Element contents
Imperfect Perception and Stochastic Choice in Experiments
Published online by Cambridge University Press: 16 December 2023
Summary
The branch of psychology that studies how physical objects are perceived by subjects is known as psychophysics. A feature of the experimental design is that the experimenter presents objectively measurable objects that are imperfectly perceived by subjects. The responses are stochastic in that a subject might respond differently in otherwise identical situations. These stochastic choices can be compared to the objectively measurable properties. This Element offers a brief introduction to the topic, explains how psychophysics insights are already present in economics, and describes experimental techniques with the goal that they are useful in the design of economics experiments. Noise is a ubiquitous feature of experimental economics and there is a large strand of economics literature that carefully considers the noise. However, the authors view the psychophysics experimental techniques as uniquely suited to helping experimental economists uncover what is hiding in the noise.
Keywords
Information
- Type
- Element
- Information
- Online ISBN: 9781009049207Publisher: Cambridge University PressPrint publication: 01 February 2024
References
Acharya, Sushant, and Wee, Shu Lin (2020): “Rational inattention in hiring decisions,” American Economic Journal: Macroeconomics, 12(1), 1–40.Google Scholar
Agranov, Marina, Caplin, Andrew, and Tergiman, Chloe (2015): “Naive play and the process of choice in guessing games,” Journal of the Economic Science Association, 1(2), 146–157.Google Scholar
Agranov, Marina, and Ortoleva, Pietro (2017): “Stochastic choice and preferences for randomization,” Journal of Political Economy, 125(1), 40–68.Google Scholar
Allred, Sarah R., Crawford, L. Elizabeth, Duffy, Sean, and Smith, John (2016): “Working memory and spatial judgments: Cognitive load increases the central tendency bias,” Psychonomic Bulletin and Review, 23(6), 1825–1831.Google Scholar
Alós-Ferrer, Carlos, Fehr, Ernst, and Netzer, Nick (2021): “Time will tell: Recovering preferences when choices are noisy,” Journal of Political Economy, 129(6), 1828–1877.CrossRefGoogle Scholar
Alós-Ferrer, Carlos, and Garagnani, Michele (2021): “Choice consistency and strength of preference,” Economics Letters, 198, 109672.Google Scholar
Alós-Ferrer, Carlos, and Garagnani, Michele (2022a): “Strength of preference and decisions under risk,” Journal of Risk and Uncertainty, 64, 309–329.Google Scholar
Alós-Ferrer, Carlos, and Garagnani, Michele (2022b): “The gradual nature of economic errors,” Journal of Economic Behavior and Organization, 200, 55–66.Google Scholar
Luis, Amador-Hidalgo, Pablo, Brañas-Garza, Espín, Antonio M., García-Muñoz, Teresa, and Hernández-Román, Ana (2021): “Cognitive abilities and risk-taking: Errors, not preferences,” European Economic Review, 134, 103694.Google Scholar
American Psychological Association (2010): Publication Manual of the American Psychological Association. Washington, DC, 6th ed.Google Scholar
Anderson, Norman H. (1970): “Functional measurement and psychophysical judgment,” Psychological Review, 77(3), 153–170.CrossRefGoogle ScholarPubMed
Antonides, Gerrit (2008): “How is perceived inflation related to actual price changes in the European Union?” Journal of Economic Psychology, 29(4), 417–432.CrossRefGoogle Scholar
Gerrit, Antonides, Verhoef, Peter C., and Van Aalst, Marcel (2002): “Consumer perception and evaluation of waiting time: A field experiment,” Journal of Consumer Psychology, 12(3), 193–202.Google Scholar
Apesteguia, Jose, and Ballester, Miguel A. (2021): “Separating predicted randomness from residual behavior,” Journal of the European Economic Association, 19(2), 1041–1076.Google Scholar
Jose, Apesteguia, Ballester, Miguel A., and Jay, Lu (2017): “Single‐crossing random utility models,” Econometrica, 85(2), 661–674.Google Scholar
Argenziano, Rossella, and Gilboa, Itzhak (2017): “Psychophysical foundations of the Cobb–Douglas utility function,” Economics Letters, 157, 21–23.CrossRefGoogle Scholar
Ariely, Dan, Kamenica, Emir, and Prelec, Dražen (2008): “Man’s search for meaning: The case of Legos,” Journal of Economic Behavior and Organization, 67(3–4), 671–677.Google Scholar
Ballinger, T. Parker, and Wilcox, Nathaniel T. (1997): “Decisions, error and heterogeneity,” Economic Journal, 107(443), 1090–1105.Google Scholar
