Dr Adam Barrett

EPSRC Research Fellow

Sackler Centre for Consciousness Science

Department of Informatics

University of Sussex

Department of Informatics,
University of Sussex,
Falmer, Brighton, BN1 9QJ, UK

Office: Chichester I, 158 (first floor)
Tel: +44 - (0) 1273 - 872664



My research makes use of mathematical methods to attempt to understand what is distinct about the particular neural structures, dynamics and functions that give rise to conscious experience.

More specifically, inspired by Integrated Information Theory, a major focus of my work is on the development of potential measures of conscious level that quantify the extent to which neural dynamics simultaneously generate and integrate information. In other words, I work on modelling and developing our mathematical understanding of neural complexity, as well as deriving statistical techniques for applying abstract measures based on this concept to neuroimaging data. A key component of this involves developing methodology for quantifying the strength of directed interactions (functional connectivity) between neural dynamical variables, and this leads to applications broadly across neuroscience. Datasets I have analysed include EEG recordings from subjects undergoing general anaesthesia, and intracranial depth electrode recordings from awake and asleep epileptic patients.

Other current interests include:
- The role of metacognition in conscious awareness, and how to model and measure it at both the behavioural and neural level.
- Predictive coding and Bayesian models of perception.

Prior to the Sackler Centre for Consciousness Science, I was a postdoc at the Institute for Adaptive and Neural Computation at the University of Edinburgh. There I worked mainly on synaptic plasticity and the neural basis of learning and memory.

I obtained my PhD in theoretical physics from the University of Oxford, and the topic of my thesis was string/M-theory.

For a full CV, click here.


Sherman, M.T., Seth, A.K., Barrett, A.B., & Kanai, R. (2015). Prior expectations facilitate metacognition for perceptual decision. Consciousness and Cognition 35: 53-65. [pre-print]

Barrett, A.B. (2015). Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Phys. Rev. E 91: 052802. [e-print]

Sherman, M.T., Barrett, A.B., & Kanai, R. (2015). Inferences about consciousness using subjective reports of confidence. Chapter in "Behavioural Methods in Consciousness Research", Oxford University Press, Oxford. [pre-print]

Barrett, A.B., & Seth, A.K. (2015). Directed spectral methods. Entry in the Springer Encyclopedia of Computational Neuroscience. [pre-print]

Seth, A.K., Barrett, A.B., & Barnett, L. (2015). Granger causality analysis in neuroscience and neuroimaging. J. Neurosci. 35(8):3293-3297. [view]

Garfinkel, S.N., Seth, A.K., Barrett, A.B., Suzuki, K., & Critchley, H.D. (2015). Knowing your own heart: Distinguishing interoceptive accuracy from interoceptive awareness. Biological Psychology 104: 65-74. [view]

Scott, R.B., Dienes, Z., Barrett, A.B., Bor, D., & Seth, A.K. (2014). Blind insight: metacognitive discrimination despite chance task performance. Psych. Science 25(12): 2199-2208. [view]

Gould C., Froese T., Barrett A.B., Ward J., & Seth A.K. (2014). An extended case study on the phenomenology of sequence-space synaesthesia. Front. Hum. Neurosci. 8:433. [view]

Vandenbroucke, A.R.E., Sligte, I.G., Barrett, A.B., Seth, A.K., Fahrenfort, J.J., & Lamme, V.A.F. (2014). Accurate metacognition for visual sensory memory representations. Psych. Science 25(4): 861-873. [pre-print]

Barrett, A.B. (2014). An integration of integrated information theory with fundamental physics. Front. Psychol. 5(63). [view]

Barrett, A.B., Dienes, Z., & Seth, A.K. (2013). Measures of metacognition on signal-detection theoretic models. Psych. Meth. 18(4): 535-552. [pre-print]

