Staff profile

I am very interested in how the nervous system deals with uncertainty, whether in perception, decision making or learning. We use computational ideas (e.g. bayesian inference or reinforcement learning) to model this and test these ideas using several techniques such as psychphysics, fMRI, pharmacology etc.

• Computational Neuroscience, Perception, Decision Making, Neuroeconomics, Machine Learning

• Cognitive Neuroscience
• Learning, Memory & Cognition

• 2016: Awards: Facebook Faculty Award - Virtual Reality ;
• 0000: Editor roles: Assciate Editor for ;PLoS Comp. Biol., Editor of the Springer Publishers Encyclopedia of Computational Neuroscience (Bayesian methods section)
• 0000: Workshop organising: Co-organised the Durham Computational Biology Symposium (Durham, Nov 2018).

  Co-organisedthe Durham Probabilistic Brain Workshop (Durham, Mar 2018).

  Co-organised the Computational Models of Social Interaction Workshop (Birmingham, Oct 2014)

  Organised the Human Decision Making Workshop (Birmingham, Oct 2012)

Chapter in book

• Reniers, R., Beierholm, U. & Wood, S. (2018). Reward sensitivity and behavioural control: neuroimaging evidence for brain systems underlying risk-taking behaviour. In The Wiley Blackwell Handbook of Forensic Neuroscience. Beech, Anthony R., Carter, Adam J., Mann, Ruth E. & Rotshtein, Pia Wiley-Blackwell.
• Beierholm, Ulrik R. (2015). Bayesian Approaches in Computational Neuroscience: Overview. In Encyclopedia of Computational Neuroscience. Springer New York. 7-8.
• Shams, Ladan & Beierholm, Ulrik R. (2011). Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference Sensory Cue Integration. In Sensory Cue Integration. Trommershäuser, Julia, Kording, Konrad & Landy, Michael S. Oxford Univ Press.

Conference Paper

• Prew, W., Breckon, T.P., Bordewich, M.J.R. & Beierholm, U. (2022), Evaluating Gaussian Grasp Maps for Generative Grasping Models, Proc. Int. Joint Conf. Neural Networks. Padova, Italy, IEEE.
• Prew, W., Breckon, T.P., Bordewich, M.J.R. & Beierholm, U. (2021), Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss, 25th International Conference on Pattern Recognition (ICPR 2020). Milan, Italy, IEEE.
• Yates, Tim, Larigaldie, Nathanael & Beierholm, Ulrik (2017), A non-parametric Bayesian prior for causal inference of auditory streaming, in Gunzelmann, G., Howes, A., Tenbrink, T. & Davelaar, E. J. eds, Annual Conference of the Cognitive Science Society. London, UK, Cognitive Science Society, London, 1381-1386.
• Beierholm, U.R., Kording, K.P., Shams, L. & Ma, W.J. (2008), Comparing Bayesian models for multisensory cue combination without mandatory integration, in Platt, J.C., Koller, D., Singer, Y. & Roweis, S.T. eds, Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007. Vancouver, BC, Curran Associates, Red Hook, NY, 81-88.

