Book contents
- Sampling in Judgment and Decision Making
- Sampling in Judgment and Decision Making
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Part I Historical Review of Sampling Perspectives and Major Paradigms
- Part II Sampling Mechanisms
- Chapter 5 The J/DM Separation Paradox and the Reliance on the Small Samples Hypothesis
- Chapter 6 Sampling as Preparedness in Evaluative Learning
- Chapter 7 The Dog that Didn’t Bark
- Chapter 8 Unpacking Intuitive and Analytic Memory Sampling in Multiple-Cue Judgment
- Part III Consequences of Selective Sampling
- Part IV Truncation and Stopping Rules
- Part V Sampling as a Tool in Social Environments
- Part VI Computational Approaches
- Index
- References
Chapter 7 - The Dog that Didn’t Bark
Bayesian Approaches to Reasoning from Censored Data
from Part II - Sampling Mechanisms
Published online by Cambridge University Press: 01 June 2023
Book contents
- Sampling in Judgment and Decision Making
- Sampling in Judgment and Decision Making
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Part I Historical Review of Sampling Perspectives and Major Paradigms
- Part II Sampling Mechanisms
- Chapter 5 The J/DM Separation Paradox and the Reliance on the Small Samples Hypothesis
- Chapter 6 Sampling as Preparedness in Evaluative Learning
- Chapter 7 The Dog that Didn’t Bark
- Chapter 8 Unpacking Intuitive and Analytic Memory Sampling in Multiple-Cue Judgment
- Part III Consequences of Selective Sampling
- Part IV Truncation and Stopping Rules
- Part V Sampling as a Tool in Social Environments
- Part VI Computational Approaches
- Index
- References
Summary
Inductive reasoning involves generalizing from samples of evidence to novel cases. Previous work in this field has focused on how sample contents guide the inductive process. This chapter reviews a more recent and complementary line of research that emphasizes the role of the sampling process in induction. In line with a Bayesian model of induction, beliefs about how a sample was generated are shown to have a profound effect on the inferences that people draw. This is first illustrated in research on beliefs about sampling intentions: was the sample generated to illustrate a concept or was it generated randomly? A related body of work examines the effects of sampling frames: beliefs about selection mechanisms that cause some instances to appear in a sample and others to be excluded. The chapter describes key empirical findings from these research programs and highlights emerging issues such as the effect of timing of information about sample generation (i.e., whether it comes before or after the observed sample) and individual differences in inductive reasoning. The concluding section examines how this work can be extended to more complex reasoning problems where observed data are subject to selection biases.
- Type
- Chapter
- Information
- Sampling in Judgment and Decision Making , pp. 153 - 176Publisher: Cambridge University PressPrint publication year: 2023
References
Baker, C. L., Jara-Ettinger, J., Saxe, R., & Tenenbaum, J. B. (2017). Rational quantitative attribution of beliefs, desires and percepts in human mentalizing. Nature Human Behaviour, 1, 0064. https://doi.org/10.1038/s41562–017-0064CrossRefGoogle Scholar
Bareinboim, E., Tian, J., & Pearl, J. (2014). Recovering from selection bias in causal and statistical inference. In Proceedings of the twenty-eighth AAAI conference on artificial intelligence, pp. 2410–2416.Google Scholar
Denrell, J. (2005). Why most people disapprove of me: Experience sampling in impression formation. Psychological Review, 112(4), 951–978. https://doi.org/10.1037/0033-295X.112.4.951CrossRefGoogle ScholarPubMed
Ecker, U. K., Lewandowsky, S., & Tang, D. T. (2010). Explicit warnings reduce but do not eliminate the continued influence of misinformation. Memory & Cognition, 38(8), 1087–1100.CrossRefGoogle Scholar
