Centre for Social, Cognitive and Affective Neuroscience (cSCAN)

cSCAN brings together leading researchers who study social interaction, cognition, communication, and affect, creating synergy across these areas at multiple levels of explanation using the most sophisticated scientific tools available. We benefit from the Institute of Neuroscience & Psychology’s state-of-the-art infrastructure, which combines fMRI, MEG, EEG, TMS, peripheral physiological assessment, and eye-, body-, and face-tracking technology with an impressive computing infrastructure and accomplished technical staff.

cSCAN will host the annual INP debate on  26-27 October 2017. This year’s debate topic is "How do online interactions shape our social world?". We will explore the question of whether online social interaction has any unique implications for human interaction and human nature, or whether it is just "interaction as usual".


BarrBarsalou, DeBruine, Foster, Garrod, Garrod, Jack, JonesLages, McAleer, Papies, PhiliastidesRousselet, Scheepers, Schyns, Sereno, Simmons and Vinciarelli.


cSCAN’s research focuses on (1) Social Perception, Signalling and Affect, (2) Social Interaction, Communication and Language, (3) Motivation, Decision-making and Self-regulation, and (4) Quantitative Methods, Analyses and Technologies.

Social Perception, Signalling and Affect

‌This theme concentrates on how social signals, such as those present in faces and voices, shape social interactions and impressions. We have developed novel modelling methods to reveal new cultural and biological effects on social signal processing. Some of this work was recently presented at the Royal Society of London’s Summer Science Exhibition 2015. You can see a video trailer for our exhibit here. Our work on social signals has implications for human-robot interactions.‌

  • Jack, R.E. & Schyns, P.G. (2017). Towards a social psychophysics of face communication. Annual Review of Psychology. doi:10.1146/annurev-psych-010416-044242
  • Jack, R. E. & Schyns, P. G. (2015). The human face as a dynamic tool for social communication. Current Biology, 25, R621-R634.
  • Van Rijsbergen, N., Jaworska, K., Rousselet, G. A. & Schyns, P. G. (2014). With age comes representational wisdom in social signals. Current Biology, 24, 2792-2796.
  • Jones, B. C., Hahn, A. C., Fisher, C., Wincenciak, J., Kandrik, M., Roberts, S. C., Little, A. C. & DeBruine, L. M. (2015). Facial coloration tracks changes in women’s estradiol. Psychoneuroendocrinology, 56, 29-39.
  • Petrini, K., McAleer, P., Neary, C., Gillard, J., and Pollick, F. E. (2014) Experience in judging intent to harm modulates parahippocampal activity: an fMRI study with experienced CCTV operators. Cortex, 57, 74-91.
  • Rousselet, G. A., Ince, R. A., van Rijsbergen, N. J. & Schyns, P. G. (2014). Eye coding mechanisms in early human face event-related potentials. Journal of Vision, 14.
  • Dal Martello, M. F., DeBruine, L. M. & Maloney, L. T. (2015). Allocentric kin recognition is not affected by facial inversion. Journal of Vision, 15, 5.

Social Interaction, Communication and Language

This theme concentrates on how social interaction affects language and communication. We adopt a novel dyadic approach looking at cognitive aspects of linguistic and non-linguistic communication embedded in social contexts.  We are particularly concerned with those aspects of communication that depend upon individuals acting together, such as alignment, synchronicity and turn-taking.

  • Chen, Q., Zhang, J., Xu, X., Scheepers, C., Yang, Y., & Tanenhaus, M. K. (2016). Prosodic expectations in silent reading: ERP evidence from rhyme scheme and semantic congruence in classic Chinese poems. Cognition, 154, 11 - 21.
  • Kamide, Y., Lindsay, S., Scheepers, C., & Kukona, A. (2016). Event processing in the visual world: projected motion paths during spoken sentence comprehension. Journal of Experimental Psychology: Learning, Memory and Cognition, 42, 804-812.
  • Bögels, S., Barr, D. J., Garrod, S. & Kessler, K. (2014). Conversational interaction in the scanner: Mentalizing during language processing as revealed by MEG. Cerebral Cortex, 25, 3219-3234.
  • Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S. & Keysers, C. (2012). Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends in Cognitive Sciences, 16, 114-121.
  • Pickering, M. J. & Garrod, S. (2013) An integrated theory of language production and comprehension. Behavioral and Brain Sciences, 36, 329-347.
  • Kronmüller, E. & Barr, D. J. (2015). Referential precedents in spoken language comprehension: A review and meta-analysis. Journal of Memory and Language, 83, 1-19.
  • Tamariz, M., Ellison, T. M., Barr, D. J. & Fay, N. (2014). Cultural selection drives the evolution of human communication systems. Proceedings of the Royal Society of London B, 281, 20140488.

