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 drift-diffusion model (DDM) to predict peoples' behavior in different tasks
- using brain stimulation techniques like transcranial direct current stimulation (tDCS) to influence how the brain makes decisions
- using brain-damaged and psychiatric patients to understand abnormalities in decision-making
- using fMRI data combined with machine learning techniques to predict mental states and improve economic institutions
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.
Chen, W.J. & Krajbich, I. (2017). Computational modeling of epiphany learning. PNAS. DOI:
Konovalov, A. & Krajbich, I. (2017). Money in the Bank: Distortive Effects of Accumulated Earnings on Risky Choice. Neuron, 93(3), 473-475.
Enax, L., Krajbich, I., & Weber, B. (2016). Salient nutrition labels increase the integration of health attributes in food decision-making. Judgment and Decision Making, 11(5), 460-471. [pdf]
Konovalov, A. & Krajbich, I. (2016). Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning. Nature Communications, 7. DOI: 10.1038/ncomms12438
Krajbich, I., Camerer, C., & Rangel, A. (2016). Exploring the scope of neurometrically informed mechanism design. Games and Economic Behavior. DOI: 10.1016/j.geb.2016.05.001
Konovalov, A. & Krajbich, I. (2016). Over a decade of neuroeconomics: What have we learned? Organizational Research Methods. DOI: 10.1177/1094428116644502
Ashby, N.J.S., Johnson, J.G., Krajbich, I., & Wedel, M. (2016). Applications and innovations of eye-movement research in judgment and decision making. Journal of Behavioral Decision Making 29(96-10)2, DOI: 10.1002/bdm.1956
Oud*, B., Krajbich*, I., Miller, K., Cheong, J.H., Botvinick*, & M., Fehr*, E. (2016). Irrational time allocation in decision making. Proceedings of the Royal Society B, 283(1822). *joint first and last authorship
Krajbich, I. & Smith, S. M. (2015). Modeling eye movements and response times in consumer choice. Journal of Agricultural & Food Industrial Organization, 13(1), 55-72.
Krajbich, I., & Dean, M. (2015). How can neuroscience inform economics? Current Optinion in Behavioral Sciences, 5, 51-57.
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.
Chumbley, J. R., Krajbich, I., Engelmann, J. B., Russell, E., Van Uum, S., Koren, G., & Fehr, E. (2014). Endogenous Cortisol Predicts Decreased Loss Aversion in Young Men. Psychological Science, 25(11), 2102-2105.
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. [pdf]
Polania, R., Krajbich, I., Grueschow, M., Ruff, C. (2014). "Neural oscillations and synchronization differentially support evidence accumulation in perceptual and value-based decision-making." Neuron, 82, 709-720
Krajbich, I., Lu, D., Camerer, C., Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3(193), 1-18.
Wang, S., Krajbich, I., Adolphs, R., Tsuchiya, N. (2012). The role of risk aversion in non-conscious decision making. Frontiers in Psychology, 3(50), 1-17.
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 of the USA, 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. [pdf]
Krajbich, I., Adolphs, R., Tranel, D., Denburg, N., Camerer, C. (2009). Economic games quantify diminished sense of guilt in patients with damage to the prefrontal cortex. The Journal of Neuroscience, 29(7), 2188-2192.
Hsu, M., Krajbich, I., Zhao, C., Camerer, C. (2009). Neural response to reward anticipation under risk is nonlinear in probabilities. The Journal of Neuroscience, 29(7), 2231-2237.
Kang, M.J., Hsu, M., Krajbich, I., Loewenstein, G., McClure, S., Wang, J.T., Camerer, C. (2009). The wick in the candle of learning: Epistemic curiosity activates reward circuitry and enhances memory. Psychological Science, 20(8), 963-973.
Curriculum Vitae [pdf]
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