The MRI Laboratory
MRI (Magnetic Resonance Imaging) has become a mainstay of current cognitive neuroscience research. It is used to record detailed images of organs and tissues within the body and it can help identify dynamic networks of brain activity. Once identified these networks can be integrated with increasingly sophisticated models of the brain's structure, which can help us understand how the brain creates perception, cognition and behaviour.

The research area of human social behaviour investigates how people control their own actions and understand those of others. Humans are masters in predicting others’ behaviour. 

By watching the facial expressions of a child, we know exactly which toy they will go for. When seeing someone frown at an open window, we are not surprised when they get up to close it. We make these predictions readily and automatically.

Investigating theories of prediction engines

Further to the findings of a recent ESRC-funded project, which concluded that people constantly predict others’ behaviour, MRI Laboratory Co-Lead, Dr Patric Bach, Associate Professor and his team are now testing predictive processing theories of social perception. These theories argue that the brain understands other’s behaviour by acting as a prediction engine.

The brain constantly tries out attributing different mental states to theirs, for example, what their goals and beliefs might be and then tests if these states fit their actual behaviour. We then instantly know whether our assumptions about others are correct because they behave as expected, or wrong because they behave differently, so we have to revise our assumptions. To characterise these processes, the team will use a combination of experimental psychology and neuroimaging, using EEG (Electroencephalography), which measures electrical activity of the brain and fMRI (functional Magnetic Resonance Imaging).

fMRI (functional Magnetic Resonance Imaging) uses the same principles of atomic physics, however, MRI scans image anatomical structure, whereas fMRI scans image metabolic function – measuring activity by detecting changes associated with blood flow.

Investigating bias and logic

Working with patients who have experienced brain lesions, MRI Laboratory Co-Lead, Dr Matt Roser, uses brain imaging, brain stimulation and behavioural tasks to better understand how perceptual, cognitive and motor processes are integrated between the two cerebral hemispheres of the brain.

Strong research experience in three particular areas – processes underlying bias in reasoning in the adult brain, the learning of visual regularities in autism, and the interaction of the two brain hemispheres in the aged – has converged to form our current research; the interaction of bias and logic in the aged and autistic brain.

Matt and Patric’s strong research experience covers three particular areas:

  • processes underlying bias in reasoning in the adult brain
  • the learning of visual regularities in autism
  • the interaction of the two brain hemispheres in the aged adults and adults scanned for suspected dementia.

Repetitive transcranial magnetic stimulation is targeted on functionally-active brain regions using MRI-guided neuronavigation

Brain imaging resource for dementia

Working with Dr Lucy Lee, Consultant Radio Neurologist at University Hospitals Plymouth NHS Trust, Matt is also developing the Dementia Brain-image database that will integrate MRI data from healthy aged adults, and adults scanned for suspected dementia.

It will provide a joint resource, also available to research groups and members of the Alzheimer's Research UK South West Network, that can be interrogated in various ways, to help us to understand why some individuals go on to age well while others progress rapidly into dementia.

Research expertise

Lab leads: Dr Patric Bach, Associate Professor in Psychology, and Dr Matt Roser, Lecturer in Psychology

Other research in this lab will be carried out by:
Dr Elsa Fourangan, Professor Stephen Hall, Dr Giorgio Ganis, Dr Nadege Bault, Dr Jeremy Goslin, Professor Andy Wills.

Key publications

Ward E, Ganis G & Bach P 2019 'Spontaneous vicarious perception of the content of others’ visual perspective' Current Biology 29, (5) 874–880, DOI PEARL

Bach P, Hudson M, McDonough KL & Edwards R 2018 'Perceptual Teleology: Expectations of Action Efficiency Bias Social Perception' Proceedings of the Royal Society B: Biological Sciences, DOI PEARL

Colton J, Bach P, Whalley B & Mitchell CJ 2018 'Intention insertion: activating an action’s perceptual consequences is sufficient to induce non-willed motor behaviour' Journal of Experimental Psychology: General PEARL

Bach P & Schenke KC 2017 'Predictive social perception: Towards a unifying framework from action observation to person knowledge' Social and Personality Psychology Compass 11, (7) e12312-e12312, DOI PEARL

Hudson M, Nicholson T, Ellis R & Bach P 2016 'I see what you say: Prior knowledge of other’s goals automatically biases the perception of their actions' Cognition146, 245-250, DOI PEARL

