cSCAN researchers address fundamental questions in social perception, cognition, and interaction with a focus on how humans process dynamic multi-modal social signals (e.g., faces, bodies, voices, language) including with artificial agents (e.g., social robots, avatars in real or virtual realities). cSCAN researchers use state-of-the-art multidisciplinary methods including a wide variety of behavioral, computational modelling, and neuroscience technologies (e.g., fMRI, MEG).

cSCAN members lead an international research network, with AE roles and editorial board membership of prominent journals (e.g., Psychological Science, JEP: General, Cognition, PNAS), substantial national and international funding (e.g., ERC, RCUK, ONR MURI, DARPA), and a range of UK Trusts and Foundations and industrial partners (e.g., FurHat Robotics, Dimensional Imaging).

Centre Inititatives

  • The 20th ACM International Conference on Intelligent Virtual Agents (IVA). Conference hosted by cSCAN (October 2020)
  • The 7th Consortium of European Research on Emotion (CERE) Conference. Conference hosted by centre (April 2018).
  • Face Facts: Revealing the information hidden in faces. Interactive exhibition at the Royal Society of London’s 2015 Summer Science Exhibition.

Further Publications

  • Tan, Y., Rérolle, S., Lalitharatne, T. D., Van Zalk, N., Jack, R. E., & Nanayakkara, T. (2022). Simulating dynamic facial expressions of pain from visuo-haptic interactions with a robotic patient. Scientific Reports. https://doi.org/10.1038/s41598-022-08115-1
  • Liu, M., Duan, Y., Ince, R. A. A., Chen, C., Garrod, O. G. B., Schyns, P. G., & Jack, R. E. (2022). Facial expressions elicit multiplexed perceptions of emotion categories and dimensions. Current Biology, 32(1), 200-209. https://doi.org/10.1016/j.cub.2021.10.035
  • Zhan, J., Liu, M., Garrod, O.G.B., Daube, C., Ince, R. A. A., Jack, R. E., Schyns, P. G. (2021). Modelling individual preferences reveals that face beauty is not universally perceived across cultures. Current Biology, 31(10), 2243–2252.
  • Chen, C., Crivelli, C., Garrod, O. G. B., Schyns, P. G., Fernández-Dols, J-M., & Jack, R. E. (2018). Distinct facial expressions represent pain and pleasure across cultures. Proceedings of the National Academy of Science of the USA, 115(43), E10013–E10021. https://doi.org/10.1073/pnas.1807862115
  • Jack, R. E., Crivelli, C., & Wheatley, T. (2018). Data-driven methods to diversify knowledge of human psychology. Trends in Cognitive Sciences, 22(1), 1–5. https://doi.org/10.1016/j.tics.2017.10.002
  • Cross, E. S., & Ramsey, R. (2021). Mind Meets Machine: Towards a Cognitive Science of Human-Machine Interactions. Trends in cognitive sciences, 25(3), 200–212. https://doi.org/10.1016/j.tics.2020.11.009
  • Henschel, A., Hortensius, R., & Cross, E. S. (2020). Social Cognition in the Age of Human-Robot Interaction. Trends in neurosciences, 43(6), 373–384. https://doi.org/10.1016/j.tins.2020.03.013
  • Cross, E. S., Riddoch, K. A., Pratts, J., Titone, S., Chaudhury, B., & Hortensius, R. (2019). A neurocognitive investigation of the impact of socializing with a robot on empathy for pain. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 374(1771), 20180034. https://doi.org/10.1098/rstb.2018.0034
  • Apšvalka, D., Cross, E. S., & Ramsey, R. (2018). Observing Action Sequences Elicits Sequence-Specific Neural Representations in Frontoparietal Brain Regions. The Journal of neuroscience : the official journal of the Society for Neuroscience, 38(47), 10114–10128. https://doi.org/10.1523/JNEUROSCI.1597-18.2018
  • Williams, E. H., Cristino, F., & Cross, E. S. (2019). Human body motion captures visual attention and elicits pupillary dilation. Cognition, 193, 104029. https://doi.org/10.1016/j.cognition.2019.104029
  • Orlandi, A., Cross, E. S., & Orgs, G. (2020). Timing is everything: Dance aesthetics depend on the complexity of movement kinematics. Cognition, 205, 104446. https://doi.org/10.1016/j.cognition.2020.104446
  • Raviv, L., Lupyan, G., Green, S. C. (in press). How variability shapes learning and generalization. Trends in Cognitive Science. https://doi.org/10.1016/j.tics.2022.03.007
  • Raviv, L., & Kirby, S. (in press). Self-Domestication and the cultural evolution of language. Oxford Handbook of Cultural Evolution
  • Raviv, L., de Heer Kloots, M., & Meyer, A. (2021). What makes a language easy to learn? A preregistered study on how systematic structure and community size affect language learnability. Cognition, 210, 104620. https://doi.org/10.1016/j.cognition.2021.104620
  • Raviv, L., Meyer, A., & Lev-Ari, S. (2020). The role of social network structure in the emergence of linguistic structure. Cognitive Science, 44(8), e12876. doi:10.1111/cogs.12876
  • Raviv, L., Meyer, A., Lev-Ari, S. (2019b). Larger communities create more systematic languages. Proceedings of the Royal Society B: Biological Science, 286(1907). https://doi.org/10.1098/rspb.2019.1262
  • Raviv, L., Meyer, A., Lev-Ari, S. (2019a). Compositional structure can emerge without generational transmission. Cognition, 182, 151-164. https://doi.org/10.1016/j.cognition.2018.09.010
  • Raviv, L., & Arnon, I. (2018a). Systematicity, but not compositionality: Examining the emergence of linguistic structure in children and adults using iterated learning. Cognition, 181, 160-173. https://doi.org/10.1016/j.cognition.2018.08.011
  • Raviv, L., & Arnon, I. (2018b). The developmental trajectory of children’s auditory and visual statistical learning abilities: Modality-based differences in the effect of age. Developmental Science. 21(4): e12593. https://doi.org/10.1111/desc.12593
  • Havron, N., Raviv, L., & Arnon, I. (2018). Literate and preliterate children show different learning patterns in an artificial language learning task. Journal of Cultural Cognitive Science, 2, 21-33. https://doi.org/10.1007/s41809-018-0015-9