references.bib

@article{barronSmallFeedbackbasedDecisions2003,
  title = {Small Feedback-Based Decisions and Their Limited Correspondence to Description-Based Decisions},
  author = {Barron, Greg and Erev, Ido},
  year = {2003},
  month = jul,
  journal = {Journal of Behavioral Decision Making},
  volume = {16},
  number = {3},
  pages = {215--233},
  issn = {0894-3257, 1099-0771},
  doi = {10.1002/bdm.443},
  langid = {english}
}

@article{bhatiaSequentialSamplingParadoxes2014,
  title = {Sequential Sampling and Paradoxes of Risky Choice},
  author = {Bhatia, Sudeep},
  year = {2014},
  month = oct,
  journal = {Psychonomic Bulletin \& Review},
  volume = {21},
  number = {5},
  pages = {1095--1111},
  issn = {1069-9384, 1531-5320},
  doi = {10.3758/s13423-014-0650-1},
  langid = {english}
}

@article{bogaczPhysicsOptimalDecision2006,
  title = {The Physics of Optimal Decision Making: {{A}} Formal Analysis of Models of Performance in Two-Alternative Forced-Choice Tasks.},
  shorttitle = {The Physics of Optimal Decision Making},
  author = {Bogacz, Rafal and Brown, Eric and Moehlis, Jeff and Holmes, Philip and Cohen, Jonathan D.},
  year = {2006},
  journal = {Psychological Review},
  volume = {113},
  number = {4},
  pages = {700--765},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/0033-295X.113.4.700},
  langid = {english}
}

@article{busemeyerDecisionFieldTheory1993,
  title = {Decision Field Theory: {{A}} Dynamic-Cognitive Approach to Decision Making in an Uncertain Environment.},
  shorttitle = {Decision Field Theory},
  author = {Busemeyer, Jerome R. and Townsend, James T.},
  year = {1993},
  journal = {Psychological Review},
  volume = {100},
  number = {3},
  pages = {432--459},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/0033-295X.100.3.432},
  langid = {english}
}

@article{erevAnomaliesForecastsDescriptive2017,
  title = {From Anomalies to Forecasts: {{Toward}} a Descriptive Model of Decisions under Risk, under Ambiguity, and from Experience.},
  shorttitle = {From Anomalies to Forecasts},
  author = {Erev, Ido and Ert, Eyal and Plonsky, Ori and Cohen, Doron and Cohen, Oded},
  year = {2017},
  month = jul,
  journal = {Psychological Review},
  volume = {124},
  number = {4},
  pages = {369--409},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/rev0000062},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\9M7IVMF6\\Erev et al. - 2017 - From anomalies to forecasts Toward a descriptive .pdf}
}

@article{erevChoicePredictionCompetition2010,
  title = {A Choice Prediction Competition: {{Choices}} from Experience and from Description},
  shorttitle = {A Choice Prediction Competition},
  author = {Erev, Ido and Ert, Eyal and Roth, Alvin E. and Haruvy, Ernan and Herzog, Stefan M. and Hau, Robin and Hertwig, Ralph and Stewart, Terrence and West, Robert and Lebiere, Christian},
  year = {2010},
  month = jan,
  journal = {Journal of Behavioral Decision Making},
  volume = {23},
  number = {1},
  pages = {15--47},
  issn = {08943257, 10990771},
  doi = {10.1002/bdm.683},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\XEDLN3VB\\Erev et al. - 2010 - A choice prediction competition Choices from expe.pdf}
}

@article{foxDecisionsExperienceSampling2006,
  title = {'{{Decisions}} from Experience' = Sampling Error + Prospect Theory: {{Reconsidering Hertwig}}, {{Barron}}, {{Weber}} \& {{Erev}} (2004)},
  author = {Fox, Craig R. and Hadar, Liat},
  year = {2006},
  journal = {Judgement and Decision Making},
  volume = {1},
  number = {2},
  pages = {159--161}
}

