Dr. Ian Krajbich

Assistant Professor

Decision Psychology, Cognitive Psychology, Economics

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 veryinterdisciplinary 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’s background

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.


Ian’s CV

Ian’s lab webpage


Selected publications:

Smith, S. & Krajbich, I. (2018) “Gaze amplifies value in decision making” Psychological Science, in press

Chen, F., & Krajbich, I. (2018) “Biased sequential sampling underlies the effects of time pressure and delay in social decision making” Nature Communications, in press

Smith, S., & Krajbich, I. (2018) “Attention and Choice Across Domains” Journal of Experimental Psychology: General, in press

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

Konovalov, A. & Krajbich, I. (2016). Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning. Nature Communications, 7:12438

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 preferenceAmerican 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

Full list of publications