Associate Professor, Director of the Decision Sciences Collaborative
I am a neuroeconomist with a background in both the social sciences and neuroscience. My research combines tools from psychology, neuroscience and economics to investigate the mechanisms behind decision-making. We are a very interdisciplinary lab and are interested both in using economic tasks and theory to better understand cognitive neuroscience, and in using models and measures from cognitive neuroscience to do better economics.
Specific examples of research topics in the lab include:
- using eye-tracking to study the relationship between what people look at and what they choose
- using functional magnetic resonance imaging (fMRI) to see how the brain assigns value to different items and makes choices between them
- using computational models like the diffusion model (DDM) to predict people’s behavior in different tasks
- using fMRI data combined with machine learning techniques to predict mental states and improve economic institutions
- using response times to infer people’s preferences and beliefs
Ian obtained his B.S. in Physics and Business Economics at Caltech, then stayed at Caltech do his M.Sc. in Social Sciences and Ph.D. in Behavioral and Social Neuroscience with Antonio Rangel, Colin Camerer, Ralph Adolphs and John Ledyard. He was then a Postdoc for one year with Antonio Rangel and Colin Camerer, followed by two years with Ernst Fehr at the University of Zurich.
Thomas, A.W., Molter, F., Krajbich, I. (2021) Uncovering the computational mechanisms underlying many-alternative choice. eLife, 10:e57012
Stillman, P.E., Krajbich, I., Ferguson, M.J. (2020) Using dynamic monitoring of choices to predict and understand risk preferences. Proceedings of the National Academy of Sciences of the USA, 117(50): 31738-31747.
Konovalov, A. & Krajbich, I. (2020) Mouse tracking reveals structure knowledge in the absence of model-based choice Nature Communications, 11: 1893
Konovalov, A. & Krajbich, I. (2019) Revealed strength of preference: Inference from response times Judgment and Decision Making, 14(4): 381-394
Krajbich, I. (2019) Accounting for attention in sequential sampling models of decision making Current Opinion in Psychology, 29: 6-11
Smith, S. & Krajbich, I. (2019) Gaze amplifies value in decision making Psychological Science, 30(1): 116-128
Chen, F., & Krajbich, I. (2018) Biased sequential sampling underlies the effects of time pressure and delay in social decision making Nature Communications, 9:3557
Konovalov, A. & Krajbich, I. (2018) Neurocomputational Dynamics of Sequence Learning. Neuron, 98, 1-12
Chen, W.J. & Krajbich, I. (2017). Computational modeling of epiphany learning. Proceedings of the National Academy of Sciences, 114(18):4637-4642
Krajbich, I., Hare, T., Bartling, B., Morishima, Y., & Fehr, E. (2015). A common mechanism underlying food choice and social decisions. PLoS Computational Biology, 11(10): e1004371
Krajbich, I., Bartling, B., Hare, T., & Fehr, E. (2015). Rethinking fast and slow based on a critique of reaction-time reverse inference. Nature Communications, 6:7455
Krajbich, I., Oud, B., Fehr, E. (2014). Benefits of neuroeconomics modeling: New policy interventions and predictors of preference. American Economic Review: Papers & Proceedings, 104(5), 501-506
Krajbich, I., Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based choice. Proceedings of the National Academy of Sciences, 108(33), 13852-13857
Krajbich, I., Armel, C., Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292-1298
Krajbich, I., Camerer, C., Ledyard, J., Rangel, A. (2009). Using neural measures of economic value to solve the public goods free-rider problem. Science, 326(5952), 596-599