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

• 2016: Awards: Facebook Faculty Award - Virtual Reality 
• 2000: Editor roles: Assciate Editor for PLoS Comp. Biol., Editor of the Springer Publishers Encyclopedia of Computational Neuroscience (Bayesian methods section)
• 2000: 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 A. R. Beech, A. J. Carter, R. E. Mann, & P. Rotshtein (Eds.), The Wiley Blackwell Handbook of Forensic Neuroscience. Wiley
• Beierholm, U. R. (2015). Bayesian Approaches in Computational Neuroscience: Overview. In Encyclopedia of Computational Neuroscience (7-8). Springer New York. https://doi.org/10.1007/978-1-4614-6675-8_778
• Shams, L., & Beierholm, U. R. (2011). Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference : Sensory Cue Integration. In J. Trommershäuser, K. Kording, & M. S. Landy (Eds.), Sensory Cue Integration. Oxford Univ Press. https://doi.org/10.1093/acprof%3Aoso/9780195387247.001.0001

Conference Paper

• Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022). Evaluating Gaussian Grasp Maps for Generative Grasping Models.
• Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. . https://doi.org/10.1109/icpr48806.2021.9413197
• Yates, T., Larigaldie, N., & Beierholm, U. (2017). A non-parametric Bayesian prior for causal inference of auditory streaming. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. . J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (1381-1386). https://doi.org/10.1101/139188
• Beierholm, U., Kording, K., Shams, L., & Ma, W. (2008). Comparing Bayesian models for multisensory cue combination without mandatory integration. In J. C. Platt, D. Koller, Y. Singer, & S. T. Roweis (Eds.), Advances in neural information processing systems 20: Proceedings of the 21st Annual Conference on Neural Information Processing Systems 2007; December 3-6, 2007, Vancouver, B.C., Canada (81-88)

