Chris Mathys

Home
Talks
Publications
Videos

Selected Publications

Lundbak Olesen, C., Mikus, N., Hansen, M., Legrand, N., Thestrup Waade, P., & Mathys, C. (2026). It Is What It Isn't: Introducing a Constraint-Based Approach to Structure Learning. Entropy, 28(5), 534. https://doi.org/10.3390/e28050534
Banaschewski, M. T., Mathys, C., Winkler, I., Todd, J., & Auksztulewicz, R. (2026). Predictive Processing Over the Course of Aging: Multiple Timescales of Effective Connectivity. European Journal of Neuroscience, 63(1), e70387. https://doi.org/10.1111/ejn.70387
Knolle, F., Sterner, E. F., Demler, V. F., MacGregor, L. J., & Mathys, C. (2026). Guided by Expectations: Overweighted Semantic Priors in Schizotypy and Their Links to Glutamate. Biological Psychiatry, 99(7), 569–579. https://doi.org/10.1016/j.biopsych.2025.06.025
Mathys, C., Legrand, N., Waade, P. T., Mikus, N., & Weber, L. A. (2026). Robust volatility updates for Hierarchical Gaussian Filtering. arXiv:2605.00966. https://doi.org/10.48550/arXiv.2605.00966
Ehmsen, J. F., Nikolova, N., Christensen, D. E., Banellis, L., Böhme, R. A., Brændholt, M., Courtin, A. S., Krænge, C. E., Mitchell, A. G., Deolindo, C. S., Steenkjær, C. H., Vejlø, M., Mathys, C., Allen, M. G., & Fardo, F. (2025). Thermosensory predictive coding underpins an illusion of pain. Science Advances. https://doi.org/10.1126/sciadv.adq0261
Hess, A. J., Iglesias, S., Köchli, L., Marino, S., Müller-Schrader, M., Rigoux, L., Mathys, C., Harrison, O. K., Heinzle, J., Frässle, S., & Stephan, K. E. (2025). Bayesian Workflow for Generative Modeling in Computational Psychiatry. Computational Psychiatry, 9(1). https://doi.org/10.5334/cpsy.116
Jassim, N., Waade, P. T., Parsons, O., Petzschner, F. H., Rua, C., Rodgers, C. T., Baron-Cohen, S., Suckling, J., Mathys, C., & Lawson, R. P. (2025). Computational signatures of uncertainty are reflected in motor cortex excitatory neurochemistry. Nature Communications, 16(1), 9737. https://doi.org/10.1038/s41467-025-64702-6
Todd, J., Yeark, M., Godfrey, M., Mathys, C., & Winkler, I. (2025). Auditory Inference and Long-Term Modulation of Excitation and Inhibition. Psychophysiology, 62(11), e70193. https://doi.org/10.1111/psyp.70193
Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., & Mathys, C. (2025). The generalized Hierarchical Gaussian Filter. arXiv:2305.10937. https://doi.org/10.48550/arXiv.2305.10937
Thestrup Waade, P., Lundbak Olesen, C., Ehrenreich Laursen, J., Nehrer, S. W., Heins, C., Friston, K., & Mathys, C. (2025). As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference. Entropy, 27(2), 143. https://doi.org/10.3390/e27020143
Nehrer, S. W., Ehrenreich Laursen, J., Heins, C., Friston, K., Mathys, C., & Thestrup Waade, P. (2025). Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models. Entropy, 27(1), 62. https://doi.org/10.3390/e27010062
Atanassova, D. V., Oosterman, J. M., Diaconescu, A. O., Mathys, C., Madariaga, V. I., & Brazil, I. A. (2025). Exploring when to exploit: The cognitive underpinnings of foraging-type decisions in relation to psychopathy. Translational Psychiatry, 15, 31. https://doi.org/10.1038/s41398-025-03245-2
Mikus, N., Lamm, C., & Mathys, C. (2025). Computational Phenotyping of Aberrant Belief Updating in Individuals With Schizotypal Traits and Schizophrenia. Biological Psychiatry, 97(2), 188–197. https://doi.org/10.1016/j.biopsych.2024.08.021