@article{gelmanInferenceIterativeSimulation1992,
  title = {Inference from Iterative Simulation Using Multiple Sequences},
  author = {Gelman, Andrew and Rubin, Donald B.},
  year = {1992},
  month = nov,
  journal = {Statistical Science},
  volume = {7},
  number = {4},
  issn = {0883-4237},
  doi = {10.1214/ss/1177011136},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\SMZ2EAKC\\Gelman and Rubin - 1992 - Inference from Iterative Simulation Using Multiple.pdf}
}

@book{georgiiStochastikEinfuhrungWahrscheinlichkeitstheorie2015,
  title = {Stochastik: {{Einf\"uhrung}} in Die {{Wahrscheinlichkeitstheorie}} Und {{Statistik}} [{{Stochastics}}: {{Introduction}} to Probability and Statistics]},
  shorttitle = {Stochastik},
  author = {Georgii, Hans-Otto},
  year = {2015},
  series = {De {{Gruyter Studium}}},
  edition = {5. Auflage},
  publisher = {{De Gruyter}},
  address = {{Berlin ; Boston}},
  isbn = {978-3-11-035969-5},
  lccn = {QA273 .G456 2016},
  keywords = {Mathematical statistics,Probabilities,Stochastic processes,Textbooks}
}

@article{gigerenzerHowExplainBehavior2020,
  title = {How to Explain Behavior?},
  author = {Gigerenzer, Gerd},
  year = {2020},
  month = oct,
  journal = {Topics in Cognitive Science},
  volume = {12},
  number = {4},
  pages = {1363--1381},
  issn = {1756-8757, 1756-8765},
  doi = {10.1111/tops.12480},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\TLTCUH5P\\Gigerenzer - 2020 - How to Explain Behavior.pdf}
}

@article{goldsteinExpressionTheoryPreference1987,
  title = {Expression Theory and the Preference Reversal Phenomena},
  author = {Goldstein, William M. and Einhorn, Hillel J.},
  year = {1987},
  journal = {Psychological Review},
  volume = {94},
  number = {2},
  pages = {236--254},
  issn = {0033-295X},
  doi = {10.1037/0033-295X.94.2.236},
  langid = {english}
}

@article{griffithsProbabilisticModelsCognition2010,
  title = {Probabilistic Models of Cognition: Exploring Representations and Inductive Biases},
  shorttitle = {Probabilistic Models of Cognition},
  author = {Griffiths, Thomas L. and Chater, Nick and Kemp, Charles and Perfors, Amy and Tenenbaum, Joshua B.},
  year = {2010},
  month = aug,
  journal = {Trends in Cognitive Sciences},
  volume = {14},
  number = {8},
  pages = {357--364},
  publisher = {{Elsevier}},
  issn = {1364-6613, 1879-307X},
  doi = {10.1016/j.tics.2010.05.004},
  langid = {english},
  pmid = {20576465},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\KCSGL9NU\\Griffiths et al. - 2010 - Probabilistic models of cognition exploring repre.pdf;C\:\\Users\\Linus Hof\\Zotero\\storage\\C6YN7QTX\\S1364-6613(10)00112-9.html}
}

@article{guestHowComputationalModeling2021,
  title = {How Computational Modeling Can Force Theory Building in Psychological Science},
  author = {Guest, Olivia and Martin, Andrea E.},
  year = {2021},
  month = jul,
  journal = {Perspectives on Psychological Science},
  volume = {16},
  number = {4},
  pages = {789--802},
  issn = {1745-6916, 1745-6924},
  doi = {10.1177/1745691620970585},
  abstract = {Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined\textemdash what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\SWLBJLQW\\Guest and Martin - 2021 - How Computational Modeling Can Force Theory Buildi.pdf}
}

@article{hauDescriptionexperienceGapRisky2008,
  title = {The Description-Experience Gap in Risky Choice: The Role of Sample Size and Experienced Probabilities},
  shorttitle = {The Description-Experience Gap in Risky Choice},
  author = {Hau, Robin and Pleskac, Timothy J. and Kiefer, J{\"u}rgen and Hertwig, Ralph},
  year = {2008},
  month = dec,
  journal = {Journal of Behavioral Decision Making},
  volume = {21},
  number = {5},
  pages = {493--518},
  issn = {08943257, 10990771},
  doi = {10.1002/bdm.598},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\7ZTDJB72\\Hau et al. - 2008 - The description-experience gap in risky choice th.pdf}
}