Motivation, Decision-making and Self-regulation

This theme concentrates on the cognitive and neural processes that regulate social behaviour. Current research is focused on characterising the neural correlates of perceptual and value-based decision-making and investigating how personal goals and environmental cues influence affect, contemplative skills, and health behaviour.

  • Papies, E. K. (2016). Health goal priming as a situated intervention tool: how to benefit from nonconscious motivational routes to health behaviour. Health Psychology Review, 10, 408-424.
  • Diaz, J. A., Queirazza, F. & Philiastides, M. G. (2017). Perceptual learning alters post-sensory processing in human decision making, Nature Human Behaviour, 1, 1-9.
  • Fouragnan, E., Retzler, C., Mullinger, K., & Philiastides, M. G. (2015). Two spatiotemporally distinct value systems shape reward-based learning in the human brain. Nature Communications, 6, 8107.
  • Gherman, S., & Philiastides, M. G. (2015). Neural representations of confidence emerge from the process of decision formation during perceptual choices. NeuroImage, 106, 134-143.
  • Lebois, L.A.M., Papies, E.K., Gopinath, K., Cabanban, R., Quigley, K.S., Krishnamurthy, V., Barrett, L.F., & Barsalou, L. (2015). A shift in perspective: Decentering through mindful attention to imagined stressful events. Neuropsychologia, 75, 505-524.
  • Papies, E. K., Potjes, I., Keesman, M., Schwinghammer, S., & van Koningsbruggen, G. M. (2014). Using health primes to reduce unhealthy snack purchases among overweight consumers in a grocery store. International Journal of Obesity, 38, 597–602.
  • Papies, E. K., Pronk, T. M., Keesman, M., & Barsalou, L. W. (2015). The benefits of simply observing: Mindful attention modulates the link between motivation and behavior. Journal of Personality and Social Psychology, 108, 148–170
  • Philiastides, M., Heekeren, H., & Sajda, P. (2014) Human scalp potentials reflect a mixture of decision-related signals during perceptual choices. Journal of Neuroscience, 34, 16877-16889.
  • Barsalou, L. W. (2013) Mirroring as pattern completion inferences within situated conceptualizations. Cortex, 49, 2951-2953.

Quantitative Methods, Analyses, and Technologies

Research into social interaction and the processing of social signals poses unique methodological and technological challenges.  Research in this theme meets these challenges by developing, evaluating, and implementing new statistical methods, computational models, and technologies.

  • Barr, D.J., Levy, R., Scheepers, C. and Tily, H.J. (2013) Random effects structure for confirmatory hypothesis testing: keep it maximal. Journal of Memory and Language, 68(3), pp. 255-278. doi:10.1016/j.jml.2012.11.001
  • Bieniek MM, Sekuler AB, Bennett PJ, Rousselet GA (2015).  A robust and representative lower bound on object processing speed in humans.  European Journal of Neuroscience. doi: 10.1111/ejn.13100
  • Jaworska, K., van Rijsbergen, N. J., McNair, S. W., Delis, I., Garrod, O. G. B., Jack, R. E., Rousselet, G. A., Schyns, P. G. (2014).  Characterizing the manifods of dynamic facial expression categorization.  Journal of Vision, 14, 1385-1385.
  • Kronmuller, E., & Barr, D. J. (2015). Referential precedents in spoken language comprehension: A review and meta-analysis.  Journal of Memory and Language, 83, 1-19.
  • Lages, M. (2014) Testing probabilistic models of binocular 3D motion perception. Testing, Psychometrics and Methods in Applied Psychology, 4, 389-406.
  • Pernet CR, Latinus M, Nichols TE, Rousselet GA (2014). Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. Journal of Neuroscience Methods, doi: 10.1016/j.jneumeth.2014.08.003
  • Richoz, A-R, Jack, R. E., Garrod, O. G. B., Schyns, P. G., Caldara, R. (2015). Reconstructing dynamic mental models of facial expressions in prosopagnosia reveals distinct representations for identity and expression.  Cortex,  65, 50-64.