Brouzou, K. O., Kamp, D., Hensel, L., Lüdtke, J., Lahnakoski, J. M., Dukart, J., Mikus, N., Mathys, C., Eickhoff, S. B., & Schilbach, L. (2025). Using personalised brain stimulation to modulate social cognition in adults with autism-spectrum-disorder: protocol for a randomised single-blind rTMS study. BMC Psychiatry, 25(1), 281. https://doi.org/10.1186/s12888-025-06707-x
Atanassova, D. V., Mathys, C., Diaconescu, A. O., Madariaga, V. I., Oosterman, J. M., & Brazil, I. A. (2024). Diminished pain sensitivity mediates the relationship between psychopathic traits and reduced learning from pain. Communications Psychology, 2(1), 86. https://doi.org/10.1038/s44271-024-00133-1
Suthaharan, P., Thompson, S. L., Rossi-Goldthorpe, R. A., Rudebeck, P. H., Walton, M. E., Chakraborty, S., Noonan, M. P., Costa, V. D., Murray, E. A., Mathys, C. D., Groman, S. M., Mitchell, A. S., Taylor, J. R., Corlett, P. R., & Chang, S. W. C. (2024). Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia. Cell Reports, 43(6), 114355. https://doi.org/10.1016/j.celrep.2024.114355
Mancinelli, F., Nolte, T., Griem, J., Lohrenz, T., Feigenbaum, J., King-Casas, B., Montague, P. R., Fonagy, P., & Mathys, C. (2024). Attachment and borderline personality disorder as the dance unfolds: A quantitative analysis of a novel paradigm. Journal of Psychiatric Research, 175, 470–478. https://doi.org/10.1016/j.jpsychires.2024.03.046
Suárez-Pinilla, M., Schofield, H., Hill, R., Brookes, M., Mathys, C., & Strange, B. A. (2024). Hippocampal processing of levels of uncertainty and environmental volatility: OPM-MEG correlates of learning through levels of uncertainty by a hierarchical Gaussian filter. 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). https://doi.org/10.1109/MetroXRAINE62247.2024.10795811
Mikus, N., Eisenegger, C., Mathys, C., Clark, L., Müller, U., Robbins, T. W., Lamm, C., & Naef, M. (2023). Blocking D2/D3 dopamine receptors in male participants increases volatility of beliefs when learning to trust others. Nature Communications, 14, 4310. https://doi.org/10.1038/s41467-023-39823-5
Cieri, F., Carhart-Harris, R. L., Mathys, C., Turnbull, O., & Solms, M. (2023). Editorial: Frontiers in psychodynamic neuroscience. Frontiers in Human Neuroscience, 17. https://doi.org/10.3389/fnhum.2023.1170480
Lundbak Olesen, C., Waade, P. T., Albantakis, L., & Mathys, C. (2023). Phi fluctuates with surprisal: An empirical pre-study for the synthesis of the free energy principle and integrated information theory. PLOS Computational Biology, 19(10), e1011346. https://doi.org/10.1371/journal.pcbi.1011346
Mikus, N., Korb, S., Massaccesi, C., Gausterer, C., Graf, I., Willeit, M., Eisenegger, C., Lamm, C., Silani, G., & Mathys, C. (2022). Effects of dopamine D2/3 and opioid receptor antagonism on the trade-off between model-based and model-free behaviour in healthy volunteers. eLife, 11, e79661. https://doi.org/10.7554/eLife.79661
Erdmann, T., & Mathys, C. (2022). A generative framework for the study of delusions. Schizophrenia Research, 245, 42–49. https://doi.org/10.1016/j.schres.2020.11.048
Kafadar, E., Fisher, V. L., Quagan, B., Hammer, A., Jaeger, H., Mourgues, C., Thomas, R., Chen, L., Imtiaz, A., Sibarium, E., Negreira, A. M., Sarisik, E., Polisetty, V., Benrimoh, D., Sheldon, A. D., Lim, C., Mathys, C., & Powers, A. R. (2022). Conditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility. Biological Psychiatry, 92(10), 772–780. https://doi.org/10.1016/j.biopsych.2022.05.007