Eddy, D. (1982). Probabilistic reasoning in clinical medicine: Problems and opportunities. In Kahneman, D., Slovic, P., & Tversky, A. (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 249–267). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511809477.019Google Scholar
Edwards, W. (1971). Bayesian and regression models of human information processing: A myopic perspective. Organizational Behavior and Human Performance, 6(6), 639–648.Google Scholar
Elwin, E., Juslin, P., Olsson, H., & Enkvist, T. (2007). Constructivist coding: Learning from selective feedback. Psychological Science, 18(2), 105–110.Google Scholar
Feeney, A. (2018). Forty years of progress on category-based inductive reasoning. In Ball, L. J. & Thompson, V. A. (Eds.), The Routledge international handbook of thinking and reasoning (pp. 167–185). New York: Routledge/Taylor & Francis.Google Scholar
Feeney, A., & Heit, E. (2011). Properties of the diversity effect in category-based inductive reasoning. Thinking & Reasoning, 17(2), 156–181. https://doi.org/10.1080/13546783.2011.566703CrossRefGoogle Scholar
Feiler, D. C., Tong, J. D., & Larrick, R. P. (2013). Biased judgment in censored environments. Management Science, 59(3), 573–591.Google Scholar
Fiedler, K. (2012). Meta-cognitive myopia and the dilemmas of inductive-statistical inference. In Ross, B. (Ed.), The Psychology of Learning and Motivation (Vol. 57, pp. 1–55). San Diego: Elsevier. https://doi.org/10.1016/B978–0-12-394293-7.00001-7Google Scholar
Fiedler, K., Ackerman, R., & Scarampi, C. (2019). Metacognition: Monitoring and controlling one’s own knowledge, reasoning and decisions. In Sternberg, R. J. & Funke, J. (Eds.), The psychology of human thought: An introduction (pp. 89–111). Heidelberg: Heidelberg University. https://doi.org/10.17885/heiup.470.c6669Google Scholar
Fiedler, K., Brinkmann, B., Betsch, T., & Wild, B. (2000). A sampling approach to biases in conditional probability judgments: Beyond base rate neglect and statistical format. Journal of Experimental Psychology: General, 129(3), 399–418. https://doi.org/10.1037/0096-3445.129.3.399CrossRefGoogle ScholarPubMed
Fiedler, K., Hütter, M., Schott, M., & Kutzner, F. (2019). Metacognitive myopia and the overutilization of misleading advice. Journal of Behavioral Decision Making, 32(3), 317–333. https://doi.org/10.1002/bdm.2109CrossRefGoogle Scholar
Franke, M., Dulcinati, G., & Pouscoulous, N. (2020). Strategies of deception: Under‐informativity, uninformativity, and lies: Misleading with different kinds of implicature. Topics in Cognitive Science, 12(2), 583–607. https://doi.org/10.1111/tops.12456CrossRefGoogle ScholarPubMed
Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19(4), 25–42. https://doi.org/10.1257/089533005775196732Google Scholar
Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences, 20(11), 818–829. https://doi.org/10.1016/j.tics.2016.08.005CrossRefGoogle ScholarPubMed
Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51(4), 334–384. https://doi.org/10.1016/j.cogpsych.2005.05.004CrossRefGoogle ScholarPubMed
Hamill, R., Wilson, T. D., & Nisbett, R. E. (1980). Insensitivity to sample bias: Generalizing from atypical cases. Journal of Personality and Social Psychology, 39(4), 578–589. https://doi.org/10.1037/0022-3514.39.4.578Google Scholar
Hayes, B. K., Banner, S., Forrester, S., & Navarro, D. J. (2019). Selective sampling and inductive inference: Drawing inferences based on observed and missing evidence. Cognitive Psychology, 113, Article 101221. https://doi.org/10.1016/j.cogpsych.2019.05.003Google Scholar
Hayes, B. K., Banner, S., & Navarro, D. J. (2017). Sampling frames, Bayesian inference and inductive reasoning. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. (Eds.), Proceedings of the 39th annual meeting of the Cognitive Science Society (pp. 488–493).Google Scholar