Hudson M, Nicholson T, Simpson WA, Ellis R & Bach P 2016 'One step ahead: The perceived kinematics of others’ actions are biased toward expected goals' Journal of Experimental Psychology: General 145, (1) 1-7, DOI PEARL

Bach P, Nicholson T & Hudson M 2014 'The affordance-matching hypothesis: how objects guide action understanding and prediction' FRONTIERS IN HUMAN NEUROSCIENCE 8, DOI PEARL

Karuza EA, Emberson LL, Roser ME, Cole D, Aslin RN & Fiser J 2017 'Neural Signatures of Spatial Statistical Learning: Characterising the Extraction of Structure from Complex Visual Scenes' Journal of Cognitive Neuroscience 29, (12) 1963–1976, DOI PEARL

Nicholson T, Roser M & Bach P 2017 'Understanding the Goals of Everyday Instrumental Actions Is Primarily Linked to Object, Not Motor-Kinematic, Information: Evidence from fMRI' PLOS ONE 12, (1) e0169700-e0169700, DOI PEARL 

Roser ME, Aslin RN, McKenzie R, Zahra D & Fiser J 2015 'Enhanced visual statistical learning in adults with autism' Neuropsychology 29, (2) 163-172 Author Site, DOI 

Sambrook TD, Roser M & Goslin J 2012 'Prospect theory does not describe the feedback-related negativity value function' Psychophysiology 49, (12) 1533-1544 Author Site, DOI 

Linnet E & Roser ME 2012 'Age-Related Differences in Interhemispheric Visuomotor Integration Measured by the Redundant Target Effect' PSYCHOLOGY AND AGING27, (2) 399-409 Author Site, DOI

Roser, M. E., Corballis, M. C., Jansari, A., Fulford, J., Benattayallah, A., & Adams, W. M. (2012). Bilateral redundancy gain and callosal integrity in a man with callosal lipoma: A diffusion-tensor imaging study. Neurocase, 18(3), 185-198.

Marrett, N. E., de-Wit, L. H., Roser, M. E., Kentridge, R. W., Milner, A. D., & Lambert, A. J. (2011). Testing the dorsal stream attention hypothesis: Electrophysiological correlates and the effects of ventral stream damage. Visual Cognition, 19(9), 1089–1121.

Queirazza F, Fouragnan E, Cavanagh J, Steele D, Philiastides M. Neural signatures of reinforcement learning in unmedicated depressed patients predict response to Cognitive Behavioural Therapy. Science Advances, 5 (7), eaav4962, 2019

Fouragnan E, Retzler C, Philiastides MG. Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis. Hum Brain Mapp. 2018;00:1–20. March 2018

Fouragnan E, Retzler C, Mullinger K, Philiastides MG.Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans. Scientific Reports. July 2017 (7):4762.

Pisauro A, Fouragnan E, Retzler C, Mullinger K, Philiastides MG. Neural correlates of evidence accumulation during value-based decisions revealed via simultaneous EEG-fMRI. Nature Communications. July 2017 (8): 15808.

Fouragnan E, Retzler C, Mullinger K, Philiastides M. Two spatiotemporally distinct value systems shape reward-based learning in the human brain. August 2015. Nature Communication (6):8107doi: 10.1038/ncomms9107

Fouragnan E, Chierchia G, Greiner S, Neveu R, Avesani P, Coricelli, G. Reputational priors magnify striatal responses to violation to trust. February 2013. The Journal of Neuroscience 33(8):3602–3611.

Ganis, G., & Keenan, J. P. (2009). The cognitive neuroscience of deception. Soc Neurosci, 4(6), 465-472.

Ganis, G., Kosslyn, S. M., Stose, S., Thompson, W. L., & Yurgelun-Todd, D. A. (2003). Neural correlates of different types of deception: an fMRI investigation. Cereb Cortex, 13(8), 830-836.

Ganis, G., Morris, R. R., & Kosslyn, S. M. (2009). Neural processes underlying self- and other-related lies: an individual difference approach using fMRI. Soc Neurosci, 4(6), 539-553.

Ganis, G., Rosenfeld, J. P., Meixner, J., Kievit, R. A., & Schendan, H. E. (2011). Lying in the scanner: covert countermeasures disrupt deception detection by functional magnetic resonance imaging. Neuroimage, 55(1), 312-319.

Ganis, G., Schendan, H. E., & Kosslyn, S. M. (2007). Neuroimaging evidence for object model verification theory: Role of prefrontal control in visual object categorization. Neuroimage, 34(1), 384-398.