@article{hertwigConstructbehaviorGapDescription2018,
  title = {The Construct-Behavior Gap and the Description\textendash Experience Gap: {{Comment}} on {{Regenwetter}} and {{Robinson}} (2017).},
  shorttitle = {The Construct\textendash Behavior Gap and the Description\textendash Experience Gap},
  author = {Hertwig, Ralph and Pleskac, Timothy J.},
  year = {2018},
  month = oct,
  journal = {Psychological Review},
  volume = {125},
  number = {5},
  pages = {844--849},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/rev0000121},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\IGFS3MWD\\Hertwig and Pleskac - 2018 - The construct–behavior gap and the description–exp.pdf}
}

@article{hertwigDecisionsExperienceEffect2004,
  title = {Decisions from Experience and the Effect of Rare Events in Risky Choice},
  author = {Hertwig, Ralph and Barron, Greg and Weber, Elke U. and Erev, Ido},
  year = {2004},
  month = aug,
  journal = {Psychological Science},
  volume = {15},
  number = {8},
  pages = {534--539},
  issn = {0956-7976, 1467-9280},
  doi = {10.1111/j.0956-7976.2004.00715.x},
  abstract = {When people have access to information sources such as newspaper weather forecasts, drug-package inserts, and mutual-fund brochures, all of which provide convenient descriptions of risky prospects, they can make decisions from description. When people must decide whether to back up their computer's hard drive, cross a busy street, or go out on a date, however, they typically do not have any summary description of the possible outcomes or their likelihoods. For such decisions, people can call only on their own encounters with such prospects, making decisions from experience. Decisions from experience and decisions from description can lead to dramatically different choice behavior. In the case of decisions from description, people make choices as if they overweight the probability of rare events, as described by prospect theory. We found that in the case of decisions from experience, in contrast, people make choices as if they underweight the probability of rare events, and we explored the impact of two possible causes of this underweighting\textemdash reliance on relatively small samples of information and overweighting of recently sampled information. We conclude with a call for two different theories of risky choice.},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\CI6SUI2J\\Hertwig et al. - 2004 - Decisions from Experience and the Effect of Rare E.pdf}
}

@article{hertwigDecisionsExperienceWhy2010,
  title = {Decisions from Experience: {{Why}} Small Samples?},
  shorttitle = {Decisions from Experience},
  author = {Hertwig, Ralph and Pleskac, Timothy J.},
  year = {2010},
  month = may,
  journal = {Cognition},
  volume = {115},
  number = {2},
  pages = {225--237},
  issn = {00100277},
  doi = {10.1016/j.cognition.2009.12.009},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\2W7XFMBE\\Hertwig and Pleskac - 2010 - Decisions from experience Why small samples.pdf}
}

@article{hertwigDescriptionexperienceGapRisky2009,
  title = {The Description-Experience Gap in Risky Choice},
  author = {Hertwig, Ralph and Erev, Ido},
  year = {2009},
  month = dec,
  journal = {Trends in Cognitive Sciences},
  volume = {13},
  number = {12},
  pages = {517--523},
  issn = {13646613},
  doi = {10.1016/j.tics.2009.09.004},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\UWZ333MQ\\Hertwig and Erev - 2009 - The description–experience gap in risky choice.pdf}
}

@article{hillsInformationSearchDecisions2010,
  title = {Information Search in Decisions from Experience: Do Our Patterns of Sampling Foreshadow Our Decisions?},
  shorttitle = {Information Search in Decisions from Experience},
  author = {Hills, Thomas T. and Hertwig, Ralph},
  year = {2010},
  month = dec,
  journal = {Psychological Science},
  volume = {21},
  number = {12},
  pages = {1787--1792},
  issn = {0956-7976, 1467-9280},
  doi = {10.1177/0956797610387443},
  abstract = {Do different patterns of sampling influence the decisions people make, even when the information the decisions are based on is equivalent? Do more and less switching between options correlate with different kinds of decision policies? In past research, the correspondence between search and decision patterns has been difficult to ascertain because the information obtained has often been confounded with its consequences in an exploration-exploitation trade-off. We used a sampling task in which information is explored prior to being exploited. We found that search patterns did reveal decision policies. Individuals who transitioned more frequently between options were more likely to choose options that win most of the time in round-wise comparisons and were more likely to underweight rare, risky events. Less switching between options was associated with choosing options that win in the long run on the basis of summary comparisons\textemdash decisions consistent with expected-value maximization and linear weighting of outcomes.},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\GN59WA5K\\Hills and Hertwig - 2010 - Information Search in Decisions From Experience D.pdf}
}