Bayrak, Oben K., and Hey, John D. (2020): “Understanding preference imprecision,” Journal of Economic Surveys, 34(1), 154–174.Google Scholar
Bazerman, Max H., and Samuelson, William F. (1983): “I won the auction but don’t want the prize,” Journal of Conflict Resolution, 27(4), 618–634.Google Scholar
Becker, Gordon M., DeGroot, Morris H., and Marschak, Jacob (1963): “Stochastic models of choice behavior,” Systems Research and Behavioral Science, 8(1), 41–55.Google Scholar
Bernasconi, Michele, and Seri, Raffaello (2016): “What are we estimating when we fit Stevens’ power law?” Journal of Mathematical Psychology, 75, 137–149.Google Scholar
Bernoulli, Daniel (1738): “Exposition of a new theory on the measurement of risk,” (translated by Sommer, Louise (1954): Econometrica, 22(1), 23–36).Google Scholar
Bhui, Rahul (2019a): “A statistical test for the optimality of deliberative time allocation,” Psychonomic Bulletin and Review, 26(3), 855–867.Google Scholar
Bhui, Rahul (2019b): “Testing optimal timing in value-linked decision making,” Computational Brain and Behavior, 2(2), 85–94.CrossRefGoogle Scholar
Blavatskyy, Pavlo R. (2008): “Stochastic utility theorem,” Journal of Mathematical Economics, 44, 1049–1056.Google Scholar
Blavatskyy, Pavlo R. (2011): “Probabilistic risk aversion with an arbitrary outcome set,” Economics Letters, 112(1), 34–37.CrossRefGoogle Scholar
Boldrin, Michele, Christiano, Lawrence J., and Fisher, Jonas D. M. (2001): “Habit persistence, asset returns, and the business cycle,” American Economic Review, 91(1), 149–166.CrossRefGoogle Scholar
Bordalo, Pedro, Gennaioli, Nicola, and Shleifer, Andrei (2012): “Salience theory of choice under risk,” Quarterly Journal of Economics, 127(3), 1243–1285.CrossRefGoogle Scholar
Brañas-Garza, Pablo, Ductor, Lorenzo, and Kovářík, Jaromír (2022): “The role of unobservable characteristics in friendship network formation,” Working paper, ArXiv:2206.13641.Google Scholar
Brañas-Garza, Pablo, Estepa-Mohedano, Lorenzo, Jorrat, Diego, Orozco, Victor, and Rascon-Ramirez, Ericka (2021): “To pay or not to pay: Measuring risk preferences in lab and field,” Judgment and Decision Making, 16 (5), 1290–1313.CrossRefGoogle Scholar
Brañas-Garza, Pablo, García-Muñoz, Teresa, and Hernán-González, Roberto (2012): “Cognitive effort in the beauty contest game,” Journal of Economic Behavior and Organization, 83(2), 254–260.CrossRefGoogle Scholar
Brañas-Garza, Pablo, Jorrat, Diego, Espín, Antonio M., and Sánchez, Angel (2023): “Paid and hypothetical time preferences are the same: Lab, field and online evidence,” Experimental Economics, 26, 412–434.Google Scholar
Brañas-Garza, Pablo, and Smith, John (2016): “Cognitive abilities and economic behavior,” Journal of Behavioral and Experimental Economics, 64, 1–4.Google Scholar
Brocas, Isabelle, Carrillo, Juan D., and Tarrasó, Jorge (2018): “How long is a minute?” Games and Economic Behavior, 111, 305–322.Google Scholar
Bruni, Luigino, and Sugden, Robert (2007): “The road not taken: How psychology was removed from economics, and how it might be brought back,” Economic Journal, 117(516), 146–173.Google Scholar
Bush, Robert R., and Mosteller, Frederick (1955): Stochastic Models for Learning. John Wiley, New York.Google Scholar
Butler, David J. (2000): “Do non-expected utility choice patterns spring from hazy preferences? An experimental study of choice errors,” Journal of Economic Behavior and Organization, 41(3), 277–297.Google Scholar
Butler, David J., and Loomes, Graham C. (2007): “Imprecision as an account of the preference reversal phenomenon,” American Economic Review, 97(1), 277–297.CrossRefGoogle Scholar
Cabrales, Antonio, Hernández, Penélope, and Sánchez, Angel (2020): “Robots, labor markets, and universal basic income,” Humanities and Social Sciences Communications, 7, 185.CrossRefGoogle Scholar
Camerer, Colin F., and Teck-Hua, Ho (1994): “Violations of the betweenness axiom and nonlinearity in probability,” Journal of Risk and Uncertainty, 8(2), 167–196.Google Scholar
Capen, Edward C., Clapp, Robert V., and Campbell, William M. (1971): “Competitive bidding in high-risk situations,” Journal of Petroleum Technology, 23(6), 641–653.CrossRefGoogle Scholar
Caplin, Andrew (2012): “Choice sets as percepts,” in Neuroscience of Preference and Choice: Cognitive and Neural Mechanisms, Dolan, Raymond, and Sharot, Tali (Eds.), Academic Press, Waltham, 295–304.CrossRefGoogle Scholar
