaning underpin all social interactions - from long term decisions about how we relate to others emotionally to moment-to-moment decisions about how we respond to their behaviour - little is known about the underlying mechanisms.This project leverages recent advances in AI/deep learning to test novel theoretical frameworks that promise to uncover the mechanisms behind these attributions. Accordingly, social inferences to do not emerge from a simple reading of others' behaviour, but reflect active attempts to project meaning onto it, to test whether what we think about others reflects what they actually do. Our approach rests on the observation that strikingly similar projection mechanisms underpin recent advances in AI/deep learning for artificial 2D/3D image generation (e.g., diffusion models, domain adaption) that achieve performance good-enough to fool human observers in mistaking artificial for real images (e.g., "deep fake" videos). This serendipitous alignment of mechanisms makes it possible for the firs time to implement the theoretically proposed mechanisms in deep-learning architectures, to test: (1) whether these mechanisms indeed reflect human-like performace and biases, (2) whether these models can, like human observers, combine information about the seen behaviour with outside contextual information, and, (3) whether their output is good enough to fool human observers into mistaking artifical action videos with real ones.This project will provide new insights into people's remarkably social perception skills, and allow tests of the underlying mechanisms through machine-learning models. Moreover, it will provide the AI/deep-learning community and UK industry with new ways to "humanize" current architectures and ensure that models align with human expectations.

社交互动依赖于“看到”他人行为的含义的能力：我们的朋友在打开礼物时的兴奋，他们在拂开蜘蛛时的厌恶，或者只是他们“想要”他们当前正在伸手去拿的东西。虽然这种意义的归属支撑着所有的社会互动——从我们如何与他人情感联系的长期决策到我们如何回应他们的行为的即时决策——但我们对潜在的机制知之甚少。这个项目利用了最新的进展在人工智能/深度学习领域测试新颖的理论框架，有望揭示这些归因背后的机制。因此，社会推论并不是从对他人行为的简单解读中产生的，而是反映了将意义投射到其上的积极尝试，以测试我们对他人的看法是否反映了他们的实际行为。我们的方法基于这样的观察：惊人相似的投影机制支撑着人工智能/深度学习在人工 2D/3D 图像生成（例如扩散模型、域适应）方面的最新进展，其性能足以欺骗人类观察者，使他们将人工图像误认为是真实的图像（例如“深度伪造”视频）。这种偶然的机制对齐使得第一次有可能在深度学习架构中实现理论上提出的机制，以测试：（1）这些机制是否确实反映了类人的表现和偏见，（2）这些模型是否可以，像人类观察者一样，将所看到行为的信息与外部上下文信息相结合，以及（3）它们的输出是否足以欺骗人类观察者，使其将人造动作视频与真实动作视频相混淆。该项目将为人们的行为提供新的见解社会感知技能，并允许通过机器学习模型测试潜在机制。此外，它将为人工智能/深度学习社区和英国工业界提供“人性化”当前架构的新方法，并确保模型符合人类期望。