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Deep Learning feature extraction for social interaction prediction in movies and visual cortex

   UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents

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  Mr Jared de Bruin  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents.


  • Lars Muckli: School of Psychology
  • Fani Deligianni: School of Computing Science

While watching a movie, a viewer is immersed in the spatiotemporal structure of the movie’s audiovisual and high level conceptual content [Raz19]. The nature of the movies induces a natural waxing and waning of more and less social immersive content. This immersion can be exploited during brain imaging experiments to emulate as closely as possible the every-day human life experience, including brain processes involved in social perception. The human brain is a prediction machine: in addition to receiving sensory information, it actively generates sensory predictions. It implements this by creating internal models about the world which are used to predict upcoming sensory inputs. This basic but powerful concept is used in several studies in Artificial Intelligence (AI) to perform different type of predictions: from video inner-frames for video interpolation [Bao19], to irregularity detection [Sabokrou18], passing through future sound prediction [Oord18]. Despite different studies on AI focusing on how to use visual features to detect and track actors in a movie [Afouras20], it is not clear in the brain how cortical networks for social cognition involve layers in the visual cortex for processing the social interaction cues occurring between actors. Several studies suggest that biological motion recognition (the visual processing of others’ actions) is central to understanding interactions between agents and involves top-down social cognition with bottom up visual processing. We will use cortical layer specific fMRI at Ultra High Field to read brain activity during movie stimulation. Using the latest advances in Deep Learning [Bao19, Afouras20], we will study how the interaction between two people in a movie is processed, trying to analyse predictions that occur between frames. The comparison between the two representation sets, which involves the analysis of the movie video with Deep Learning and its response measured within the brain, will occur doing model comparison with Representational Similarity Analysis (RSA) [Kriegeskorte08]. The work and its natural extensions will help clarify how the early visual cortex is responsible for guiding attention in social scene understanding. The student will spend time in both domains: studying and analysing the state-of-the-art methods in pose estimation and scene understanding in Artificial Intelligence. In brain imaging, they will learn how to perform a brain imaging study with fMRI: from data collection and understanding, to analysis methods. These two fields will provide a solid background in both brain imaging and artificial intelligence, teaching the student the ability to transfer skills and draw conclusions across domains.


[Afouras20] Afouras, T., Owens, A., Chung, J. S., & Zisserman, A. (2020). Self-supervised learning of audio-visual objects from video. European Conference on Computer Vision (ECCV 2020).
[Bao19] Bao, W., Lai, W. S., Ma, C., Zhang, X., Gao, Z., & Yang, M. H. (2019). Depth-aware video frame interpolation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3703-3712).
[Kriegeskorte08] Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers in systems neuroscience, 2, 4.
[Oord18] Oord, A. V. D., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.
[Raz19] Raz, G., Valente, G., Svanera, M., Benini, S., & Kovács, A. B. (2019). A Robust Neural Fingerprint of Cinematic Shot-Scale. Projections, 13(3), 23-52.
[Sabokrou18] Sabokrou, M., Pourreza, M., Fayyaz, M., Entezari, R., Fathy, M., Gall, J., & Adeli, E. (2018, December). Avid: Adversarial visual irregularity detection. In Asian Conference on Computer Vision (pp. 488-505). Springer, Cham.
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