Caplin, Andrew, Csaba, Dániel, Leahy, John, and Nov, Oded (2020): “Rational inattention, competitive supply, and psychometrics,” Quarterly Journal of Economics, 135(3), 1681–1724.Google Scholar
Caplin, Andrew, and Dean, Mark (2015): “Revealed preference, rational inattention, and costly information acquisition,” American Economic Review, 105(7), 2183–2203.Google Scholar
Carpenter, Jeffrey, and Huet-Vaughn, Emiliano (2019): “Real-effort tasks,” in Handbook of Research Methods and Applications in Experimental Economics, Schram, Arthur, and Ule, Aljaž (Eds.), Edward Elgar, Cheltenham, 368–383.Google Scholar
Case, Karl E., Quigley, John M., and Shiller, Robert J. (2005): “Comparing wealth effects: The stock market versus the housing market,” Advances in Macroeconomics, 5(1), Article 1.Google Scholar
Cerreia-Vioglio, Simone, Dillenberger, David, Ortoleva, Pietro, and Riella, Gil (2019): “Deliberately stochastic,” American Economic Review, 109(7), 2425–2445.Google Scholar
Churcher, B. G. (1935): “A loudness scale for industrial noise measurements,” Journal of the Acoustical Society of America, 6(4), 216–225.Google Scholar
Cooper, David J., and Rege, Mari (2011): “Misery loves company: Social regret and social interaction effects in choices under risk and uncertainty,” Games and Economic Behavior, 73(1), 91–110.CrossRefGoogle Scholar
Corgnet, Brice, Hernán-González, Roberto, and Kujal, Praveen (2020): “On booms that never bust: Ambiguity in experimental asset markets with bubbles,” Journal of Economic Dynamics and Control, 110, 103754.Google Scholar
Amicis, De, Luisa, Binenti, Silvia, Maciel Cardoso et al. (2020): “Understanding drivers when investing for impact: An experimental study,” Palgrave Communications, 6, 86.Google Scholar
Dean, Mark, and Neligh, Nathaniel (2023): “Experimental tests of rational inattention,” Journal of Political Economy, forthcoming.Google Scholar
Debreu, Gerard (1960): “Individual choice behavior: A theoretical analysis,” American Economic Review, 50(1), 186–188.Google Scholar
Dessein, Wouter, Galeotti, Andrea, and Santos, Tano (2016): “Rational inattention and organizational focus,” American Economic Review, 106(6), 1522–1536.CrossRefGoogle Scholar
Dewan, Ambuj, and Neligh, Nathaniel (2020): “Estimating information cost functions in models of rational inattention,” Journal of Economic Theory, 187, 105011.Google Scholar
Duffy, Sean, Gussman, Steven, and Smith, John (2021): “Visual judgments of length in the economics laboratory: Are there brains in stochastic choice?” Journal of Behavioral and Experimental Economics, 93, 101708.Google Scholar
Duffy, Sean, Hertel, Johanna, Igan, Deniz, Pinheiro, Marcelo, and Smith, John (2022): “On Bayesian integration in sensorimotor learning: Another look at Kording and Wolpert (2004),” Cortex, 153, 87–96.CrossRefGoogle Scholar
Duffy, Sean, and Smith, John (2020): “On the category adjustment model: Another look at Huttenlocher, Hedges, and Vevea (2000),” Mind and Society, 19(1), 163–193.Google Scholar
Duffy, Sean, and Smith, John (2023): “An economist and a psychologist form a line: What can imperfect perception of length tell us about stochastic choice?” Working paper, Rutgers University-Camden.Google Scholar
Dutilh, Gilles, and Rieskamp, Jörg (2016): “Comparing perceptual and preferential decision making,” Psychonomic Bulletin and Review, 23(3), 723–737.Google Scholar
Eliaz, Kfir, and Spiegler, Ran (2011): “Consideration sets and competitive marketing,” Review of Economic Studies, 78(1), 235–262.Google Scholar
Engen, Trygg, and Tulunay, Ülker (1957): “Some sources of error in half-heaviness judgments,” Journal of Experimental Psychology, 54(3), 208–212.Google Scholar
Evans, Nathan J., and Wagenmakers, Eric-Jan (2020): “Evidence accumulation models: Current limitations and future directions,” Quantitative Methods for Psychology, 16(2), 73–90.CrossRefGoogle Scholar
Falk, Armin, and Zimmermann, Florian (2013): “A taste for consistency and survey response behavior,” CESifo Economic Studies, 59(1), 181–193.Google Scholar
Falmagne, Jean-Claude (2002): Elements of Psychophysical Theory. Oxford University Press, New York.Google Scholar
Fechner, Gustav Theodor (1860): Elemente der Psychophysik. (Elements of psychophysics, translated 1966. Holt, Rinehart, and Winston, New York.)Google Scholar
Frydman, Cary, and Jin, Lawrence J. (2022): “Efficient coding and risky choice,” Quarterly Journal of Economics, 137(1), 161–213.Google Scholar
Fudenberg, Drew, Iijima, Ryota, and Strzalecki, Tomasz (2015): “Stochastic choice and revealed perturbed utility,” Econometrica, 83(6), 2371–2409.Google Scholar
