Dr Lei Zhang BSc, MSc, PhD

Dr Lei Zhang

School of Psychology
Associate Professor

Dr. Lei Zhang is a cognitive computational neuroscientist. He directs the Adaptive Learning Psychology and Neuroscience Lab, ALP(e)N Lab, which addresses the fundamental question of the “adaptive brain” by studying the cognitive, computational, and neurobiological basis of (social) learning and decision-making in health and disease (i.e. Computational Psychiatry).

Qualifications

  • PhD in Cognitive Neuroscience (summa cum laude), University of Hamburg & University Medical Center Hamburg-Eppendorf, Germany; Awarded Chinese Government Prize for Outstanding Doctoral Dissertation Abroad
  • MSc in Cognitive Neuroscience, University of the Basque Country & Basque Center on Cognition, Brain & Language, Spain
  • BSc in Psychology, Beijing Normal University, China

Biography

Dr Lei Zhang obtained his BSc in Psychology from Beijing Normal University, China, and his MSc in Cognitive Neuroscience at Basque Center on Cognition, Brain and Language, Spain. He received his PhD (summa cum laude), along with a one-year transition postdoc, with Dr Jan Gläscher at the Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany. Between 2016 and 2017, he was a Roche intern for Scientific Exchange (RiSE) at one of the research-based pharmaceutical companies, F. Hoffmann-La Roche AG, based in Basel, Switzerland. Between 2019 and 2022, he worked as a postdoctoral fellow with Prof Claus Lamm at the Social Cognitive and Affective Neuroscience Unit (SCAN-Unit), University of Vienna, Austria. Dr Zhang joined the Centre for Human Brain Health, Institute of Mental Health, and School of Psychology at University of Birmingham as an Associate Professor in winter 2022.

Teaching

Dr Lei Zhang performs research-informed teaching. He teaches an MSc-level module on Proposing Research in Psychology.

Postgraduate supervision

PhD students: Students interested in working with Dr Zhang should email him to discuss potential funding opportunities. Current funding schemes include MRC-AIM, BBSRC-MIBTP, and CSC.

Postdoc researchers: Excellent and motivated postdoc researchers are welcome to contact Dr Zhang to discuss potential projects and funding opportunities (both national and international).

Research

Learning and decision-making, flexible & adaptive (learning) behavior, decision neuroscience/neuroeconomics, social learning, reinforcement learning, cognitive modeling, Bayesian methods, neuroimaging, fMRI, TMS, pain, aversive learning, computational psychiatry

Other activities

Awards

  • Rising Star Award, Association for Psychological Science (APS), 2022
  • Commendation Award for Open Science, European Society for Cognitive and Affective Neuroscience (ESCAN), 2022
  • Open & Reproducible Science Award, Society for Social Neuroscience (S4SN), 2021
  • Trainee Professional Development Award, Society for Neuroscience (SfN), 2020
  • Teaching Award for Early Career Researchers, University of Vienna, 2020
  • Fellow, Kavli Summer Institute in Cognitive Neuroscience (“Brain Camp”), Santa Barbara, CA, USA, 2019

Responsibilities

  • eLife Community Ambassador (2022-2024)
  • OHBM Student and Postdoc Special Interest Group (2021-2023)
  • S4SN Open Science Committee, Society for Social Neuroscience (2021–2022)
  • ALBA Network Member for diversity and equality in neurosciences (2021-present)
  • COS Ambassador, Center for Open Science (2020–present)
  • Steering committee member, Chinese Open Science Network (2018–present)

Publications

Key Publications

Zhao, Y., Zhang, L., Rütgen, M., Sladky, R., & Lamm, C. (2021). Neural dynamics between anterior insular cortex and right supramarginal gyrus dissociate genuine affect sharing from perceptual saliency of pretended pain. eLife, 10, e69994. DOI: 10.7554/eLife.69994, Preprint: 10.1101/2021.04.30.441951

