Laboratory for Uncertainty, Collective Intelligence and Decision-Making (LUCID) Laboratory for Uncertainty, Collective Intelligence and Decision-Making (LUCID)

Selected publications

Journal article

Liakoni, V., Lehmann, M. P., Modirshanechi, A., Brea, J., Lutti, A., Gerstner, W., & Preuschoff, K. (2022). Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making. NeuroImage, 246, 118780. https://doi.org/10.1016/j.neuroimage.2021.118780.

Williams, T. B., Burke, C. J., Nebe, S., Preuschoff, K., Fehr, E., & Tobler, P. N. (2021). Testing models at the neural level reveals how the brain computes subjective value. Proceedings of the National Academy of Sciences, 118 (43), e2106237118. https://doi.org/10.1073/pnas.2106237118.

Loued-Khenissi, L., & Preuschoff, K. (2020). A Bird's eye view from below: Activity in the temporo-parietal junction predicts from-above Necker Cube percepts. Neuropsychologia, 149, 107654. https://doi.org/10.1016/j.neuropsychologia.2020.107654.

Lauffs, M. M., Geoghan, S. A., Favrod, O., Herzog, M. H., & Preuschoff, K. (2020). Risk prediction error signaling: A two-component response? NeuroImage, 214, 116766. https://doi.org/10.1016/j.neuroimage.2020.116766.

Loued-Khenissi, L., Preuschoff, K., Pfeuffer, A., & Einhäuser, W. (2020). Anterior insula reflects surprise in value-based decision-making and perception. NeuroImage, 116549. https://doi.org/10.1016/j.neuroimage.2020.116549.

Loued-Khenissi, L., & Preuschoff, K. (2020). Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00005.

Kometer, M., Miendlarzewska, E. A., & Preuschoff, K. (2019). Neurofinance. Organizational Research Methods, 22 (1), 196-222. https://doi.org/10.1177/1094428117730891.

Döll, O., Loued-Khenissi, L., & Preuschoff, K. (2019). An Overview of Functional Magnetic Resonance Imaging Techniques for Organizational Research. Organizational Research Methods, 22 (1), 17-45. https://doi.org/10.1177/1094428118802631.

Lehmann, M. P., Xu, H. A., Liakoni, V., Herzog, M. H., Gerstner, W., & Preuschoff, K. (2019). One-shot learning and behavioral eligibility traces in sequential decision making. eLife, 8. https://doi.org/10.7554/eLife.47463.

Faraji, M., Preuschoff, K., & Gerstner, W. (2018). Balancing New against Old Information: The Role of Puzzlement Surprise in Learning. Neural Computation, 30 (1), 34-83. https://doi.org/10.1162/neco_a_01025.

Loued-Khenissi, L., & Preuschoff, K. (2015). Apathy and noradrenaline; silent partners to mild cognitive impairment in parkinsons's disease? Current Opinion in Neurology, 28 (4), 344-350. https://doi.org/10.1097/WCO.0000000000000218.

Preuschoff, K., Mohr, P. N. C., & Hsu, M. (2013). Decision making under uncertainty. Frontiers in Neuroscience, 7. https://doi.org/10.3389/fnins.2013.00218.

Preuschoff, K., Rudorf, S., & Weber, B. (2012). Neural Correlates of Anticipation Risk Reflect Risk Preferences. Journal of Neuroscience, 32 (47), 16683-16692. https://doi.org/10.1523/JNEUROSCI.4235-11.2012.

Academic meeting presentation

Preuschoff, K. (2018). Uncertainty and surprise in decision making and learning. The Brain Conferences : Computational Neuroscience of Prediction.

Preuschoff, K. (2017). Decision Neuroscience Meets Finance. 4th Neurofinance meeting.

Preuschoff, K. (2017). Humans utilize an eligibility trace when learning sequential decisions from reward. Reinforcement Learning and Decision Making.

Preuschoff, K. (2017). The eligibility trace in human learning. BMI Symposium @ EPFL.

Preuschoff, K. (2015). A Biological Plausible 3-factor Learning Rule for Expectation Maximization in Reinforcement Learning and Decision Making. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM).

Preuschoff, K. (2015). Computational approaches to choice under uncertainty. Symposium on Biology of Decision Making.

Book chapter

Lebreton, M., & Preuschoff, K. (2019). Neuroeconomics: data analysis. Handbook of Research Methods and Applications in Experimental Economics. Edward Elgar.

Publicly available material describing new curricula or courses

Girardin, M., Berrada, T. N., Chaieb, I., Gibson Brandon, R. N., Hoesli, M. E. R., Krueger, P., ... & Valta, P. (2016). Make Smart Investment Decisions in a Global World. Coursera, Geneva, Switzerland.

Doctoral theses

Falahi, M. (2020). Golf Theory, a unifying framework for studying human choice behavior [Doctoral Thesis]. https://doi.org/10.13097/archive-ouverte/unige:155070

Doell, O. (2019). Four essays in behavioral economics and information security [Doctoral Thesis]. https://doi.org/10.13097/archive-ouverte/unige:122601

Other

Preuschoff, K. (2022). Value representations: Fast and slow. Neuron.

Lehmann, M., Xu, H., Liakoni, V., Preuschoff, K., Gerstner, W., & Herzog, M. (2017). Evidence for eligibility traces in human learning., Switzerland.