Journal Article

• Wedge-Roberts, R, Aston, S, Beierholm, U, Kentridge, R, Hurlbert, A, Nardini, M & Olkkonen, M (2023). Developmental changes in colour constancy in a naturalistic object selection task. Developmental Science 26(2): e13306.
• Aston, S., Pattie, C., Graham, R., Slater, H., Beierholm, U. & Nardini, M. (2022). Newly learned shape-colour associations show signatures of reliability-weighted averaging without forced fusion or a memory colour effect. Journal of Vision 22(13): 8.
• Hird, Emily J, Beierholm, Ulrik, De Boer, Lieke, Axelsson, Jan, Backman, Lars & Guitart-Masip, Marc (2022). Dopamine and reward-related vigor in younger and older adults. Neurobiology of Aging 118: 34-43.
• Shams, Ladan & Beierholm, Ulrik (2022). Bayesian Causal Inference: A Unifying Neuroscience Theory. Neuroscience and Biobehavioral Reviews 137: 104619.
• Aston, S., Beierholm, U. & Nardini, M. (2022). Newly learned novel cues to location are combined with familiar cues but not always with each other. Journal of Experimental Psychology: Human Perception and Performance 48(6): 639-652.
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• Aston, S, Negen, J, Nardini, M & Beierholm, U (2022). Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data. Behavior Research Methods 54(1): 508-521.
• Wiese, Holger, Anderson, Dasha, Beierholm, Ulrik, Tuettenberg, Simone C., Young, Andrew W. & Burton, A. Mike (2022). Detecting a viewer's familiarity with a face: Evidence from event-related brain potentials and classifier analyses. Psychophysiology 59(1): e13950.
• Beierholm, U., Rohe, T., Ferrari, A. Stegle, O. & Noppeney, U. (2020). Using the past to estimate sensory uncertainty. eLife 9: e54172.
• Wedge-Roberts, Rebecca, Aston, Stacey, Beierholm, Ulrik, Kentridge, Robert, Hurlbert, Anya, Nardini, Marko & Olkkonen, Maria (2020). Specular highlights improve colour constancy when other cues are weakened. Journal of Vision 20(12): 1-22.
• Spicer, Jake, Sanborn, Adam N. & Beierholm, Ulrik R. (2020). Using Occam's razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements. Cognitive Psychology 122: 101309.
• Kiryakova,R, Aston,S, Beierholm,U & Nardini,M (2020). Bayesian transfer in a complex spatial localization task. Journal of Vision 20(6): 17.
• Jones, Samuel A., Beierholm, Ulrik, Meijer, David & Noppeney, Uta (2019). Older adults sacrifice response speed to preserve multisensory integration performance. Neurobiology of Aging 84: 148-157.
• Ursino, Mauro, Cuppini, Cristiano, Magosso, Elisa, Beierholm, Ulrik & Shams, Ladan (2019). Explaining the Effect of Likelihood Manipulation and Prior through a Neural Network of the Audiovisual Perception of Space. Multisensory Research 32(2): 111–144.
• Odegaard, Brian, Beierholm, Ulrik, Carpenter, Jason & Shams, Ladan (2019). Prior expectation of objects in space is dependent on the direction of gaze. Cognition 182: 220-226.
• Taheri, Maliheh, Rotshtein, Pia & Beierholm, Ulrik (2018). The effect of attachment and environmental manipulations on cooperative behavior in the prisoner’s dilemma game. PLOS ONE 13(11): e0205730.
• Jucker, J. L., Thornborrow, T., Beierholm, U., Burt, D. M., Barton, R. A., Evans, E. H., Jamieson, M. & Boothroyd, L. G. (2017). Nutritional status and the influence of TV consumption on female body size ideals in populations recently exposed to the media. Scientific Reports 7(1): 8438.
• Griffiths, Benjamin & Beierholm, Ulrik (2017). Opposing effects of reward and punishment on human vigor. Scientific Reports 7(1): 42287
• Sanborn, Adam & Beierholm, Ulrik (2016). Fast and Accurate Learning When Making Discrete Numerical Estimates. PLOS Computational Biology 12(4): e1004859.
• Beierholm, Ulrik R (2014). Bayes Optimality of Human Perception, Action and Learning: Behavioural and Neural Evidence. Lecture notes in computer science 8603: 117-129.
• Beierholm, Ulrik, Guitart-Masip, Marc, Economides, Marcos, Chowdhury, Rumana, Düzel, Emrah, Dolan, Ray & Dayan, Peter (2013). Dopamine Modulates Reward-Related Vigor. Neuropsychopharmacology official publication of the American College of Neuropsychopharmacology 38: 1495-1503.
• Wunderlich, Klaus, Beierholm, Ulrik R., Bossaerts, Peter & O'Doherty, John P. (2011). The human prefrontal cortex mediates integration of potential causes behind observed outcomes. Journal of neurophysiology 106(3): 1558-1569.
• Beierholm, Ulrik R., Anen, Cedric, Quartz, Steven & Bossaerts, Peter (2011). Separate encoding of model-based and model-free valuations in the human brain. NeuroImage 58(3): 955-962.
• Guitart-Masip, M, Beierholm, UR, Dolan, R, Duzel, E & Dayan, P (2011). Vigor in the Face of Fluctuating Rates of Reward: An Experimental Examination. Journal of Cognitive Neuroscience 23(12): 1-6.
• Wozny, David R., Beierholm, Ulrik R. & Shams, Ladan (2010). Probability Matching as a Computational Strategy Used in Perception. PLoS Computational Biology 6(8): e1000871.
• Beierholm, Ulrik R. & Dayan, Peter (2010). Pavlovian-Instrumental Interaction in ‘Observing Behavior’. PLoS Computational Biology 6(9): e1000903.
• Shams, Ladan & Beierholm, Ulrik R. (2010). Causal inference in perception. Trends in Cognitive Sciences 14(9): 1-8.
• Beierholm, UR, Quartz, SR & Shams, L. (2009). Bayeisan priors are encoded independently from likelihoods in human multisensory perception. Journal of Vision 9(5): 1-9.
• Wozny, D.R. David R, Beierholm, Ulrik R & Shams, Ladan (2008). Human trimodal perception follows optimal statistical inference. Journal of Vision 8(3): 24.
• Körding, K.P., Beierholm, U., Ma, W.J., Quartz, S., Tenenbaum, J.B. & Shams, L. (2007). Causal inference in multisensory perception. PLoS One 2(9): e943.
• Beierholm, Ulrik Ravnsborg, Jacobsen, Jens Christian Brings, Holstein-Rathlou, Niels-Henrik & Alstrøm, Preben (2007). Characteristics of blood vessels forming 'sausages-on-a-string' patterns during hypertension. Physica A 376: 387-393.
• Shams, Ladan, Ma, W.J. Wei Ji & Beierholm, Ulrik (2005). Sound-induced flash illusion as an optimal percept. Neuroreport 16(17): 1923-7.
• Jacobsen, J.C.B., Beierholm, U., Mikkelsen, R., Gustafsson, F., Alstrøm, P. & Holstein-Rathlou, N.H. (2002). “Sausage-string” appearance of arteries and arterioles can be caused by an instability of the blood vessel wall. American Journal of Physiology - Regulatory, Integrative and Comparative Physiology 283(5): R1118-R1130.
• Alstrom, P, Beierholm, U, Nielsen, C, Ryge, J & Kiehn, O (2002). Reliability of neural encoding. Physica A: Statistical Mechanics and its Applications 314(1-4): 61-68.
• Beierholm, U., Nielsen, C.D. D, Ryge, J., Alstrøm, P. & Kiehn, O. (2001). Characterization of reliability of spike timing in spinal interneurons during oscillating inputs. Journal of neurophysiology 86(4): 1858-68.

Other (Print)

• Beierholm, Ulrik R. (2015). Bayesian models of perception. 2236-2239.