Zhang, L., Gläscher, J. (2020). A brain network supporting social influences in human decision-making. Science Advances, 6(34), eabb4159. DOI: 10.1126/sciadv.abb4159. Preprint: 10.1101/551614. [Featured by: Soltani, A. (2020). Learning from Others, but with What Confidence? Trends in Cognitive Sciences. 24(12), 963–964.DOI: 10.1016/j.tics.2020.09.011]

Zhang, L.*, Lengersdorff, L.*, Mikus, N., Gläscher, J., & Lamm, C. (2020). Using reinforcement learning models in social neuroscience: frameworks, pitfalls, and suggestions of best practices. Social Cognitive and Affective Neuroscience, 15(6), 695–707. DOI: 10.1093/scan/nsaa089. Preprint: 10.31234/osf.io/uthw2

Crawley, D.*, Zhang, L.*, Emily, J., …, den Ouden, H., Loth, E., & the EU-AIMS LEAP group (2020). Modeling cognitive flexibility in autism spectrum disorder and typical development reveals comparable developmental shifts in learning mechanisms. PLOS Biology, 18(10), e3000908. DOI: 10.1371/journal.pbio.3000908. Preprint: 10.31234/osf.io/h7jcm

Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1, 24–57. DOI: 10.1162/CPSY_a_00002. Preprint: 10.1101/064287

 

Journal articles

Jin, H., Wang, Q., Yang, Y. F., Zhang, H., Gao, M., Jin, S., ..., Zhang, L., Zuo, X.-N., & Chuan-Peng, H. (in press). Chinese Open Science Network (COSN): Building an Open Science community from scratch. Advances in Methods and Practices in Psychological Science. Preprint: 10.31234/osf.io/ac9by

Zhao, Y., Zhang, L., Rütgen, M., Sladky, R., & Lamm, C. (2022). Effective connectivity reveals distinctive patterns in response to others' genuine affective experience of disgust. NeuroImage, 259, 119404. DOI: 10.1016/j.neuroimage.2022.119404. Preprint: 10.1101/2021.09.03.458875

Mitter, B., Zhang, L., Bauer, P., Baca, A., & Tschan, H. (2022). Modeling the Relationship between Load and Repetitions to Failure in Resistance Training: A Bayesian Analysis. European Journal of Sport Science. DOI: 10.1080/17461391.2022.2089915

’t Hart, BM., Achakulvisut, T., …, Zhang, L., …, d'Oleire Uquillas, F., & van Viegen, T. (2022). Neuromatch Academy: a 3-week, online summer school in computational neuroscience. Journal of Open Source Education, 5(49), 118. DOI: 10.21105/jose.00118

Geng, H.-Y., Chen, J., Hu, C.-P., Jin, J.-W., Chan, R. C. K., Li, Y., Hu, X.-Q., Zhang, R.-Y., & Zhang, L. (2022). Promoting Computational Psychiatry in China. Nature Human Behaviour, 6(5), 615–617. DOI: 10.1038/s41562-022-01328-4

Zhou, L., Zou, T., Zhang, L., Zhang, Y. Y., & Liang, Z.-Y. (2021). "Carpe diem?": Disjunction effect of incidental affect on intertemporal choice. Frontiers in Psychology, 5896. DOI: 10.3389/fpsyg.2021.782472

Schmalz, X., Manresa, J., & Zhang, L. (2021). What is a Bayes Factor? Psychological Methods. DOI: 10.1037/met0000421. Preprint: 10.31219/osf.io/vgqbt

Joue, G., Chakroun, K., Bayer, J., Gläscher, J., Zhang, L., Fuss, J., Hennies, N., Sommer, T. (2021). Sex differences and exogenous estrogen influence learning and brain responses to prediction errors. Cerebral Cortex, 32(9), 2022–2036. DOI: 10.1093/cercor/bhab334

Zhao, Y., Zhang, L., Rütgen, M., Sladky, R., & Lamm, C. (2021). Neural dynamics between anterior insular cortex and right supramarginal gyrus dissociate genuine affect sharing from perceptual saliency of pretended pain. eLife, 10, e69994. DOI: 10.7554/eLife.69994, Preprint: 10.1101/2021.04.30.441951