Fudenberg, Drew, Newey, Whitney, Strack, Philipp, and Strzalecki, Tomasz (2020): “Testing the drift-diffusion model,” Proceedings of the National Academy of Sciences, 117(52), 33141–33148.Google Scholar
Fudenberg, Drew, Strack, Philipp, and Strzalecki, Tomasz (2018): “Speed, accuracy, and the optimal timing of choices,” American Economic Review, 108(12), 3651–3684.Google Scholar
Gabaix, Xavier, Laibson, David, Moloche, Guillermo, and Weinberg, Stephen (2006): “Costly information acquisition: Experimental analysis of a boundedly rational model,” American Economic Review, 96(4), 1043–1068.CrossRefGoogle Scholar
Gescheider, George A. (1997): Psychophysics: The Fundamentals. Routledge Press, New York.Google Scholar
Gescheider, George A., Wright, John H., and Polak, John W. (1971): “Detection of vibrotactile signals differing in probability of occurrence,” Journal of Psychology, 78(2), 253–260.Google Scholar
Gill, David, and Prowse, Victoria (2012): “A structural analysis of disappointment aversion in a real effort competition,” American Economic Review, 102(1), 469–503.Google Scholar
Gneezy, Uri, Niederle, Muriel, and Rustichini, Aldo (2003): “Performance in competitive environments: Gender differences,” Quarterly Journal of Economics, 118(3), 1049–1074.CrossRefGoogle Scholar
Goeree, Jacob K., Holt, Charles A., and Palfrey, Thomas R. (2003): “Risk averse behavior in generalized matching pennies games,” Games and Economic Behavior, 45(1), 97–113.Google Scholar
Goeree, Jacob K., Holt, Charles A., and Palfrey, Thomas R. (2010): “Quantal response equilibria,” in Behavioural and Experimental Economics, Durlauf, Steven N., Blume, Lawrence E. (Eds.), New Palgrave Economics Collection, Palgrave Macmillan, New York, 234–242.Google Scholar
Gonzalez, Richard, and George, Wu (1999): “On the shape of the probability weighting function,” Cognitive Psychology, 38(1), 129–166.Google Scholar
Goryunov, Maxim, and Rigos, Alexandros (2022): “Discontinuous and continuous stochastic choice and coordination in the lab,” Journal of Economic Theory, 206, 105557.Google Scholar
Hausman, Jerry A., and Wise, David A. (1978): “A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preferences,” Econometrica, 46(2), 403–426.Google Scholar
Hautus, Michael J., Macmillan, Neil A., and Creelman, C. Douglas (2022): Detection Theory: A User’s Guide. Routledge, New York.Google Scholar
Heinemann, Frank, Nagel, Rosemarie, and Ockenfels, Peter (2009): “Measuring strategic uncertainty in coordination games,” Review of Economic Studies, 76(1), 181–221.Google Scholar
Heng, Joseph A., Woodford, Michael, and Polania, Rafael (2020): “Efficient sampling and noisy decisions,” eLife, 9, e54962.Google Scholar
Henmon, V. A. C. (1911): “The relation of the time of a judgment to its accuracy,” Psychological Review, 18(3), 186–201.CrossRefGoogle Scholar
Hey, John D. (1995): “Experimental investigations of errors in decision making under risk,” European Economic Review, 39(3–4), 633–640.Google Scholar
Hey, John D. (2001): “Does repetition improve consistency?” Experimental Economics, 4(1), 5–54.Google Scholar
Hey, John D. (2005): “Why we should not be silent about noise,” Experimental Economics, 8(4), 325–345.Google Scholar
Hey, John D., Lotito, Gianna, and Maffioletti, Anna (2010): “The descriptive and predictive adequacy of theories of decision making under uncertainty/ambiguity,” Journal of Risk and Uncertainty, 41(2), 81–111.Google Scholar
Hey, John D., and Orme, Chris (1994): “Investigating generalizations of expected utility theory using experimental data,” Econometrica, 62(6), 1291–1326.Google Scholar
Hildenbrand, Werner (1971): “Random preferences and equilibrium analysis,” Journal of Economic Theory, 3(4), 414–429.Google Scholar
Hollingworth, Harry Levi (1910): “The central tendency of judgment,” Journal of Philosophy, Psychology and Scientific Methods, 7(17), 461–469.Google Scholar
Horan, Sean, Manzini, Paola, and Mariotti, Marco (2022): “When is coarseness not a curse? Comparative statics of the coarse random utility model,” Journal of Economic Theory, 202, 105445.Google Scholar
Joel, Huber, Payne, John W., and Puto, Christopher (1982): “Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis,” Journal of Consumer Research, 9(1), 90–98.Google Scholar
Janellen, Huttenlocher, Hedges, Larry V., and Vevea, Jack L. (2000): “Why do categories affect stimulus judgment?” Journal of Experimental Psychology: General, 129(2), 220–241.Google Scholar