@article{jareckiFrameworkBuildingCognitive2020,
  title = {A Framework for Building Cognitive Process Models},
  author = {Jarecki, Jana B. and Tan, Jolene H. and Jenny, Mirjam A.},
  year = {2020},
  month = dec,
  journal = {Psychonomic Bulletin \& Review},
  volume = {27},
  number = {6},
  pages = {1218--1229},
  issn = {1069-9384, 1531-5320},
  doi = {10.3758/s13423-020-01747-2},
  abstract = {Abstract                            The term               process model               is widely used, but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that the following dimensions characterize process models: They have a scope that includes different levels of abstraction. They specify a hypothesized mental information transformation. They make predictions not only for the behavior of interest but also for processes. The models' predictions for the processes can be derived from the input, without reverse inference from the output data. Moreover, the presumed information transformation steps are not contradicting current knowledge of human cognitive capacities. Lastly, process models require a conceptual scope specifying levels of abstraction for the information entering the mind, the proposed mental events, and the behavior of interest. This framework can be used for refining models before testing them or after testing them empirically, and it does not rely on specific modeling paradigms. It can be a guideline for developing cognitive process models. Moreover, the framework can advance currently unresolved debates about which models belong to the category of process models.},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\ZQKHKARG\\Jarecki et al. - 2020 - A framework for building cognitive process models.pdf}
}

@article{kahnemanProspectTheoryAnalysis1979,
  title = {Prospect Theory: {{An}} Analysis of Decision under Risk},
  shorttitle = {Prospect Theory},
  author = {Kahneman, Daniel and Tversky, Amos},
  year = {1979},
  month = mar,
  journal = {Econometrica},
  volume = {47},
  number = {2},
  pages = {263},
  issn = {00129682},
  doi = {10.2307/1914185},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\XPGYPFQD\\Kahneman and Tversky - 1979 - Prospect Theory An Analysis of Decision under Ris.pdf}
}

@article{kellenHowVariantAre2016,
  title = {How (in)Variant Are Subjective Representations of Described and Experienced Risk and Rewards?},
  author = {Kellen, David and Pachur, Thorsten and Hertwig, Ralph},
  year = {2016},
  month = dec,
  journal = {Cognition},
  volume = {157},
  pages = {126--138},
  issn = {00100277},
  doi = {10.1016/j.cognition.2016.08.020},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\QQFH2QJQ\\Kellen et al. - 2016 - How (in)variant are subjective representations of .pdf}
}

@article{krefeld-schwalbStructuralParameterInterdependencies2022,
  title = {Structural Parameter Interdependencies in Computational Models of Cognition.},
  author = {{Krefeld-Schwalb}, Antonia and Pachur, Thorsten and Scheibehenne, Benjamin},
  year = {2022},
  month = mar,
  journal = {Psychological Review},
  volume = {129},
  number = {2},
  pages = {313--339},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/rev0000285},
  langid = {english}
}

@article{liederResourcerationalAnalysisUnderstanding2020,
  title = {Resource-Rational Analysis: {{Understanding}} Human Cognition as the Optimal Use of Limited Computational Resources},
  shorttitle = {Resource-Rational Analysis},
  author = {Lieder, Falk and Griffiths, Thomas L.},
  year = {2020},
  journal = {Behavioral and Brain Sciences},
  volume = {43},
  publisher = {{Cambridge University Press}},
  issn = {0140-525X, 1469-1825},
  doi = {10.1017/S0140525X1900061X},
  abstract = {Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.},
  langid = {english},
  keywords = {bounded rationality,cognitive biases,cognitive mechanisms,cognitive modeling,representations,resource rationality},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\YVNTR3HI\\Lieder and Griffiths - 2020 - Resource-rational analysis Understanding human co.pdf;C\:\\Users\\Linus Hof\\Zotero\\storage\\PMZUKHQ2\\586866D9AD1D1EA7A1EECE217D392F4A.html}
}

