Working Papers/Pre-Prints
Krajbich, I. (2021). Multi-parameter utility and drift-rate functions conflate attribute weights and choice consistency. PsyArXiv
Cotet, M., Krajbich, I. (2021). Response times in the wild: eBay sellers take hours longer to reject high offers and accept low offers. Social Science Electronic Publishing
Journal Articles
Yang, X., Retzler, C., Krajbich, I., Ratcliff, R., Philiastides, M.G., (2024) Attention to brand labels affects, and is affected by, evaluations of product attractiveness. Frontiers in Behavioral Economics, 2, 1274815
Berlinghieri, R., Krajbich, I., Maccheroni, F., Marinacci, M., Pirazzini, M. (2023) Measuring utility with diffusion models. Science Advances, 9(34)
Konovalov, A., Krajbich, I. (2023) Decision Times Reveal Private Information in Strategic Settings: Evidence from Bargaining Experiments. The Economic Journal, https://doi.org/10.1093/ej/uead055
Smith, S.M., Krajbich, I. (2022) Predictions and choices for others: Some insights into how and why they differ. Journal of Experimental Psychology: General, https://doi.org/10.1037/xge0001288
Shevlin, B., Smith, S.M., Hausfeld, J., Krajbich, I. (2022) Reply to Pirrone and Tsetsos: Robust evidence for enhanced high-value sensitivity. Proceedings of the National Academy of Sciences of the USA, 119 (36) e2209521119
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,119 (6) e2101508119
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. (2022) Decomposing preferences into predispositions and evaluations. Journal of Experimental Psychology: General, 151(8), 1883–1903.
Hascher, J., Desai, N., Krajbich, I. (2021) Incentivized and non-incentivized liking ratings outperform willingness-to-pay in predicting choice. Judgment and Decision Making, 16(6), 1464-1484
Yang, X., Krajbich, I. (2021). Webcam-based online eye-tracking for behavioral research. Judgment and Decision Making, 16(6), 1485-1505
- See Resources for video tutorials
Krajbich, I. (2021) No camera needed with MR-based eye tracking. Nature Neuroscience, 24, 1641-1642
Krajbich*, I., Mitsumasu*, A., Polania, R., Ruff*, C., Fehr*, E. (2021) A causal role for the right frontal eye fields in value comparison. eLife, 10:e67477 *joint first and last authors
Shevlin, B.R.K., Krajbich, I. (2021). Attention as a source of variability in decision-making: Accounting for overall-value effects with diffusion models. Journal of Mathematical Psychology, 105, 102594
Weillbächer, R.A., Krajbich, I., Rieskamp, J., Gluth, S. (2021) The influence of visual attention on memory-based preferential choice. Cognition, 215, 104804
Smith, S., & Krajbich, I. (2021) Mental representations distinguish value-based decisions from perceptual decisions. Psychonomic Bulletin & Review, 28(4), 1413-1422
Thomas, A.W., Molter, F., Krajbich, I. (2021) Uncovering the computational mechanisms underlying many-alternative choice. eLife, 10:e57012
Frydman, C., Krajbich, I. (2021) Using Response Times to Infer Others’ Private Information: An Application to Information Cascades. Management Science, in press
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
Rramani, Q., Krajbich, I., Enax, L., Brustkern, L., Weber, B. (2020) Salient nutrition labels shift peoples’ attention to healthy foods and exert more influence on their choices. Nutrition Research, 80:106-116
Konovalov, A., & Krajbich, I. (2020) Mouse tracking reveals structure knowledge in the absence of model-based choice. Nature Communications, 11: 1893
Gwinn, R., & Krajbich, I. (2020) Attitudes and attention. Journal of Experimental Social Psychology, 86
Cooper, D.J., Krajbich, I., Noussair, C.N. (2019) Choice-process data in experimental economics. Journal of the Economic Science Association, 5(1) 1-13
Smith, S., & Krajbich, I. (2019) Gaze-informed modeling of preference learning and prediction. Journal of Neuroscience, Psychology, and Economics,12(3-4):143-158
Konovalov, A., & Krajbich, I. (2019) Revealed strength of preference: Inference from response times. Judgment and Decision Making, 14(4) 381-394
Smith, S., Krajbich, I., Webb, R. (2019) Estimating the dynamic role of attention via random utility. Journal of the Economic Science Association, 5(1) 97-111
Thomas, A.W., Molter, F., Krajbich, I., Heekeren, H.R., Mohr, P.N.C (2019) Gaze bias differences capture individual choice behavior. Nature Human Behaviour 3 625-635
Roberts, I., Krajbich, I., Way, B. (2019) Acetaminophen influences social and economic trust. Scientific Reports 9:4060
Konovalov, A. & Krajbich, I. (2019). Over a decade of neuroeconomics: What have we learned? Organizational Research Methods, 22(1) 148-173
Gwinn, R., Leber, A., Krajbich, I. (2019) The spillover effects of attentional learning on value-based choice. Cognition, 182:294-306
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
Smith, S., & Krajbich, I. (2018) Attention and choice across domains. Journal of Experimental Psychology: General, 147(12):1810-1826
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
Roberts, I.D., Krajbich, I., Cheavens, J.S., Campo, J.V., Way, B.M. (2018) Acetaminophen reduces distrust in individuals with borderline personality disorder features. Clinical Psychological Science, 6(1): 145-154
Chen, J.W. & Krajbich I. (2017) Computational modeling of epiphany learning. Proceedings of the National Academy of Sciences of the USA, 114(18):4637-4642
Konovalov, A. & Krajbich, I. (2017). Money in the Bank: Distortive Effects of Accumuled 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.
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., Camerer, C., & Rangel, A. (2016). Exploring the scope of neurometrically informed mechanism design. Games and Economic Behavior. 101:49-62
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(96-102)
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 Opinion 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, 6:7455
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
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.
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.
Book Chapters
Fehr, E., Krajbich, I. (2013). Social preferences and the brain. In P. Glimcher and E. Fehr (Eds.) Neuroeconomics: Decision-Making and the Brain.