Waade, P. T., Mikus, N., & Mathys, C. (2021). Inferring in Circles: Active Inference in Continuous State Space Using Hierarchical Gaussian Filtering of Sufficient Statistics. In M. Kamp et al. (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 810–818). Springer International Publishing. https://doi.org/10.1007/978-3-030-93736-2_57
Erdmann, T., & Mathys, C. (2021). Rule Learning Through Active Inductive Inference. In M. Kamp et al. (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 715–725). Springer International Publishing. https://doi.org/10.1007/978-3-030-93736-2_51
Şenöz, İ., Podusenko, A., Akbayrak, S., Mathys, C., & de Vries, B. (2021). The Switching Hierarchical Gaussian Filter. 2021 IEEE International Symposium on Information Theory (ISIT), 1373–1378. https://doi.org/10.1109/ISIT45174.2021.9518229
Suthaharan, P., Reed, E. J., Leptourgos, P., Kenney, J. G., Uddenberg, S., Mathys, C. D., Litman, L., Robinson, J., Moss, A. J., Taylor, J. R., Groman, S. M., & Corlett, P. R. (2021). Paranoia and belief updating during the COVID-19 crisis. Nature Human Behaviour, 5, 1190–1202. https://doi.org/10.1038/s41562-021-01176-8
Kirsch, L. P., Mathys, C., Papadaki, C., Talelli, P., Friston, K., Moro, V., & Fotopoulou, A. (2021). Updating beliefs beyond the here-and-now: The counter-factual self in anosognosia for hemiplegia. Brain Communications, 3(2), fcab098. https://doi.org/10.1093/braincomms/fcab098
Frässle, S., Aponte, E. A., Bollmann, S., Brodersen, K. H., Do, C. T., Harrison, O. K., Harrison, S. J., Heinzle, J., Iglesias, S., Kasper, L., Lomakina, E. I., Mathys, C., Müller-Schrader, M., Pereira, I., Petzschner, F. H., Raman, S., Schöbi, D., Toussaint, B., Weber, L. A., & Stephan, K. E. (2021). TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry. Frontiers in Psychiatry, 12, 680811. https://doi.org/10.3389/fpsyt.2021.680811
Everett, J. A. C., Colombatto, C., Awad, E., …, Mathys, C., …, & Crockett, M. J. (2021). Moral dilemmas and trust in leaders during a global health crisis. Nature Human Behaviour, 5, 1074–1088. https://doi.org/10.1038/s41562-021-01156-y
Iglesias, S., Kasper, L., Harrison, S. J., Manka, R., Mathys, C., & Stephan, K. E. (2021). Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning. NeuroImage, 226, 117590. https://doi.org/10.1016/j.neuroimage.2020.117590
Weber, L. A., Diaconescu, A. O., Mathys, C., Schmidt, A., Kometer, M., Vollenweider, F., & Stephan, K. E. (2020). Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity. Journal of Neuroscience, 40(29), 5658–5668. https://doi.org/10.1523/JNEUROSCI.3069-19.2020
Reed, E. J., Uddenberg, S., Suthaharan, P., Mathys, C. D., Taylor, J. R., Groman, S. M., & Corlett, P. R. (2020). Paranoia as a deficit in non-social belief updating. eLife, 9, e56345. https://doi.org/10.7554/eLife.56345
Mathys, C., & Weber, L. (2020). Hierarchical Gaussian Filtering of Sufficient Statistic Time Series for Active Inference. In T. Verbelen, P. Lanillos, C. L. Buckley, & C. De Boom (Eds.), Active Inference (pp. 52–58). Springer International Publishing. https://doi.org/10.1007/978-3-030-64919-7_7
Mathys, C. (2020). Playing with free energy. Neuropsychoanalysis, 22(1–2), 81–82. https://doi.org/10.1080/15294145.2021.1878615
Henco, L., Diaconescu, A. O., Lahnakoski, J. M., Brandi, M.-L., Hörmann, S., Hennings, J., Hasan, A., Papazova, I., Strube, W., Bolis, D., Schilbach, L.*, & Mathys, C.* (2020). Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder. PLOS Computational Biology, 16(9), e1008162. https://doi.org/10.1371/journal.pcbi.1008162 (*) Joint last authors.