@inproceedings{markantModelingChoiceSearch2015,
  title = {Modeling Choice and Search in Decisions from Experience: {{A}} Sequential Sampling Approach},
  booktitle = {Proceedings of the 37th {{Annual Meeting}} of the {{Cognitive Science Society}}},
  author = {Markant, Douglas B. and Pleskac, Timothy J. and Diederich, Adele and Pachur, Thorsten},
  editor = {Noelle, D. C. and Dale, R. and Warlaumont, A. S. and Yoshimi, J. and Matlock, T. and Jennings, C. D. and Maglio P. P.},
  year = {2015},
  pages = {1512--1517},
  publisher = {{Cognitive Science Society}},
  address = {{Austin, TX}}
}

@article{marrUnderstandingComputationUnderstanding1977,
  title = {From Understanding Computation to Understanding Neural Circuitry},
  author = {Marr, David and Poggio},
  year = {1977},
  journal = {Neuroscience Research Program Bulletin},
  volume = {15},
  pages = {470--488}
}

@book{marrVisionComputationalInvestigation1982,
  title = {Vision: A Computational Investigation into the Human Representation and Processing of Visual Information},
  shorttitle = {Vision},
  author = {Marr, David},
  year = {1982},
  publisher = {{W.H. Freeman and Company}},
  address = {{San Francisco}},
  isbn = {978-0-262-51462-0},
  lccn = {QP475 .M27 2010},
  keywords = {Data processing,Human information processing,Mathematical models,Vision},
  annotation = {OCLC: ocn472791457}
}

@article{meehlAppraisingAmendingTheories1990,
  title = {Appraising and Amending Theories: {{The}} Strategy of Lakatosian Defense and Two Principles That Warrant It},
  shorttitle = {Appraising and Amending Theories},
  author = {Meehl, Paul E.},
  year = {1990},
  month = apr,
  journal = {Psychological Inquiry},
  volume = {1},
  number = {2},
  pages = {108--141},
  issn = {1047-840X, 1532-7965},
  doi = {10.1207/s15327965pli0102_1},
  langid = {english}
}

@article{nilssonHierarchicalBayesianParameter2011,
  title = {Hierarchical {{Bayesian}} Parameter Estimation for Cumulative Prospect Theory},
  author = {Nilsson, H{\aa}kan and Rieskamp, J{\"o}rg and Wagenmakers, Eric-Jan},
  year = {2011},
  month = feb,
  journal = {Journal of Mathematical Psychology},
  volume = {55},
  number = {1},
  pages = {84--93},
  issn = {00222496},
  doi = {10.1016/j.jmp.2010.08.006},
  langid = {english}
}

@article{pachurHowTwainCan2017,
  title = {How the Twain Can Meet: {{Prospect}} Theory and Models of Heuristics in Risky Choice},
  shorttitle = {How the Twain Can Meet},
  author = {Pachur, Thorsten and Suter, Renata S. and Hertwig, Ralph},
  year = {2017},
  month = mar,
  journal = {Cognitive Psychology},
  volume = {93},
  pages = {44--73},
  issn = {00100285},
  doi = {10.1016/j.cogpsych.2017.01.001},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\EFQNXVMG\\Pachur et al. - 2017 - How the twain can meet Prospect theory and models.pdf}
}

@article{payneAdaptiveStrategySelection1988,
  title = {Adaptive Strategy Selection in Decision Making},
  author = {Payne, John W. and Bettman, James R. and Johnson, Eric J.},
  year = {1988},
  journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},
  volume = {14},
  number = {3},
  pages = {534--552},
  issn = {1939-1285, 0278-7393},
  doi = {10.1037/0278-7393.14.3.534},
  langid = {english}
}