Kahneman, Daniel, and Tversky, Amos (1979): “Prospect theory: An analysis of decision under risk,” Econometrica, 47(2), 263–292.Google Scholar
Kellogg, W. N. (1931): “The time of judgment in psychometric measures,” American Journal of Psychology, 43(1), 65–86.Google Scholar
Khaw, Mel Win, Ziang, Li, and Michael, Woodford (2021): “Cognitive imprecision and small-stakes risk aversion,” Review of Economic Studies, 88(4), 1979–2013.Google Scholar
Khaw, Mel Win, Stevens, Luminita, and Woodford, Michael (2017): “Discrete adjustment to a changing environment: Experimental evidence,” Journal of Monetary Economics, 91, 88–103.Google Scholar
Kingdom, Frederick A., and Prins, Nicolaas (2016): Psychophysics: A Practical Introduction. Elsevier Science and Technology, San Diego .Google Scholar
Körding, Konrad P., and Wolpert, Daniel M. (2004): “Bayesian integration in sensorimotor learning,” Nature, 427(6971), 244–247.Google Scholar
Krueger, Lester E. (1989): “Reconciling Fechner and Stevens: Toward a unified psychophysical law,” Behavioral and Brain Sciences, 12(2), 251–267.Google Scholar
Lévy-Garboua, Louis, Maafi, Hela, Masclet, David, and Terracol, Antoine (2012): “Risk aversion and framing effects,” Experimental Economics, 15(1), 128–144.Google Scholar
Liang, Annie (2019): “Inference of preference heterogeneity from choice data,” Journal of Economic Theory, 179, 275–311.Google Scholar
Loomes, Graham (2005): “Modelling the stochastic component of behaviour in experiments: Some issues for the interpretation of data,” Experimental Economics, 8(4), 301–323.Google Scholar
Loomes, Graham, Starmer, Chris, and Sugden, Robert (1989): “Preference reversal: Information-processing effect or rational non-transitive choice?” Economic Journal, 99(395), 140–151.Google Scholar
Loomis, John, Peterson, George, Champ, Patricia, Brown, Thomas, and Lucero, Beatrice (1998): “Paired comparison estimates of willingness to accept versus contingent valuation estimates of willingness to pay,” Journal of Economic Behavior and Organization, 35(4), 501–515.Google Scholar
Jay, Lu (2016): “Random choice and private information,” Econometrica, 84(6), 1983–2027.Google Scholar
Luce, R. Duncan (1959): Individual Choice Behavior: A Theoretical Analysis. Wiley, New York.Google Scholar
Luce, R. Duncan, and Green, David M. (1972): “A neural timing theory for response times and the psychophysics of intensity,” Psychological Review, 79(1), 14–57.Google Scholar
Machina, Mark J. (1985): “Stochastic choice functions generated from deterministic preferences over lotteries,” Economic Journal, 95(379), 575–594.Google Scholar
Maćkowiak, Bartosz, Matějka, Filip, and Wiederholt, Mirko (2023): “Rational inattention: A review,” Journal of Economic Literature, 61(1), 226–273.Google Scholar
Maćkowiak, Bartosz, and Wiederholt, Mirko (2009): “Optimal sticky prices under rational inattention,” American Economic Review, 99(3), 769–803.Google Scholar
Maćkowiak, Bartosz, and Wiederholt, Mirko (2015): “Business cycle dynamics under rational inattention,” Review of Economic Studies, 82(4), 1502–1532.Google Scholar
Manzini, Paola, and Mariotti, Marco (2014): “Stochastic choice and consideration sets,” Econometrica, 82(3), 1153–1176.Google Scholar
Andreu, Mas-Colell, Whinston, Michael D., and Green, Jerry R. (1995): Microeconomic Theory. Oxford University Press, Oxford.Google Scholar
Yusufcan, Masatlioglu, Daisuke, Nakajima, and Ozbay, Erkut Y. (2012): “Revealed attention,” American Economic Review, 102(5), 2183–2205.Google Scholar
Matějka, Filip, and McKay, Alisdair (2015): “Rational inattention to discrete choices: A new foundation for the multinomial logit model,” American Economic Review, 105(1), 272–298.CrossRefGoogle Scholar
McFadden, Daniel (1974): “Conditional logit analysis of qualitative choice behavior,” in Frontiers in Econometrics, Zarembka, Paul (Ed.), Academic Press, New York, 105–142.Google Scholar
McFadden, Daniel (2001): “Economic choices,” American Economic Review, 91(3), 351–378.Google Scholar
McKelvey, Richard D., and Palfrey, Thomas R. (1995): “Quantal response equilibria for normal form games,” Games and Economic Behavior, 10(1), 6–38.Google Scholar
Mead, Walter J., Moseidjord, Asbjorn, and Sorensen, Philip E. (1983): “The rate of return earned by lessees under cash bonus bidding for OCS oil and gas leases,” Energy Journal, 4(4), 37–52.Google Scholar