Henco, L., Brandi, M.-L., Lahnakoski, J. M., Diaconescu, A. O., Mathys, C.*, Schilbach, L.* (2020). Bayesian modelling captures inter-individual differences in social belief computations in the putamen and insula. Cortex, 131, 221–236. https://doi.org/10.1016/j.cortex.2020.02.024 (*) Joint last authors.
Diaconescu, A. O., Wellstein, K. V., Kasper, L., Mathys, C., & Stephan, K. E. (2020). Hierarchical Bayesian models of social inference for probing persecutory delusional ideation. Journal of Abnormal Psychology, 129(6), 556–569. https://doi.org/10.1037/abn0000500
Diaconescu, A. O., Stecy, M., Kasper, L., Burke, C. J., Nagy, Z., Mathys, C., & Tobler, P. N. (2020). Neural arbitration between social and individual learning systems. ELife, 9, e54051. https://doi.org/10.7554/eLife.54051
Deserno, L., Boehme, R., Mathys, C., Katthagen, T., Kaminski, J., Stephan, K. E., Heinz, A., & Schlagenhauf, F. (2020). Volatility Estimates Increase Choice Switching and Relate to Prefrontal Activity in Schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(2), 173–183. https://doi.org/10.1016/j.bpsc.2019.10.007
Cole, D. M., Diaconescu, A. O., Pfeiffer, U. J., Brodersen, K. H., Mathys, C. D., Julkowski, D., Ruhrmann, S., Schilbach, L., Tittgemeyer, M., Vogeley, K., & Stephan, K. E. (2020). Atypical processing of uncertainty in individuals at risk for psychosis. NeuroImage: Clinical, 26, 102239. https://doi.org/10.1016/j.nicl.2020.102239
Adams, R. A., Moutoussis, M., Nour, M. M., Dahoun, T., Lewis, D., Illingworth, B., Veronese, M., Mathys, C., de Boer, L., Guitart-Masip, M., Friston, K. J., Howes, O. D., & Roiser, J. P. (2020). Variability in Action Selection Relates to Striatal Dopamine 2/3 Receptor Availability in Humans: A PET Neuroimaging Study Using Reinforcement Learning and Active Inference Models. Cerebral Cortex, 30(6), 3573–3589. https://doi.org/10.1093/cercor/bhz327
Friston, K. J., Preller, K. H., Mathys, C., Cagnan, H., Heinzle, J., Razi, A., & Zeidman, P. (2019). Dynamic causal modelling revisited. NeuroImage, 199, 730–744. https://doi.org/10.1016/j.neuroimage.2017.02.045
Paliwal, S., Mosley, P. E., Breakspear, M., Coyne, T., Silburn, P., Aponte, E., Mathys, C., & Stephan, K. E. (2019). Subjective estimates of uncertainty during gambling and impulsivity after subthalamic deep brain stimulation for Parkinson's disease. Scientific Reports, 9(1), 14795. https://doi.org/10.1038/s41598-019-51164-2
Siegel, J. Z., Mathys, C., Rutledge, R. B., & Crockett, M. J. (2018). Beliefs about bad people are volatile. Nature Human Behaviour, 2, 750–756. https://doi.org/10.1038/s41562-018-0425-1
Adams, R. A., Napier, G., Roiser, J. P., Mathys, C.*, Gilleen, J.* (2018). Attractor-like Dynamics in Belief Updating in Schizophrenia. Journal of Neuroscience, 38(44), 9471–9485. https://doi.org/10.1523/JNEUROSCI.3163-17.2018 (*) Joint last authors.