Yao, Y.-W., Chopurian, V., Zhang, L., Lamm, C., & Heekeren, H. (2021). Effects of non-invasive brain stimulation on visual perspective taking: A meta-analytic study. NeuroImage, 242, 118462. DOI: 10.1016/j.neuroimage.2021.118462, Preprint: s10.1101/2021.04.24.441219

Kreis, I., Zhang, L., Moritz, S. & Pfuhl, G. (2021). Spared performance but increased uncertainty in schizophrenia: Evidence from a probabilistic decision-making task. Schizophrenia Research, 243, 414–423. DOI: 10.1016/j.schres.2021.06.038, Preprint: 10.31219/osf.io/qaupb

Zhao, Y., Rütgen, M., Zhang, L., & Lamm, C. (2021). Pharmacological fMRI provides evidence for opioidergic modulation of discrimination of facial pain expressions. Psychophysiology, 58(2), e13717. DOI: 10.1111/psyp.13717. Preprint: 10.31234/osf.io/uj75y

Crawley, D.*, Zhang, L.*, Emily, J., …, den Ouden, H., Loth, E., & the EU-AIMS LEAP group (2020). Modeling cognitive flexibility in autism spectrum disorder and typical development reveals comparable developmental shifts in learning mechanisms. PLOS Biology, 18(10), e3000908. DOI: 10.1371/journal.pbio.3000908. Preprint: 10.31234/osf.io/h7jcm

Zhang, L., Gläscher, J. (2020). A brain network supporting social influences in human decision-making. Science Advances, 6(34), eabb4159. DOI: 10.1126/sciadv.abb4159. Preprint: 10.1101/551614. [Featured by: Soltani, A. (2020). Learning from Others, but with What Confidence? Trends in Cognitive Sciences. 24(12), 963–964.DOI: 10.1016/j.tics.2020.09.011]

Zhang, L.*, Lengersdorff, L.*, Mikus, N., Gläscher, J., & Lamm, C. (2020). Using reinforcement learning models in social neuroscience: frameworks, pitfalls, and suggestions of best practices. Social Cognitive and Affective Neuroscience, 15(6), 695–707. DOI: 10.1093/scan/nsaa089. Preprint: 10.31234/osf.io/uthw2

Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., ..., Zhang, L., …, Nichols T. E., Poldrack, R. A., Schonberg T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582, 84–88. DOI: 10.1038/s41586-020-2314-9. Preprint: 10.1101/843193

Bayer, J., Rusch, T., Zhang, L., Gläscher, J., & Sommer, T. (2020). Dose-dependent effects of estrogen on prediction error related neural activity in the nucleus accumbens of healthy young women. Psychopharmacology, 237(3), 745–755. DOI: 10.1007/s00213-019-05409-7

Zhou, L., Li. AM., Zhang, L., Li, S., & Liang, Z.-Y. (2019). Similarity in processes of risky choice and intertemporal choice: The case of certainty effect and immediacy effect. Acta Psychologica Sinica. 51(3), 337–352. DOI: 10.3724/SP.J.1041.2019.00337

Hu, Y., He, L.*, Zhang, L.*, Wölk, T., Dreher, J. C., & Weber, B. (2018). Spreading inequality: Neural computations underlying paying-it-forward reciprocity. Social Cognitive and Affective Neuroscience, 13(6), 578–589. DOI: 10.1093/scan/nsy040

Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1, 24–57. DOI: 10.1162/CPSY_a_00002. Preprint: 10.1101/064287

Commentaries, books & book chapters

Zhang, L. (2022). Examining mental disorders with cognitive computational neuroscience. Nature Reviews Psychology. DOI: 10.1038/s44159-022-00131-2

Zhang, L., Redžepović, S., Rose, M., & Gläscher, J. (2018). Zen and the Art of Making a Bayesian Espresso. Neuron, 98(6), 1066–1068. DOI: 10.1016/j.neuron.2018.06.023

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