Garfinkel, S.N., Barrett, A.B., Minati, L., Dolan, R.J., Seth, A.K. & Critchley, H.D. (2013). What the heart forgets: Cardiac timing influences memory for words and is modulated by metacognition and interoceptive sensitivity. Psychophysiology 50(6): 505-512. [pre-print]

Barrett, A.B., & Barnett, L. (2013). Granger causality is designed to measure effect, not mechanism. Frontiers in Neuroinformatics 7(6). [view]

van Rossum, M.C.W., Shippi, M., & Barrett, A.B. (2012). Soft-bound synaptic plasticity outperforms hard-bound plasticity for a variety of learning paradigms. PLoS Comput. Biol. 8(12): e1002836. [view]

Feldwisch-Drentrup, H., Barrett, A.B., Smith, M.T., & van Rossum, M.C.W. (2012). Fluctuations in the open time of synaptic channels: an application to noise analysis based on charge. J. Neurosci. Meth. 210(1): 15-21. [pdf]

Barrett, A.B., Murphy, M., Bruno, M.A., Noirhomme, Q., Boly, M., Laureys, S., & Seth, A.K. (2012). Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia. PLoS ONE, 7(1): e29072. [pdf]

Seth, A.K., Barrett, A.B., & Barnett, L. (2011). Causal density and information integration as measures of conscious level. Phil. Trans. Roy. Soc. A, 369:3748-3767. [pdf]

Froese, T., Gould, C., & Barrett, A.B. (2011). Re-Viewing from Within: A commentary on the use of first- and second-person methods in the science of consciousness. Constructivist Foundations, 6(2): 254-269. [pdf]

Barrett, A.B., & Seth, A.K. (2011). Practical measures of integrated information for time-series data. PLoS Comput. Biol., 7(1): e1001052. [pdf]

Seth, A.K., & Barrett, A.B. (2010). Neural theories need to account for, not discount, introspection and behaviour. Cog. Neurosci., 1(3):227-228. [pdf]

Barrett, A.B., Barnett, L., & Seth, A.K. (2010). Multivariate Granger causality and generalized variance. Phys. Rev. E, 81: 041907. [pdf]

Cortes, J.M., Greve, A., Barrett, A.B., & van Rossum, M.C.W. (2010). Dynamics and robustness of familiarity memory. Neural Comput., 22(2):448-466. [pre-print]

Barnett, L., Barrett, A.B., & Seth, A.K. (2009). Granger causality and transfer entropy are equivalent for Gaussian variables. Phys. Rev. Lett., 103: 238701. [pdf]

beim Graben, P., Barrett, A.B., & Atmanspacher, H. (2009). Stability criteria for the contextual emergence of macrostates in neural networks. Network: Computation in Neural Systems, 20(3): 177-195. [pre-print]

Barrett, A.B., Billings, G.O., Morris, R.G.M., & van Rossum, M.C.W. (2009). State based model of long-term potentiation and synaptic tagging and capture. PLoS Comput. Biol., 5(1): e1000259. [view]

Barrett, A.B., & van Rossum, M.C.W. (2008). Optimal learning rules for discrete synapses. PLoS Comput. Biol., 4(11), e1000230. [view]


"M-theory on Manifolds with G2 Holonomy", A.B. Barrett, DPhil thesis, University of Oxford, UK (2006). [e-print]

"Four-dimensional Effective M-theory on a Singular G2 Manifold", (A.B. Barrett primary author; A. Lukas senior author; L.B Anderson and M. Yamaguchi co-authors), Phys. Rev. D, 74, 086008 (2006). [e-print]

"M-Theory on the Orbifold C2/ZN", (A.B. Barrett primary author; A. Lukas senior author; L.B Anderson co-author), Phys. Rev. D, 73, 106011 (2006). [e-print]

"Classification and Moduli Kaehler Potentials of G2 Manifolds", (A.B. Barrett primary author; A. Lukas senior author), Phys. Rev. D, 71, 046004 (2005). [e-print]