Katthagen, T., Mathys, C., Deserno, L., Walter, H., Kathmann, N., Heinz, A., & Schlagenhauf, F. (2018). Modeling subjective relevance in schizophrenia and its relation to aberrant salience. PLOS Computational Biology 14, e1006319. https://doi.org/10.1371/journal.pcbi.1006319
Mirza, M. B., Adams, R. A., Mathys, C., Friston, K. J. (2018). Human visual exploration reduces uncertainty about the sensed world. PLOS ONE 13, e0190429. doi:10.1371/journal.pone.0190429
Rigoli, F., Mathys, C., Friston, K. J., Dolan, R. J. (2017). A unifying Bayesian account of contextual effects in value-based choice. PLOS Computational Biology 13, e1005769. doi:10.1371/journal.pcbi.1005769
Powers, A. R., Mathys, C., Corlett, P. R., 2017. Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science 357(6351), 596–600. doi:10.1126/science.aan3458
Lawson, R. P., Mathys, C., Rees, G. (2017). Adults with autism overestimate the volatility of the sensory environment. Nature Neuroscience 20(9), 1293–1299. doi:10.1038/nn.4615
Diaconescu, A. O., Litvak, V., Mathys, C., Kasper, L., Friston, K. J., Stephan, K. E. (2017). A computational hierarchy in human cortex. arXiv:1709.02323 [q-bio].
Brazil, I. A., Mathys, C. D., Popma, A., Hoppenbrouwers, S. S., Cohn, M. D. (2017). Representational Uncertainty in the Brain During Threat Conditioning and the Link With Psychopathic Traits. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(8), 689–695. https://doi.org/10.1016/j.bpsc.2017.04.005
Diaconescu, A. O., Mathys, C., Weber, L. A. E., Kasper, L., Mauer, J., & Stephan, K. E. (2017). Hierarchical prediction errors in midbrain and septum during social learning. Social Cognitive and Affective Neuroscience, 12(4), 618–634. https://doi.org/10.1093/scan/nsw171
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., & Petzschner, F. H. (2016). Allostatic self-efficacy: a metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in Human Neuroscience, 10, 550. https://doi.org/10.3389/fnhum.2016.00550
Marshall, L.*, Mathys, C.*, Ruge, D., de Berker, A. O., Dayan, P., Stephan, K. E., & Bestmann, S. (2016). Pharmacological fingerprints of contextual uncertainty. PLOS Biology, 14(11), e1002575. (*) Joint first authors. https://doi.org/10.1371/journal.pbio.1002575
de Berker, A. O., Rutledge, R. B., Mathys, C., Marshall, L., Cross, G. F., Dolan, R. J., & Bestmann, S. (2016). Computations of uncertainty mediate acute stress responses in humans. Nature Communications, 7, 10996. https://doi.org/10.1038/ncomms10996
Mathys, C. (2016). How could we get nosology from computation? in: Redish, A. D., Gordon, J. A. (eds.), Computational Psychiatry: New Perspectives on Mental Illness, Strüngmann Forum Reports, vol. 20. MIT Press, Cambridge, MA, pp. 121–135.
Flagel, S. B., Pine, D. S., Ahmari, S. E., First, M. B., Friston, K. J., Mathys, C., Redish, A. D., Schmack, K., Smoller, J. W., Thapar, A. (2016). A novel framework for improving psychiatric diagnostic nosology, in: Redish, A. D., Gordon, J. A. (eds.), Computational Psychiatry: New Perspectives on Mental Illness, Strüngmann Forum Reports, vol. 20. MIT Press, Cambridge, MA, pp. 169–199.
