Primary Area: Cognitive Psychology
Research and Teaching Interests:
When people make decisions, they don’t immediately know what to choose. Instead, they evaluate their options over time, gathering and comparing evidence in their support. This choice process relies on visual attention, memory, reward associations, goal alignment, strategic considerations, etc. When it’s less obvious which option to choose, people take longer to decide and shift their attention back and forth between the options.
In the Krajbich lab, we study the choice process to better understand people’s preferences. We develop and test mathematical models of the choice process, drawing on insights from economics, psychology, neuroscience, and marketing. These models incorporate choice-process measures like response times, eye tracking, mouse tracking, and brain imaging, to better predict people’s choices. We study all types of choices, but mostly ones having to do with preferences. These decisions range from choosing between snack foods in the lab, to bargaining over goods on eBay. A key insight from our work is that decision-making is a continuously evolving, noisy process, and so can yield systematically different choices over time. This challenges the standard notion that people have stable preferences, but also the behavioral notion that people use heuristics or rules to make their decisions.
Ian obtained his B.S. in Physics and Business Economics & Management at Caltech, then stayed at Caltech do his M.Sc. in Social Sciences and Ph.D. in Behavioral and Social Neuroscience under the guidance of Antonio Rangel, Colin Camerer, Ralph Adolphs and John Ledyard. After leaving Caltech, he spent 2011-2013 in Switzerland at the University of Zurich doing a postdoc with Ernst Fehr. From 2013-2023, he was a faculty member at The Ohio State University (OSU) in the Department of Psychology and the Department of Economics. From 2019-2023 he also served as the Director of OSU’s Decision Sciences Collaborative.Curriculum Vitae
- Shevlin, B., Smith, S.M., Hausfeld, J., Krajbich, I. (2022) High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity. Proceedings of the National Academy of Sciences of the USA, doi.org/10.1073/pnas.2101508119
- Yang, X. & Krajbich, I. (2022) A dynamic computational model of gaze and choice in multi-attribute decisions. Psychological Review, doi.org/10.1037/rev0000350
- Desai, N. & Krajbich, I. (2021) Decomposing preferences into predispositions and evaluations. Journal of Experimental Psychology: General, doi.org/10.1037/xge0001162
- Frydman, C. & Krajbich, I. (2021) Using response times to infer others’ private information: An application to information cascades. Management Science, doi/10.1287/mnsc.2021.3994
- 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
- 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
- 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
Ian Krajbich's Lab Website