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”他人行为中的含义的能力：我们朋友打开礼物时的兴奋，刷掉蜘蛛时的厌恶，或者只是“想要”他们当前所追求的东西。尽管这种意义的归因是所有社会互动的基础 - 从关于我们如何与他人联系到情感上如何与他人联系到有关我们如何应对其行为的决定的长期决策，但对基本机制的反应知之甚少。该项目在AI/深度学习中的最新进展是在AI/深层学习中的最新进展，可以测试这些属性的新型理论框架，这些框架有望发现这些属性背后的机构。因此，社会推论不会从对他人的行为的简单阅读中得出，而是反映了积极的尝试将意义投射到其上，以测试我们对他人的看法是否反映了他们的实际工作。我们的方法基于这样的观察，即在人工2D/3D图像产生的AI/深度学习方面具有惊人的相似投影机制（例如，扩散模型，域名适应性）实现了绩效，以实现绩效，以欺骗人类观察者，因为人为地误以为人为地误以为是真实的图像（例如，“伪造”视频）。这种机制的这种偶然性对齐使FIR的时间有可能在深度学习体系结构中实施理论上提出的机制，以测试：（1）这些机制是否确实反映了人类的表现和偏见，（2）这些模型能够像人类的观察者一样，使人们能够与外界信息相处（3），该模型是否能够与外界信息相处（3），是否有足够的动作（3），（3）借助真正的项目，该项目将为人们的社会感知技能提供新的见解，并允许通过机器学习模型对基本机制进行测试。此外，它将为AI/深度学习社区和英国行业提供“人性化”当前架构的新方法，并确保模型与人类期望保持一致。