Mirza, M. B., Adams, R. A., Mathys, C. D., & Friston, K. J. (2016). Scene construction, visual foraging, and active inference. Frontiers in Computational Neuroscience, 10, 56. https://doi.org/10.3389/fncom.2016.00056
Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., Kronbichler, M., & Friston, K. (2015). Evidence for surprise minimization over value maximization in choice behavior. Scientific Reports, 5, 16575. https://doi.org/10.1038/srep16575
Vossel, S., Mathys, C., Stephan, K. E., & Friston, K. J. (2015). Cortical Coupling Reflects Bayesian Belief Updating in the Deployment of Spatial Attention. The Journal of Neuroscience, 35(33), 11532–11542. https://doi.org/10.1523/JNEUROSCI.1382-15.2015
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015). Active inference and epistemic value. Cognitive Neuroscience 6(4), 187–214. http://dx.doi.org/10.1080/17588928.2015.1020053
Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., & Friston, K. (2015). The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes. Cerebral Cortex 25(10), 3434–3445. doi:10.1093/cercor/bhu159
Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., Wurst, F., Kronbichler, M., & Friston, K. (2015). Optimal inference with suboptimal models: Addiction and active Bayesian inference. Medical Hypotheses, 84(2), 109–117. https://doi.org/10.1016/j.mehy.2014.12.007
Mathys, C., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in Human Neuroscience, 8, 825. https://doi.org/10.3389/fnhum.2014.00825
Stephan, K. E., & Mathys, C. (2014). Computational approaches to psychiatry. Current Opinion in Neurobiology, 25, 85–92. https://doi.org/10.1016/j.conb.2013.12.007
Diaconescu, A. O., Mathys, C., Weber, L. A. E., Daunizeau, J., Kasper, L., Lomakina, E. I., Fehr, E., & Stephan, K. E. (2014). Inferring on the Intentions of Others by Hierarchical Bayesian Learning. PLOS Computational Biology, 10(9), e1003810. https://doi.org/10.1371/journal.pcbi.1003810
Hauser, T. U., Iannaccone, R., Ball, J., Mathys, C., Brandeis, D., Walitza, S., & Brem, S. (2014). Role of the medial prefrontal cortex in impaired decision making in juvenile attention-deficit/hyperactivity disorder. JAMA Psychiatry, 71(10), 1165–1173. https://doi.org/10.1001/jamapsychiatry.2014.1093
Vossel, S., Bauer, M., Mathys, C., Adams, R. A., Dolan, R. J., Stephan, K. E., & Friston, K. J. (2014). Cholinergic Stimulation Enhances Bayesian Belief Updating in the Deployment of Spatial Attention. The Journal of Neuroscience, 34(47), 15735–15742. https://doi.org/10.1523/JNEUROSCI.0091-14.2014
Vossel, S.*, Mathys, C.*, Daunizeau, J., Bauer, M., Driver, J., Friston, K. J., & Stephan, K. E. (2014). Spatial Attention, Precision, and Bayesian Inference: A Study of Saccadic Response Speed. Cerebral Cortex, 24(6), 1436–1450. (*) Joint first authors. https://doi.org/10.1093/cercor/bhs418
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013). Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning. Neuron, 80(2), 519–530. https://doi.org/10.1016/j.neuron.2013.09.009
Brodersen, K. H., Daunizeau, J., Mathys, C., Chumbley, J. R., Buhmann, J. M., & Stephan, K. E. (2013). Variational Bayesian mixed-effects inference for classification studies. NeuroImage, 76, 345–361. https://doi.org/10.1016/j.neuroimage.2013.03.008
Mathys, C. (2012). Hierarchical Gaussian filtering. ETH Zurich doctoral dissertation no. 20909. http://dx.doi.org/10.3929/ethz-a-007595146
Brodersen, K. H., Mathys, C., Chumbley, J. R., Daunizeau, J., Ong, C. S., Buhmann, J. M., & Stephan, K. E. (2012). Bayesian Mixed-Effects Inference on Classification Performance in Hierarchical Data Sets. Journal of Machine Learning Research, 13, 3133–3176.
Mathys, C., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A Bayesian foundation for individual learning under uncertainty. Frontiers in Human Neuroscience, 5, 39. https://doi.org/10.3389/fnhum.2011.00039
Mathys, C., Loui, P., Zheng, X., & Schlaug, G. (2010). Non-Invasive Brain Stimulation Applied to Heschl's Gyrus Modulates Pitch Discrimination. Frontiers in Psychology, 1, 193. https://doi.org/10.3389/fpsyg.2010.00193
Stephan, K. E., Kasper, L., Brodersen, K., & Mathys, C. (2009). Funktionelle und effektive Konnektivität [Functional and effective connectivity]. Klinische Neurophysiologie, 40(4), 222–32.
Loui, P., Guenther, F. H., Mathys, C., & Schlaug, G. (2008). Action–perception mismatch in tone-deafness. Current Biology, 18(8), R331–R332. https://doi.org/10.1016/j.cub.2008.02.045

Home
Credits