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

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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.