

Dr Adam B. BarrettSenior Lecturer in Machine Learning and Data Science Deputy Director, Sussex Centre for Consciousness Science Office: Chichester 1, 110 
I am an interdisciplinary researcher developing complexity science and data science tools and applying them in multiple domains. My background is in mathematics and theoretical physics. The majority of my research has been on neuroscience of consciousness, and approaches inspired by integrated information theory. That is, on the relations between certain neural structures, dynamics and functions, and the subjective experiences they generate. My work explores the mathematical properties of measures of complexity, emergence and information integration, analytically and in simulation, and has applied some of these to brain imaging data from wakeful rest, anaesthesia, sleep and psychedelic states. I also explore complex systems and machine learning approaches to macroeconomics, to seek insight into economic stability, and am interested in the ecological question of the stability of a postgrowth economy. Right now, I am involved in a project applying complexity measures to sound recordings from diverse ecosystems to test how they relate to ecosystem health. I have also applied time series analysis and machine learning to drought forecasting from satellite imaging data. I teach modules on Algorithmic Data Science and Fundamentals of Machine Learning, and have supervised student projects on a broad spectrum of topics. Blog posts On the Integrated Information Theory of Consciousness. On the field of macroeconomics. 
Selected publications
Starkey, J., CarhartHarris R.L., Pigorini A., Nobili L., & Barrett, A.B. (2023). Statistical diversity distinguishes global states of consciousness. biorXiv 2023.12.05.570101. [view]
[Related Vox magazine article on complexity and consciousness]Mediano, P.A.M., Rosas, F.E., Bor, D., Seth, A.K., & Barrett, A.B. (2022). The strength of weak integrated information theory. Trends Cogn Sci. [https://doi.org/10.1016/j.tics.2022.04.008]
Mediano, P.A.M., Rosas, F.E., Luppi, A.I., CarhartHarris, R.L., Bor, D., Seth, A.K., & Barrett, A.B. (2021). Towards an extended taxonomy of information dynamics via Integrated Information Decomposition. arXiv 2109.13186. [view]
Mediano, P.A.M., Rosas, F.E., Barrett, A.B.*, & Bor, D.* (2021). Decomposing spectral and phasic differences in nonlinear features between datasets. Phys Rev Lett 127, 124101. [view] (* joint senior author)
Barrett, A.B., Duivenvoorden, S., Salakpi, E., Muthoka, J.M., Mwangi, J., Oliver, S., & Rowhani, P. (2020). Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya. Remote Sensing of Environment 248, 111886. [preprint]
Barrett, A.B. & Mediano, P.A.M. (2019). The Phi measure of integrated information is not welldefined for general physical systems. J Conscious Stud 26(12): 1120. [preprint]
Mediano, P.A.M., Seth, A.K., & Barrett, A.B. (2019). Measuring integrated information: Comparison of candidate measures in theory and simulation. Entropy 21, 17. [view]
Barrett, A.B. (2018). Stability of zerogrowth economics analysed with a Minskyan model. Ecol. Econ. 146: 228239. [view] [summary in The Conversation]
Schartner, M.M., CarhartHarris, R.L., Barrett, A.B., Seth, A.K., & Muthukumaraswamy, S.D. (2017). Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin. Nat. Sci. Rep. 7: 46421. [view]
Schartner, M.M., Pigorini, A., Gibbs, S.A., Arnulfo, G., Sarasso, S., Barnett, L., Nobili, L., Massimini, M., Seth, A.K., & Barrett, A.B. (2017). Global and local complexity of intracranial EEG decreases during NREM sleep. Neurosci. Conscious. 3 (1): niw022. [view]
Barrett, A.B. (2015). Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Phys. Rev. E 91: 052802. [eprint]
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 signaldetection theoretic models. Psych. Meth. 18(4): 535552. [link]
Barrett, A.B., & Seth, A.K. (2011). Practical measures of integrated information for timeseries data. PLoS Comput. Biol., 7(1): e1001052. [view]
All publications
Mediano, P.A.M., Rosas, F.E., Timmermann, C., Roseman, L., Nutt, D.J., Feilding, A., Kaelen, M., Kringelbach, M.L., Barrett, A.B., Seth, A.K., Muthukumaraswamy, S., Bor, D., & CarhartHarris, R.L. (2024). Effects of external stimulation on psychedelic state neurodynamics. ACS Chem Neurosci doi:10.1021/acschemneuro.3c00289 [view]