Journal Article

• Zhu, H., Beierholm, U., & Shams, L. (2024). The Overlooked Role of Unisensory Precision in Multisensory Research. Current Biology, 34(6), PR229-R231. https://doi.org/10.1016/j.cub.2024.01.057
• Devine, S., Roy, M., Beierholm, U., & Otto, A. R. (2024). Proximity to Rewards Modulates Parameters of Effortful Control Exertion. Journal of Experimental Psychology: General, https://doi.org/10.1037/xge0001561
• Aston, S., Nardini, M., & Beierholm, U. (2023). Different types of uncertainty in multisensory perceptual decision making. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1886), Article 20220349. https://doi.org/10.1098/rstb.2022.0349
• 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), Article e13306. https://doi.org/10.1111/desc.13306
• Smith, D. T., Beierholm, U., & Avery, M. (2023). A presaccadic perceptual impairment at the postsaccadic location of the blindspot. PLoS ONE, 18(9), Article e0291582. https://doi.org/10.1371/journal.pone.0291582
• 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), Article 8. https://doi.org/10.1167/jov.22.13.8
• Hird, E. J., Beierholm, U., De Boer, L., Axelsson, J., Backman, L., & Guitart-Masip, M. (2022). Dopamine and reward-related vigor in younger and older adults. Neurobiology of Aging, 118, 34-43. https://doi.org/10.1016/j.neurobiolaging.2022.06.003
• Shams, L., & Beierholm, U. (2022). Bayesian Causal Inference: A Unifying Neuroscience Theory. Neuroscience & Biobehavioral Reviews, 137, Article 104619. https://doi.org/10.1016/j.neubiorev.2022.104619
• 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. https://doi.org/10.1037/xhp0001014
• 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. https://doi.org/10.3758/s13428-021-01633-2
• ’t Hart, B., Achakulvisut, T., Adeyemi, A., Akrami, A., Alicea, B., Alonso-Andres, A., …van Viegen, T. (2022). Neuromatch Academy: a 3-week, online summer school in computational neuroscience. The journal of open source education, 5(49), Article 118. https://doi.org/10.21105/jose.00118
• Wiese, H., Anderson, D., Beierholm, U., Tuettenberg, S. C., Young, A. W., & Burton, A. M. (2022). Detecting a viewer's familiarity with a face: Evidence from event-related brain potentials and classifier analyses. Psychophysiology, 59(1), Article e13950. https://doi.org/10.1111/psyp.13950
• Spicer, J., Sanborn, A. N., & Beierholm, U. R. (2020). Using Occam's razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements. Cognitive Psychology, 122, Article 101309. https://doi.org/10.1016/j.cogpsych.2020.101309
• Wedge-Roberts, R., Aston, S., Beierholm, U., Kentridge, R., Hurlbert, A., Nardini, M., & Olkkonen, M. (2020). Specular highlights improve colour constancy when other cues are weakened. Journal of Vision, 20(12), 1-22. https://doi.org/10.1167/jov.20.12.4
• Kiryakova, R., Aston, S., Beierholm, U., & Nardini, M. (2020). Bayesian transfer in a complex spatial localization task. Journal of Vision, 20(6), Article 17. https://doi.org/10.1167/jov.20.6.17
• Beierholm, U., Rohe, T., Ferrari, A., Stegle, O., & Noppeney, U. (2020). Using the past to estimate sensory uncertainty. eLife, 9, Article e54172. https://doi.org/10.7554/elife.54172
• Jones, S. A., Beierholm, U., Meijer, D., & Noppeney, U. (2019). Older adults sacrifice response speed to preserve multisensory integration performance. Neurobiology of Aging, 84, 148-157. https://doi.org/10.1016/j.neurobiolaging.2019.08.017
• Ursino, M., Cuppini, C., Magosso, E., Beierholm, U., & Shams, L. (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. https://doi.org/10.1163/22134808-20191324
• Taheri, M., Rotshtein, P., & Beierholm, U. (2018). The effect of attachment and environmental manipulations on cooperative behavior in the prisoner’s dilemma game. PLoS ONE, 13(11), Article e0205730. https://doi.org/10.1371/journal.pone.0205730
• Odegaard, B., Beierholm, U., Carpenter, J., & Shams, L. (2018). Prior expectation of objects in space is dependent on the direction of gaze. Cognition, 182, 220-226. https://doi.org/10.1016/j.cognition.2018.10.011
• Jucker, J., Thornborrow, T., Beierholm, U., Burt, D., Barton, R., Evans, E., …Boothroyd, L. (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), Article 8438. https://doi.org/10.1038/s41598-017-08653-z
• Griffiths, B., & Beierholm, U. (2017). Opposing effects of reward and punishment on human vigor. Scientific Reports, 7, Article 42287. https://doi.org/10.1038/srep42287
• Sanborn, A., & Beierholm, U. (2016). Fast and Accurate Learning When Making Discrete Numerical Estimates. PLoS Computational Biology, 12(4), Article e1004859. https://doi.org/10.1371/journal.pcbi.1004859
• Beierholm, U. R. (2014). Bayes Optimality of Human Perception, Action and Learning: Behavioural and Neural Evidence. Lecture Notes in Computer Science, 8603, 117-129. https://doi.org/10.1007/978-3-319-12084-3
• Beierholm, U., Guitart-Masip, M., Economides, M., Chowdhury, R., Düzel, E., Dolan, R., & Dayan, P. (2013). Dopamine Modulates Reward-Related Vigor. Neuropsychopharmacology, 38, 1495-1503. https://doi.org/10.1038/npp.2013.48
• Beierholm, U. R., Anen, C., Quartz, S., & Bossaerts, P. (2011). Separate encoding of model-based and model-free valuations in the human brain. NeuroImage, 58(3), 955-962. https://doi.org/10.1016/j.neuroimage.2011.06.071
• Wunderlich, K., Beierholm, U. R., Bossaerts, P., & O'Doherty, J. P. (2011). The human prefrontal cortex mediates integration of potential causes behind observed outcomes. Journal of Neurophysiology, 106(3), 1558-1569. https://doi.org/10.1152/jn.01051.2010
• Guitart-Masip, M., Beierholm, U., Dolan, R., Duzel, E., & Dayan, P. (2011). Vigor in the Face of Fluctuating Rates of Reward: An Experimental Examination. The Journal of Cognitive Neuroscience, 23(12), 1-6
• Beierholm, U. R., & Dayan, P. (2010). Pavlovian-Instrumental Interaction in ‘Observing Behavior’. PLoS Computational Biology, 6(9), https://doi.org/10.1371/journal.pcbi.1000903
• Shams, L., & Beierholm, U. R. (2010). Causal inference in perception. Trends in Cognitive Sciences, 14(9), 1-8. https://doi.org/10.1016/j.tics.2010.07.001
• Wozny, D. R., Beierholm, U. R., & Shams, L. (2010). Probability Matching as a Computational Strategy Used in Perception. PLoS Computational Biology, 6(8), https://doi.org/10.1371/journal.pcbi.1000871
• Beierholm, U., Quartz, S., & Shams, L. (2009). Bayeisan priors are encoded independently from likelihoods in human multisensory perception. Journal of Vision, 9(5), 1-9. https://doi.org/10.1167/0.0.1.55
• Wozny, D. D. R., Beierholm, U. R., & Shams, L. (2008). Human trimodal perception follows optimal statistical inference. Journal of Vision, 8(3), https://doi.org/10.1167/8.3.24.introduction
• Körding, K., Beierholm, U., Ma, W., Quartz, S., Tenenbaum, J., & Shams, L. (2007). Causal inference in multisensory perception. PLoS ONE, 2(9), Article e943. https://doi.org/10.1371/journal.pone.0000943
• Beierholm, U. R., Jacobsen, J. C. B., Holstein-Rathlou, N., & Alstrøm, P. (2007). Characteristics of blood vessels forming 'sausages-on-a-string' patterns during hypertension. Physica A: Statistical Mechanics and its Applications, 376, 387-393. https://doi.org/10.1016/j.physa.2006.10.063
• Shams, L., Ma, W. W. J., & Beierholm, U. (2005). Sound-induced flash illusion as an optimal percept. NeuroReport, 16(17), 1923-7
• Jacobsen, J., Beierholm, U., Mikkelsen, R., Gustafsson, F., Alstrøm, P., & Holstein-Rathlou, N. (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. https://doi.org/10.1152/ajpregu.00006.2002
• 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. https://doi.org/10.1016/s0378-4371%2802%2901161-5
• Beierholm, U., Nielsen, C., 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, U. R. (2015). Bayesian models of perception