@article{plonskyRelianceSmallSamples2015,
  title = {Reliance on Small Samples, the Wavy Recency Effect, and Similarity-Based Learning.},
  author = {Plonsky, Ori and Teodorescu, Kinneret and Erev, Ido},
  year = {2015},
  journal = {Psychological Review},
  volume = {122},
  number = {4},
  pages = {621--647},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/a0039413},
  langid = {english}
}

@article{prelecProbabilityWeightingFunction1998,
  title = {The Probability Weighting Function},
  author = {Prelec, Drazen},
  year = {1998},
  month = may,
  journal = {Econometrica},
  volume = {66},
  number = {3},
  pages = {497},
  issn = {00129682},
  doi = {10.2307/2998573}
}

@article{rakowBiasedSamplesNot2008,
  title = {Biased Samples Not Mode of Presentation: {{Re-examining}} the Apparent Underweighting of Rare Events in Experience-Based Choice},
  shorttitle = {Biased Samples Not Mode of Presentation},
  author = {Rakow, Tim and Demes, Kali A. and Newell, Ben R.},
  year = {2008},
  month = jul,
  journal = {Organizational Behavior and Human Decision Processes},
  volume = {106},
  number = {2},
  pages = {168--179},
  issn = {07495978},
  doi = {10.1016/j.obhdp.2008.02.001},
  langid = {english}
}

@article{ratcliffComparisonSequentialSampling2004,
  title = {A Comparison of Sequential Sampling Models for Two-Choice Reaction Time},
  author = {Ratcliff, Roger and Smith, Philip L.},
  year = {2004},
  journal = {Psychological Review},
  volume = {111},
  number = {2},
  pages = {333--367},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/0033-295X.111.2.333},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\UTVJSYC2\\Ratcliff and Smith - 2004 - A Comparison of Sequential Sampling Models for Two.pdf}
}

@article{regenwetterConstructbehaviorGapBehavioral2017,
  title = {The Construct-Behavior Gap in Behavioral Decision Research: {{A}} Challenge beyond Replicability},
  shorttitle = {The Construct\textendash Behavior Gap in Behavioral Decision Research},
  author = {Regenwetter, Michel and Robinson, Maria M.},
  year = {2017},
  month = oct,
  journal = {Psychological Review},
  volume = {124},
  number = {5},
  pages = {533--550},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/rev0000067},
  langid = {english}
}

@article{scheibehenneUsingBayesianHierarchical2015,
  title = {Using {{Bayesian}} Hierarchical Parameter Estimation to Assess the Generalizability of Cognitive Models of Choice},
  author = {Scheibehenne, Benjamin and Pachur, Thorsten},
  year = {2015},
  month = apr,
  journal = {Psychonomic Bulletin \& Review},
  volume = {22},
  number = {2},
  pages = {391--407},
  issn = {1069-9384, 1531-5320},
  doi = {10.3758/s13423-014-0684-4},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\CE7AIL2J\\Scheibehenne and Pachur - 2015 - Using Bayesian hierarchical parameter estimation t.pdf}
}

@article{simonRationalChoiceStructure1956,
  title = {Rational Choice and the Structure of the Environment},
  author = {Simon, Herbert A.},
  year = {1956},
  journal = {Psychological Review},
  volume = {63},
  number = {2},
  pages = {129--138},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/h0042769},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\QXS244MA\\Simon - 1956 - Rational choice and the structure of the environme.pdf}
}

@article{stewartDecisionSampling2006,
  title = {Decision by Sampling},
  author = {Stewart, Neil and Chater, Nick and Brown, Gordon D.A.},
  year = {2006},
  month = aug,
  journal = {Cognitive Psychology},
  volume = {53},
  number = {1},
  pages = {1--26},
  issn = {00100285},
  doi = {10.1016/j.cogpsych.2005.10.003},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\TBNPAP52\\Stewart et al. - 2006 - Decision by sampling.pdf}
}