Starkey, J., CarhartHarris R.L., Pigorini A., Nobili L., & Barrett, A.B. (2023). Statistical diversity distinguishes global states of consciousness. biorXiv 2023.12.05.570101. [view] [Related Vox magazine article on complexity and consciousness]
Salakpi, E.E., Hurley, P.D., Muthoka, J.M., Barrett, A.B., Bowell, A., Oliver, S., & Rowhani, P. (2022). Forecasting vegetation condition with a Bayesian autoregressive distributed lags (BARDL) model. Nat Hazards Earth Syst Sci 22, 27032723. [view]
Barrett, A.B., & Seth, A.K. (2022). Directed Spectral Methods. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. [preprint]
Mediano, P.A.M., Rosas, F.E., Bor, D., Seth, A.K., & Barrett, A.B. (2022). The strength of weak integrated information theory. Trends Cogn Sci. [https://doi.org/10.1016/j.tics.2022.04.008]
Rosas, F.E., Mediano, P.A.M., Luppi, A.I., Jensen, H.J., Seth, A.K., Barrett, A.B., CarhartHarris R.L., & Bor, D. (2022). Greater than the parts: Review of the information decomposition approach to causal emergence. Phil Trans R Soc A 380, 20210246. [view]
Mediano, P.A.M., Rosas, F.E., Farah, J.C., Shanahan, M., Bor, D., & Barrett A.B. (2022). Integrated information as a common signature of dynamical and informationprocessing complexity. Chaos 32, 013115. [view]
Mediano, P.A.M., Rosas, F.E., Luppi, A.I., CarhartHarris, R.L., Bor, D., Seth, A.K., & Barrett, A.B. (2021). Towards an extended taxonomy of information dynamics via Integrated Information Decomposition. arXiv 2109.13186. [view]
Mediano, P.A.M., Rosas, F.E., Barrett, A.B.*, & Bor, D.* (2021) Decomposing spectral and phasic differences in nonlinear features between datasets. Phys Rev Lett 127, 124101. [view] (* joint senior author)
Rosas, F., Mediano, P.A.M., Rassouli, B., & Barrett, A.B. (2020). An operational information decomposition via synergistic disclosure. J Phys A 53, 485001. [view]
Rosas, F.E., Mediano, P.A.M., Jensen, H.J., Seth, A.K., Barrett, A.B., CarhartHarris, R.L., & Bor, D. (2020). Reconciling emergences: An informationtheoretic approach to identify causal emergence in multivariate data. PLoS Comput. Biol. 16(12): e1008289. [view]
Barrett, A.B., Duivenvoorden, S., Salakpi, E., Muthoka, J.M., Mwangi, J., Oliver, S., & Rowhani, P. (2020). Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya. Remote Sensing of Environment 248, 111886. [preprint]
Shirley, R., Pope, E., Bartlett, M., Oliver, S., Quadrianto, N., Hurley, P., Duivenvoorden, S., Rooney, P., Barrett, A.B., Kent, C., & Bacon, J. (2020). An empirical, Bayesian approach to modelling crop yield: Maize in USA. Environmental Research Communications 2: 025002. [view]
Barrett, A.B. & Mediano, P.A.M. (2019). The Phi measure of integrated information is not welldefined for general physical systems. J Conscious Stud 26(12): 1120. [preprint]
Mediano, P.A.M., Seth, A.K., & Barrett, A.B. (2019). Measuring integrated information: Comparison of candidate measures in theory and simulation. Entropy 21, 17. [view]
Sherman, M.T., Seth, A.K., & Barrett, A.B. (2018). Quantifying metacognitive thresholds using signaldetection theory. biorXiv 361543. [preprint]
Bor, D., Barrett, A.B., Schwartzman, D., & Seth, A.K. (2018). Response to Ruby et al: On a `failed' attempt to manipulate conscious perception with transcranial magnetic stimulation to prefrontal cortex. Consciousness and Cognition 65: 334341. [view]
Barnett, L., Barrett, A.B., & Seth, A.K. (2018). Solved problems for Granger causality in neuroscience: A response to Stokes and Purdon. Neuroimage 178: 744748. [view]
Barnett, L., Barrett, A.B., & Seth, A.K. (2018). Misunderstandings regarding the application of Granger causality in neuroscience. PNAS 201714497. [preprint]
Barrett, A.B. (2018). Stability of zerogrowth economics analysed with a Minskyan model. Ecol. Econ. 146: 228239. [view] [summary in The Conversation]
Bola, M., Barrett, A.B., Pigorini, A., Nobili, L., Seth, A.K., & Marchewka A. (2018). Loss of consciousness is related to hypercorrelated gammaband activity in anesthetized macaques and sleeping humans. Neuroimage 167: 130142. [view]
Schartner, M.M., CarhartHarris, R.L., Barrett, A.B., Seth, A.K., & Muthukumaraswamy, S.D. (2017). Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin. Nat. Sci. Rep. 7: 46421. [view]
Bor, D., Schwartzman, D.J., Barrett, A.B., & Seth, A.K. (2017). Thetaburst transcranial magnetic stimulation to the prefrontal or parietal cortex does not impair metacognitive visual awareness. PLoS ONE 12(2): e0171793. [view]