Mondria, Jordi, and Thomas, Wu (2010): “The puzzling evolution of the home bias, information processing and financial openness,” Journal of Economic Dynamics and Control, 34(5), 875–896.Google Scholar
Mosteller, Frederick, and Nogee, Philip (1951): “An experimental measurement of utility,” Journal of Political Economy, 59(5), 371–404.CrossRefGoogle Scholar
Munsell, Albert E. O., Sloan, Louise L., and Godlove, Isaac H. (1933): “Neutral value scales. I. Munsell neutral value scale,” Journal of the Optical Society of America, 23(11), 394–411.Google Scholar
Murray, David J. (1993): “A perspective for viewing the history of psychophysics,” Behavioral and Brain Sciences, 16(1), 115–137.Google Scholar
Naeher, Dominik (2022): “Technology adoption under costly information processing,” International Economic Review, 63(2), 699–753.Google Scholar
Natenzon, Paulo (2019): “Random choice and learning,” Journal of Political Economy, 127(1), 419–457.Google Scholar
Navarro-Martinez, Daniel, Loomes, Graham, Isoni, Andrea, Butler, David, and Alaoui, Larbi (2018): “Boundedly rational expected utility theory,” Journal of Risk and Uncertainty, 57(3), 199–223.Google Scholar
Ochs, Jack (1995): “Games with unique, mixed strategy equilibria: An experimental study,” Games and Economic Behavior, 10(1), 202–217.Google Scholar
Okunade, Albert A. (1992): “Functional forms and habit effects in the US demand for coffee,” Applied Economics, 24(11), 1203–1212.Google Scholar
Oud, Bastiaan, Krajbich, Ian, Miller, Kevin et al. (2016): “Irrational time allocation in decision-making,” Proceedings of the Royal Society B: Biological Sciences, 283(1822), 20151439.Google Scholar
Payne, John W., Bettman, James R., and Johnson, Eric J. (1993): The Adaptive Decision Maker, Cambridge University Press, Cambridge.Google Scholar
Payzan-LeNestour, Elise, and Woodford, Michael (2022): “Outlier blindness: A neurobiological foundation for neglect of financial risk,” Journal of Financial Economics, 143(3), 1316–1343.Google Scholar
Pirrone, Angelo, Wen, Wen, and Sheng, Li (2018): “Single-trial dynamics explain magnitude sensitive decision making,” BMC Neuroscience, 19, 54.Google Scholar
Plateau, Joseph Antoine Ferdinand (1872): “Sur la mesure des sensations physiques, et sur la loi qui lie l’intensité de la cause excitante,” Bulletins de l’Academie Royale des Sciences, des Lettres, et des Beaux-Arts de Belgique, 33, 376–388.Google Scholar
Pleskac, Timothy J., Shuli, Yu, Hopwood, Christopher, and Liu, Taosheng (2019): “Mechanisms of deliberation during preferential choice: Perspectives from computational modeling and individual differences,” Decision, 6(1), 77–107.CrossRefGoogle ScholarPubMed
Pratt, John W., Wise, David A., and Zeckhauser, Richard (1979): “Price differences in almost competitive markets,” Quarterly Journal of Economics, 93(2), 189–211.Google Scholar
Prelec, Drazen (1998): “The probability weighting function,” Econometrica, 66(3), 497–527.Google Scholar
Rabin, Matthew (2000): “Risk aversion and expected-utility theory: A calibration theorem,” Econometrica, 68(5), 1281–1292.Google Scholar
Ratcliff, Roger (1978): “A theory of memory retrieval,” Psychological Review, 85(2), 59–108.CrossRefGoogle Scholar
Ratcliff, Roger (2014): “Measuring psychometric functions with the diffusion model,” Journal of Experimental Psychology: Human Perception and Performance, 40(2), 870–888.Google Scholar
Ratcliff, Roger, and McKoon, Gail (2008): “The diffusion decision model: Theory and data for two-choice decision tasks,” Neural Computation, 20(4), 873–922.Google Scholar
Ratcliff, Roger, and Rouder, Jeffrey N. (1998): “Modeling response times for two-choice decisions,” Psychological Science, 9(5), 347–356.Google Scholar
Reutskaja, Elena, Nagel, Rosemarie, Camerer, Colin F., and Rangel, Antonio (2011): “Search dynamics in consumer choice under time pressure: An eye-tracking study,” American Economic Review, 101(2), 900–926.Google Scholar
Rivera-Garrido, Noelia, Ramos-Sosa, M. P., Accerenzi, Michela, and Brañas-Garza, Pablo (2022): “Continuous and binary sets of responses are not the same: Evidence from the field,” Scientific Reports, 12, 14376.Google Scholar
Roberts, John H., and Lattin, James M. (1991): “Development and testing of a model of consideration set composition,” Journal of Marketing Research, 28(4), 429–440.CrossRefGoogle Scholar
Rodríguez, Jorge, Urzúa, Sergio, and Reyes, Loreto (2016): “Heterogeneous economic returns to post-secondary degrees: Evidence from Chile,” Journal of Human Resources, 51(2), 416–460.Google Scholar