@techreport{stewartPsychologicalParametersHave2018,
  type = {Preprint},
  title = {Psychological Parameters Have Units: {{A}} Bug Fix for Stochastic Prospect Theory and Other Decision Models},
  shorttitle = {Psychological Parameters Have Units},
  author = {Stewart, Neil and Scheibehenne, Benjamin and Pachur, Thorsten},
  year = {2018},
  month = apr,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/qvgcd},
  abstract = {To fit models like prospect theory or expected utility theory to choice data, a stochastic model is needed to turn differences in values into choice probabilities. In these models, the parameter measuring risk aversion is strongly correlated with the parameter measuring the sensitivity to differences in value. We use dimensional analysis from the physical sciences to show that this is because the sensitivity parameter has units which depend on the risk aversion parameter. This means that comparing sensitivities across individuals with different level of risk aversion is meaningless and forbidden. We suggest a simple bug fix for prospect theory and other decision models which corrects this problem. The bug fix completely removes the correlation between sensitivity and risk aversion parameters in model estimations and allows the parameters to be interpreted as they were originally intended.},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\438KCNC6\\Stewart et al. - 2018 - Psychological parameters have units A bug fix for.pdf}
}

@article{stottCumulativeProspectTheory2006,
  title = {Cumulative Prospect Theory's Functional Menagerie},
  author = {Stott, Henry P.},
  year = {2006},
  month = mar,
  journal = {Journal of Risk and Uncertainty},
  volume = {32},
  number = {2},
  pages = {101--130},
  issn = {0895-5646, 1573-0476},
  doi = {10.1007/s11166-006-8289-6},
  langid = {english}
}

@article{tverskyAdvancesProspectTheory1992,
  title = {Advances in Prospect Theory: {{Cumulative}} Representation of Uncertainty},
  shorttitle = {Advances in Prospect Theory},
  author = {Tversky, Amos and Kahneman, Daniel},
  year = {1992},
  month = oct,
  journal = {Journal of Risk and Uncertainty},
  volume = {5},
  number = {4},
  pages = {297--323},
  issn = {0895-5646, 1573-0476},
  doi = {10.1007/BF00122574},
  langid = {english}
}

@article{ungemachAreProbabilitiesOverweighted2009,
  title = {Are Probabilities Overweighted or Underweighted When Rare Outcomes Are Experienced (Rarely)?},
  author = {Ungemach, Christoph and Chater, Nick and Stewart, Neil},
  year = {2009},
  month = apr,
  journal = {Psychological Science},
  volume = {20},
  number = {4},
  pages = {473--479},
  issn = {0956-7976, 1467-9280},
  doi = {10.1111/j.1467-9280.2009.02319.x},
  abstract = {When making decisions involving risky outcomes on the basis of verbal descriptions of the outcomes and their associated probabilities, people behave as if they overweight small probabilities. In contrast, when the same outcomes are instead experienced in a series of samples, people behave as if they underweight small probabilities. We present two experiments showing that the existing explanations of the underweighting observed in decisions from experience are not sufficient to account for the effect. Underweighting was observed when participants experienced representative samples of events, so it cannot be attributed to undersampling of the small probabilities. In addition, earlier samples predicted decisions just as well as later samples did, so underweighting cannot be attributed to recency weighting. Finally, frequency judgments were accurate, so underweighting cannot be attributed to judgment error. Furthermore, we show that the underweighting of small probabilities is also reflected in the best-fitting parameter values obtained when prospect theory, the dominant model of risky choice, is applied to the data.},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\GYXBH6AE\\Ungemach et al. - 2009 - Are Probabilities Overweighted or Underweighted Wh.pdf}
}

@article{vanrooijPsychologicalModelsTheir2022,
  title = {Psychological Models and Their Distractors},
  author = {{van Rooij}, Iris},
  year = {2022},
  month = mar,
  journal = {Nature Reviews Psychology},
  volume = {1},
  number = {3},
  pages = {127--128},
  issn = {2731-0574},
  doi = {10.1038/s44159-022-00031-5},
  langid = {english}
}

