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Neural Activity Reveals Preferences Without Choices

Citations

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Cited by:

  1. Colby, Bonnie, 2023. "Teaching Water Resource Economics for Policy Analysis," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 5(3), September.
  2. Ryan Webb, 2019. "The (Neural) Dynamics of Stochastic Choice," Management Science, INFORMS, vol. 65(1), pages 230-255, January.
  3. Clithero, John A., 2018. "Response times in economics: Looking through the lens of sequential sampling models," Journal of Economic Psychology, Elsevier, vol. 69(C), pages 61-86.
  4. Eszter Timár & Hubert János Kiss & Dániel Horn, 2026. "Comprehension and deliberation: decomposing response time in economic experiments," KRTK-KTI WORKING PAPERS 2611, Institute of Economics, Centre for Economic and Regional Studies.
  5. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
  6. Webb, Ryan & Mehta, Nitin & Levy, Ifat, 2021. "Assessing consumer demand with noisy neural measurements," Journal of Econometrics, Elsevier, vol. 222(1), pages 89-106.
  7. Daniel Serra, 2019. "La neuroéconomie en question : débats et controverses," CEE-M Working Papers halshs-02160911, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
  8. Daniel Serra, 2021. "Decision-making: from neuroscience to neuroeconomics—an overview," Theory and Decision, Springer, vol. 91(1), pages 1-80, July.
  9. Smith, Trenton G., 2023. "Endocrine state is the physical manifestation of subjective beliefs," Journal of Economic Psychology, Elsevier, vol. 96(C).
  10. Glimcher, Paul W. & Tymula, Agnieszka A., 2023. "Expected subjective value theory (ESVT): A representation of decision under risk and certainty," Journal of Economic Behavior & Organization, Elsevier, vol. 207(C), pages 110-128.
  11. Ryan H Murphy, 2023. "The clear expectation of cultural betterment in the face of rising living standards," Rationality and Society, , vol. 35(3), pages 338-365, August.
  12. Ryan Webb & Paul W. Glimcher & Kenway Louie, 2021. "The Normalization of Consumer Valuations: Context-Dependent Preferences from Neurobiological Constraints," Management Science, INFORMS, vol. 67(1), pages 93-125, January.
  13. Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
  14. Krajbich, Ian & Camerer, Colin & Rangel, Antonio, 2017. "Exploring the scope of neurometrically informed mechanism design," Games and Economic Behavior, Elsevier, vol. 101(C), pages 49-62.
  15. Jonathan A. Parker & Nicholas S. Souleles, 2017. "Reported Effects vs. Revealed-Preference Estimates: Evidence from the propensity to spend tax rebates," NBER Working Papers 23920, National Bureau of Economic Research, Inc.
  16. Dimitris Christelis & Dimitris Georgarakos & Tullio Jappelli & Luigi Pistaferri & Maarten van Rooij, 2019. "Asymmetric Consumption Effects of Transitory Income Shocks," Economic Journal, Royal Economic Society, vol. 129(622), pages 2322-2341.
  17. Fadong Chen & Urs Fischbacher, 2016. "Response time and click position: cheap indicators of preferences," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 2(2), pages 109-126, November.
  18. Guo, Julie & Tymula, Agnieszka, 2021. "Waterfall illusion in risky choice – exposure to outcome-irrelevant gambles affects subsequent valuation of risky gambles," European Economic Review, Elsevier, vol. 139(C).
  19. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.
  20. George Judge, 2015. "Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems," Econometrics, MDPI, vol. 3(1), pages 1-10, February.
  21. Xiaoxue Gao & Eshin Jolly & Hongbo Yu & Huiying Liu & Xiaolin Zhou & Luke J. Chang, 2024. "The psychological, computational, and neural foundations of indebtedness," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
  22. Sophie Bavard & Erik Stuchlý & Arkady Konovalov & Sebastian Gluth, 2024. "Humans can infer social preferences from decision speed alone," PLOS Biology, Public Library of Science, vol. 22(6), pages 1-27, June.
  23. Motoki, Kosuke & Suzuki, Shinsuke & Kawashima, Ryuta & Sugiura, Motoaki, 2020. "A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 99-117.
  24. Lusk, Jayson L. & Crespi, John M. & McFadden, Brandon R. & Cherry, J. Bradley C. & Martin, Laura & Bruce, Amanda, 2016. "Neural antecedents of a random utility model," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PA), pages 93-103.
  25. Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.
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