Rozen, Kareen (2010): “Foundations of intrinsic habit formation,” Econometrica, 78(4), 1341–1373.Google Scholar
Rustichini, Aldo, and Siconolfi, Paolo (2014): “Dynamic theory of preferences: Habit formation and taste for variety,” Journal of Mathematical Economics, 55, 55–68.Google Scholar
Salomon, Joshua A., and Murray, Christopher J. L. (2004): “A multi‐method approach to measuring health‐state valuations,” Health Economics, 13(3), 281–290.Google Scholar
Savage, Leonard J. (1954): The Foundations of Statistics. Wiley, New York. Reprinted in 1972 by Dover, New York.Google Scholar
Schram, Arthur, Brandts, Jordi, and Gërxhani, Klarita (2019): “Social-status ranking: A hidden channel to gender inequality under competition,” Experimental Economics, 22(2), 396–418.Google Scholar
See, Judi E., Warm, Joel S., Dember, William N., and Howe, Steven R. (1997): “Vigilance and signal detection theory: An empirical evaluation of five measures of response bias,” Human Factors, 39(1), 14–29.Google Scholar
Serences, John T., and Saproo, Sameer (2010): “Population response profiles in early visual cortex are biased in favor of more valuable stimuli,” Journal of Neurophysiology, 104(1), 76–87.Google Scholar
Shepard, Roger N. (1981): “Psychological relations and psychophysical scales: On the status of ‘direct’ psychophysical measurement,” Journal of Mathematical Psychology, 24(1), 21–57.Google Scholar
Shevlin, Blair R. K., Smith, Stephanie M., Hausfeld, Jan, and Krajbich, Ian (2022): “High-value decisions are fast and accurate, inconsistent with diminishing value sensitivity,” Proceedings of the National Academy of Sciences, 119(6), e2101508119.Google Scholar
Shocker, Allan D., Ben-Akiva, Moshe, Boccara, Bruno, and Nedungadi, Prakash (1991): “Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions,” Marketing Letters, 2(3), 181–197.Google Scholar
Sims, Christopher A. (2003): “Implications of rational inattention,” Journal of Monetary Economics, 50(3), 665–690.Google Scholar
Sinn, Hans-Werner (1985): “Psychophysical laws in risk theory,” Journal of Economic Psychology, 6(2), 185–206.Google Scholar
Smith, Vernon L. (1976): “Experimental economics: Induced value theory,” American Economic Review, 66(2), 274–279.Google Scholar
Solomon, Joshua A. (2009): “The history of dipper functions,” Attention, Perception, & Psychophysics, 71(3), 435–443.Google Scholar
Stevens, Luminita (2020): “Coarse pricing policies,” Review of Economic Studies, 87(1), 420–453.Google Scholar
Stevens, S. S. (1936): “A scale for the measurement of a psychological magnitude: Loudness,” Psychological Review, 43(5), 405–416.Google Scholar
Stevens, S. S. (1956): “The direct estimation of sensory magnitudes: Loudness,” American Journal of Psychology, 69(1), 1–25.Google Scholar
Stevens, S. S. (1957): “On the psychophysical law,” Psychological Review, 64(3), 153–181.Google Scholar
Stevens, S. S. (1960): “The psychophysics of sensory function,” American Scientist, 48(2), 226–253.Google Scholar
Stevens, S. S. (1961): “To honor Fechner and repeal his law: A power function, not a log function, describes the operating characteristic of a sensory system,” Science, 133(3446), 80–86.Google Scholar
Stevens, S. S. (1971): “Issues in psychophysical measurement,” Psychological Review, 78(5), 426–450.Google Scholar
Stevens, S. S., and Guirao, Miguelina (1962): “Loudness, reciprocality, and partition scales,” Journal of the Acoustical Society of America, 34.9B, 1466–1471.Google Scholar
Stigler, George J. (1950a): “The development of utility theory. I,” Journal of Political Economy, 58(4), 307–327.Google Scholar
Stigler, George J. (1950b): “The development of utility theory. II,” Journal of Political Economy, 58(5), 373–396.Google Scholar
Summerfield, Christopher, and Tsetsos, Konstantinos (2012): “Building bridges between perceptual and economic decision-making: Neural and computational mechanisms,” Frontiers in Neuroscience, 6, 70.Google Scholar
Swets, John A., Tanner, Wilson P. Jr, and Birdsall, Theodore G. (1961): “Decision processes in perception,” Psychological Review, 68(5), 301–340.Google Scholar
Takahashi, Taiki (2011): “Psychophysics of the probability weighting function,” Physica A: Statistical Mechanics and its Applications, 390(5), 902–905.Google Scholar
Tanner, W. P., Swets, J. A., and Green, D. M. (1956): “Some general properties of the hearing mechanism,” University of Michigan, Electronic Defense Group, Technical Report No. 30.Google Scholar