@article{vanrooijTheoryTestHow2021,
  title = {Theory before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science},
  shorttitle = {Theory before the Test},
  author = {{van Rooij}, Iris and Baggio, Giosu{\`e}},
  year = {2021},
  month = jul,
  journal = {Perspectives on Psychological Science},
  volume = {16},
  number = {4},
  pages = {682--697},
  issn = {1745-6916, 1745-6924},
  doi = {10.1177/1745691620970604},
  abstract = {Drawing on the philosophy of psychological explanation, we suggest that psychological science, by focusing on effects, may lose sight of its primary explananda: psychological capacities. We revisit Marr's levels-of-analysis framework, which has been remarkably productive and useful for cognitive psychological explanation. We discuss ways in which Marr's framework may be extended to other areas of psychology, such as social, developmental, and evolutionary psychology, bringing new benefits to these fields. We then show how theoretical analyses can endow a theory with minimal plausibility even before contact with empirical data: We call this the theoretical cycle. Finally, we explain how our proposal may contribute to addressing critical issues in psychological science, including how to leverage effects to understand capacities better.},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\K54CB2HM\\van Rooij and Baggio - 2021 - Theory Before the Test How to Build High-Verisimi.pdf}
}

@article{weberPredictingRiskSensitivity2004,
  title = {Predicting Risk Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation},
  shorttitle = {Predicting Risk Sensitivity in Humans and Lower Animals},
  author = {Weber, Elke U. and Shafir, Sharoni and Blais, Ann-Ren{\'e}e},
  year = {2004},
  journal = {Psychological Review},
  volume = {111},
  number = {2},
  pages = {430--445},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/0033-295X.111.2.430},
  langid = {english}
}

@incollection{wulffAdaptiveExplorationWhat2019,
  title = {Adaptive Exploration: {{What}} You See Is up to You},
  booktitle = {Taming Uncertainty},
  author = {Wulff, Dirk U. and {Markant, Doug} and {Pleskac, Timothy J.} and {Hertwig, Ralph}},
  editor = {Hertwig, Ralph and Pleskac, Timothy J. and Pachur, Thorsten},
  year = {2019},
  publisher = {{MIT Press}},
  address = {{Cambridge, MA}},
  isbn = {978-0-262-03987-1},
  lccn = {BF463.U5 H47 2019},
  keywords = {Decision making,Psychological aspects,Uncertainty}
}

@article{wulffHowShortLongrun2015,
  title = {How Short- and Long-Run Aspirations Impact Search and Choice in Decisions from Experience},
  author = {Wulff, Dirk U. and Hills, Thomas T. and Hertwig, Ralph},
  year = {2015},
  month = nov,
  journal = {Cognition},
  volume = {144},
  pages = {29--37},
  issn = {00100277},
  doi = {10.1016/j.cognition.2015.07.006},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\D84Y6ZKJ\\Wulff et al. - 2015 - How short- and long-run aspirations impact search .pdf}
}

@article{wulffMetaanalyticReviewTwo2018,
  title = {A Meta-Analytic Review of Two Modes of Learning and the Description-Experience Gap},
  author = {Wulff, Dirk U. and {Mergenthaler-Canseco}, Max and Hertwig, Ralph},
  year = {2018},
  month = feb,
  journal = {Psychological Bulletin},
  volume = {144},
  number = {2},
  pages = {140--176},
  issn = {1939-1455, 0033-2909},
  doi = {10.1037/bul0000115},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\IPU983ET\\Wulff et al. - 2018 - A meta-analytic review of two modes of learning an.pdf}
}

@phdthesis{zilkerMeasuringModelingConstruction2020,
  title = {Measuring and Modeling the Construction of Preferences in Decision Making under Risk},
  author = {Zilker, Veronika},
  year = {2020},
  address = {{Berlin}},
  school = {Freie Universit\"at Berlin}
}

@article{zilkerNonlinearProbabilityWeighting2021,
  title = {Nonlinear Probability Weighting Can Reflect Attentional Biases in Sequential Sampling.},
  author = {Zilker, Veronika and Pachur, Thorsten},
  year = {2021},
  month = aug,
  journal = {Psychological Review},
  issn = {1939-1471, 0033-295X},
  doi = {10.1037/rev0000304},
  langid = {english},
  file = {C\:\\Users\\Linus Hof\\Zotero\\storage\\JGDU8YKT\\Zilker and Pachur - 2021 - Nonlinear probability weighting can reflect attent.pdf}
}