Ganis, G., Thompson, W. L., & Kosslyn, S. M. (2004). Brain areas underlying visual mental imagery and visual perception: an fMRI study. Brain Res Cogn Brain Res, 20(2), 226-241.

Ganis, G., Thompson, W. L., & Kosslyn, S. M. (2005). Understanding the effects of task-specific practice in the brain: insights from individual-differences analyses. Cogn Affect Behav Neurosci, 5(2), 235-245.

Hsu, C. W., Begliomini, C., Dall'Acqua, T., & Ganis, G. (2019). The effect of mental countermeasures on neuroimaging-based concealed information tests. Hum Brain Mapp, 40(10), 2899-2916.

Karuza, E. A., Emberson, L. L., Roser, M. E., Cole, D., Aslin, R. N., & Fiser, J. (2017). Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes. Journal of Cognitive Neuroscience, 29(12), 1963-1976.

Nicholson, T., Roser, M.E., Bach, P. (2017). Understanding the goals of everyday instrumental actions is primarily linked to object, not motor-kinematic, information: evidence from fMRI. PLoS ONE, 12(1), e0169700.

Roser, M., Evans, J. S. B., McNair, N. S., Fuggetta, G., Handley, S. J., Carroll, L. S., & Trippas, D. (2015). Investigating reasoning with multiple integrated neuroscientific methods. Frontiers in Human Neuroscience, 9, 41.

Roser, M. E., Corballis, M. C., Jansari, A., Fulford, J., Benattayallah, A., & Adams, W. M. (2012). Bilateral redundancy gain and callosal integrity in a man with callosal lipoma: A diffusion-tensor imaging study. Neurocase, 18(3), 185-198.

Fugelsang, J.A., Roser, M.E., Corballis, P.M., Gazzaniga, M.S., & Dunbar, K.N. (2005). Brain mechanisms underlying perceptual causality. Cognitive Brain Research, 24, 41-47.

Hall SD, Yamawaki N, Fisher AE, Clauss RP, Woodhall GL & Stanford IM. (2010). Desynchronisation of pathological low-frequency brain activity by the hypnotic drug zolpidem. Clinical Neurophysiology. 121(4): 549-55.

Ronnqvist KC, McAllister CJ, Woodhall GL, Stanford & Hall SD. (2013). A multimodal perspective on the composition of cortical oscillations. Frontiers in Human Neuroscience. 7, 132.

Brookes M, Gibson, A, Hall SD, Furlong PL, Barnes GR, Hillebrand, A, Francis S & Morris P. (2005).GLM-beamformer method demonstrates stationary field, alpha ERD and gamma ERS co-localisation with fMRI BOLD response in visual cortex. NeuroImage, 26(1): 302-8.

Hall SD, Holliday IE, Hillebrand A, Singh KD, Furlong PL, Hadjipapas A & Barnes GR. (2005). The Missing Link: analogous human and primate cortical gamma oscillations. Neuroimage. 26(1):13-17.

Inkster, A.B., Milton, F., Edmunds, C.E.R., Benattayallah, A., & Wills, A.J. (preprint). Neural correlates of the inverse base-rate effect.

Milton, F., Bealing, P., Carpenter, K.L., Bennattayallah, A., & Wills, A.J. (2017). The neural correlates of similarity- and rule-based generalization. Journal of Cognitive Neuroscience, 29, 150-166.

Carpenter, K., Wills, A.J., Bennattayallah, A., & Milton, F. (2016). A comparison of the neural correlates that underlie rule-based and information-integration category learning. Human Brain Mapping, 37, 3557–3574.

Milton, F., Wills, A.J., & Hodgson, T.L. (2009). The neural basis of overall similarity and single-dimension sorting. NeuroImage, 46, 319-326.


Predicting the future? Making sense of our sixth sense...

fMRI studies which look at changes over time have seen many major breakthroughs. The neuroimaging community is benefitting from the development of multi-voxel classifier analysis (MVPA), which allows detection of high sensitivity patterns of activity in the brain, and combines conventional fMRI scanning with machine learning techniques.

These techniques measure whether a task just generally activates a part of the brain, but also allows us to 'look into' this brain and discover which information it carries. It is now possible to 'read out' which face or object a person is currently thinking about, or which decision they will make seconds later.

Enhancing research through BRIC 

The MRI Laboratory in the BRIC facility will provide an environment for investigating theories with cutting-edge neuroimaging methodologies, made possible by a state-of-the-art 3-Tesla Siemens Prisma MR scanner, which will be the most advanced scanner in the region.