Schartner, M.M., Pigorini, A., Gibbs, S.A., Arnulfo, G., Sarasso, S., Barnett, L., Nobili, L., Massimini, M., Seth, A.K., & Barrett, A.B. (2017). Global and local complexity of intracranial EEG decreases during NREM sleep. Neurosci. Conscious. 3 (1): niw022. [view]
Barrett, A.B. (2016). A comment on Tononi & Koch (2015) `Consciousness: here, there and everywhere?'. Phil. Trans. R. Soc. B 20140198. [view]
Schartner, M.M., Seth, A.K., Noirhomme, Q., Boly, M., Bruno, M.A., Laureys, S., & Barrett, A.B. (2015). Complexity of multidimensional spontaneous EEG decreases during propofol induced general anaesthesia. PLoS ONE 10(8): e0133532. [view]
Sherman, M.T., Seth, A.K., Barrett, A.B., & Kanai, R. (2015). Prior expectations facilitate metacognition for perceptual decision. Consciousness and Cognition 35: 5365. [preprint]
Barrett, A.B. (2015). Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Phys. Rev. E 91: 052802. [eprint]
Sherman, M.T., Barrett, A.B., & Kanai, R. (2015). Inferences about consciousness using subjective reports of confidence. In: Overgaard, M. (ed.) Behavioral Methods in Consciousness Research. Oxford University Press, Oxford, UK. [preprint]
Seth, A.K., Barrett, A.B., & Barnett, L. (2015). Granger causality analysis in neuroscience and neuroimaging. J. Neurosci. 35(8):32933297. [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: 6574. [view]
Barrett, A.B., & Seth, A.K. (2014). Directed spectral methods. In: Jaeger, D. and Jung, R. (eds.) Encyclopedia of Computational Neuroscience. Springer, New York. [preprint]
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): 21992208. [view]
Gould C., Froese T., Barrett A.B., Ward J., & Seth A.K. (2014). An extended case study on the phenomenology of sequencespace 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): 861873. [preprint]
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 signaldetection theoretic models. Psych. Meth. 18(4): 535552. [link]
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): 505512. [preprint]
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). Softbound synaptic plasticity outperforms hardbound plasticity for a variety of learning paradigms. PLoS Comput. Biol. 8(12): e1002836. [view]
FeldwischDrentrup, 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): 1521. [pdf]
Barrett, A.B., Murphy, M., Bruno, M.A., Noirhomme, Q., Boly, M., Laureys, S., & Seth, A.K. (2012). Granger causality analysis of steadystate electroencephalographic signals during propofolinduced anaesthesia. PLoS ONE, 7(1): e29072. [view]
Seth, A.K., Barrett, A.B., & Barnett, L. (2011). Causal density and information integration as measures of conscious level. Phil. Trans. Roy. Soc. A, 369:37483767.[link]
Froese, T., Gould, C., & Barrett, A.B. (2011). ReViewing from Within: A commentary on the use of first and secondperson methods in the science of consciousness. Constructivist Foundations, 6(2): 254269. [pdf]
Barrett, A.B., & Seth, A.K. (2011). Practical measures of integrated information for timeseries data. PLoS Comput. Biol., 7(1): e1001052. [view]
Seth, A.K., & Barrett, A.B. (2010). Neural theories need to account for, not discount, introspection and behaviour. Cog. Neurosci., 1(3):227228. [link]
Barrett, A.B., Barnett, L., & Seth, A.K. (2010). Multivariate Granger causality and generalized variance. Phys. Rev. E, 81: 041907. [link]
Cortes, J.M., Greve, A., Barrett, A.B., & van Rossum, M.C.W. (2010). Dynamics and robustness of familiarity memory. Neural Comput., 22(2):448466. [preprint]
Barnett, L., Barrett, A.B., & Seth, A.K. (2009). Granger causality and transfer entropy are equivalent for Gaussian variables. Phys. Rev. Lett., 103: 238701. [link]
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): 177195. [preprint]
Barrett, A.B., Billings, G.O., Morris, R.G.M., & van Rossum, M.C.W. (2009). State based model of longterm 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]
Physics
"Mtheory on Manifolds with G2 Holonomy", A.B. Barrett, DPhil thesis, University of Oxford, UK (2006). [eprint]
"Fourdimensional Effective Mtheory on a Singular G2 Manifold", (A.B. Barrett primary author; A. Lukas senior author; L.B Anderson and M. Yamaguchi coauthors), Phys. Rev. D, 74, 086008 (2006). [eprint]
"MTheory on the Orbifold C2/ZN", (A.B. Barrett primary author; A. Lukas senior author; L.B Anderson coauthor), Phys. Rev. D, 73, 106011 (2006). [eprint]
"Classification and Moduli Kaehler Potentials of G2 Manifolds", (A.B. Barrett primary author; A. Lukas senior author), Phys. Rev. D, 71, 046004 (2005). [eprint]