Taubinsky, Dmitry, and Rees-Jones, Alex (2018): “Attention variation and welfare: Theory and evidence from a tax salience experiment,” Review of Economic Studies, 85(4), 2462–2496.Google Scholar
Tavares, Gabriela, Perona, Pietro, and Rangel, Antonio (2017): “The attentional drift diffusion model of simple perceptual decision-making,” Frontiers in Neuroscience, 11, 468.Google Scholar
Thaler, Richard (1980): “Toward a positive theory of consumer choice,” Journal of Economic Behavior and Organization, 1(1), 39–60.Google Scholar
Thaler, Richard (1988): “Anomalies: The winner’s curse,” Journal of Economic Perspectives, 2(1), 191–202.Google Scholar
Thurstone, L. L. (1927a): “A law of comparative judgment,” Psychological Review, 34(4), 273–286.Google Scholar
Thurstone, L. L. (1927b): “Psychophysical analysis,” American Journal of Psychology, 38(3), 368–389.Google Scholar
Train, Kenneth E. (2009): Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge.Google Scholar
Tserenjigmid, Gerelt (2020): “On the characterization of linear habit formation,” Economic Theory, 70(1), 49–93.Google Scholar
Tsetsos, Konstantinos, Moran, Rani, Moreland, James et al. (2016): “Economic irrationality is optimal during noisy decision making,” Proceedings of the National Academy of Sciences, 113(11), 3102–3107.Google Scholar
Tversky, Amos, and Kahneman, Daniel (1981): “The framing of decisions and the psychology of choice,” Science, 211(4481), 453–458.Google Scholar
Tversky, Amos, and Kahneman, Daniel (1992): “Advances in prospect theory: Cumulative representation of uncertainty,” Journal of Risk and Uncertainty, 5(4), 297–323.Google Scholar
Tversky, Amos, and Russo, J. Edward (1969): “Substitutability and similarity in binary choices,” Journal of Mathematical Psychology, 6(1), 1–12.Google Scholar
Volkmann, John (1934): “The relation of the time of judgment to the certainty of judgment,” Psychological Bulletin, 31(9), 672–673.Google Scholar
Wakker, Peter P. (2010): Prospect Theory: For Risk and Ambiguity. Cambridge University Press, Cambridge.Google Scholar
Weber, Elke (2004): “Perception matters: Psychophysics for economists,” in The Psychology of Economic Decisions: Reasons and Choices (Vol. 2), Brocas, Isabelle, and Carrillo, Juan (Eds.), Oxford University Press, New York, 163–176.Google Scholar
Weber, Ernst (1834): De Tactu. (The Sense of Touch, translated 1978. Academic Press, New York.)Google Scholar
Weber, Roberto A. (2003): “‘Learning’ with no feedback in a competitive guessing game,” Games and Economic Behavior, 44(1), 134–144.Google Scholar
Weil, R. S., Furl, N., Ruff, C. C. et al. (2010): “Rewarding feedback after correct visual discriminations has both general and specific influences on visual cortex,” Journal of Neurophysiology, 104(3), 1746–1757.Google Scholar
Wichmann, Felix A., and Jäkel, Frank (2018): “Methods in psychophysics,” Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5(7), 265–306.Google Scholar
Willemsen, Martijn C., and Johnson Eric, J. (2019): “(Re) visiting the decision factory: Observing cognition with MouselabWEB,” in A Handbook of Process Tracing Methods, Michael, Schulte-Mecklenbeck, Anton, Kuehberger, and Johnson, Joseph G. (Eds.), Routledge, New York, 76–95.Google Scholar
Woodford, Michael (2020): “Modeling imprecision in perception, valuation, and choice,” Annual Review of Economics, 12, 579–601.Google Scholar
Wooldridge, Jeffrey M. (2019): “Correlated random effects models with unbalanced panels,” Journal of Econometrics, 211(1), 137–150.Google Scholar
Yellott, John I. (1977): “The relationship between Luce’s choice axiom, Thurstone’s theory of comparative judgment, and the double exponential distribution,” Journal of Mathematical Psychology, 15(2), 109–144.Google Scholar
Yuksel, Sevgi (2022): “Specialized learning and political polarization,” International Economic Review, 63(1), 457–474.Google Scholar
Zauberman, Gal, Kim, B. Kyu, Malkoc, Selin A., and Bettman, James R. (2009): “Discounting time and time discounting: Subjective time perception and intertemporal preferences,” Journal of Marketing Research, 46(4), 543–556.Google Scholar
Zeigenfuse, Matthew D., Pleskac, Timothy J., and Liu, Taosheng (2014): “Rapid decisions from experience,” Cognition, 131(2), 181–194.Google Scholar
Accessibility standard: Unknown
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
Accessibility compliance for the HTML of this element is currently unknown
and may be updated in the future.
- 3
- Cited by