Hayes, B. K., Hawkins, G. E., Newell, B. R., Pasqualino, M., & Rehder, B. (2014). The role of causal models in multiple judgments under uncertainty. Cognition, 133(3), 611–620. https://doi.org/10.1016/j.cognition.2014.08.011Google Scholar
Hayes, B. K., & Heit, E. (2018). Inductive reasoning 2.0. Wiley interdisciplinary reviews: Cognitive Science, 9(3), 1–13, e1459, https://doi.org/10.1002/wcs.1459Google Scholar
Hayes, B. K., Navarro, D. J., Stephens, R. G., Ransom, K. J., & Dilevski, N. (2019). The diversity effect in inductive reasoning depends on sampling assumptions. Psychonomic Bulletin & Review, 26(3), 1043–1050. https://doi.org/10.3758/s13423–018-1562-2Google Scholar
Hayes, B. K., Wen, Y. Y., Connor Desai, S., & Navarro, D. J. (2022). Who is sensitive to selection biases in inductive reasoning? Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication. https://doi.org/10.1037/xlm0001171.Google Scholar
Hemmer, P., Tauber, S., & Steyvers, M. (2015). Moving beyond qualitative evaluations of Bayesian models of cognition. Psychonomic Bulletin & Review, 22(3), 614–628. https://doi.org/10.3758/s13423–014-0725-zGoogle Scholar
Henriksson, M. P., Elwin, E., & Juslin, P. (2010). What is coded into memory in the absence of outcome feedback? Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(1), 1–16.Google Scholar
Hogarth, R., Lejarraga, T., & Soyer, E. (2015). The two settings of kind and wicked learning environments. Current Directions in Psychological Science, 24, 379–385.CrossRefGoogle Scholar
Hsu, A. S., Horng, A., Griffiths, T. L., & Chater, N. (2017). When absence of evidence is evidence of absence: Rational inferences from absent data. Cognitive Science, 41(Suppl 5), 1155–1167. https://doi.org/10.1111/cogs.12356Google Scholar
Koehler, J., & Mercer, M. (2009). Selection neglect in mutual fund advertisements. Management Science, 55(7), 1107–1121.Google Scholar
Lawson, C. A., & Kalish, C. W. (2009). Sample selection and inductive generalization. Memory & Cognition, 37(5), 596–607. https://doi.org/10.3758/MC.37.5.596Google Scholar
Le Mens, G., & Denrell, J. (2011). Rational learning and information sampling. Psychological Review, 118, 379–392.Google Scholar
Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106–131.Google Scholar
Lewandowsky, S., Oberauer, K., Yang, L.-X., & Ecker, U. K. H. (2010). A working memory test battery for MATLAB. Behavior Research Methods, 42(2), 571–585. https://doi.org/10.3758/BRM.42.2.571Google Scholar
Liew, J., Grisham, J. R., & Hayes, B. K. (2018). Inductive and deductive reasoning in obsessive-compulsive disorder. Journal of Behavior Therapy and Experimental Psychiatry, 59, 79–86. https://doi.org/10.1016/j.jbtep.2017.12.001CrossRefGoogle ScholarPubMed
Lombrozo, T. (2006). The structure and function of explanations. Trends in Cognitive Sciences, 10(10), 464–470.CrossRefGoogle ScholarPubMed
Mascaro, O., & Sperber, D. (2009). The moral, epistemic, and mindreading components of children’s vigilance towards deception. Cognition, 112(3), 367–380. https://doi.org/10.1016/j.cognition.2009.05.012Google Scholar
Medin, D. L., Coley, J., Storms, G., & Hayes, B. K. (2003). A relevance theory of induction. Psychonomic Bulletin & Review, 10, 517–532.Google Scholar
Mercier, H. (2016). The argumentative theory: Predictions and empirical evidence. Trends in Cognitive Sciences, 20(9), 689–700. https://doi.org/10.1016/j.tics.2016.07.001Google Scholar
Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences, 34(2), 57–74. https://doi.org/10.1017/S0140525X10000968CrossRefGoogle ScholarPubMed
Navarro, D., Dry, M., & Lee, M. (2012). Sampling assumptions in inductive generalization. Cognitive Science, 36(2), 187–223. https://doi.org/10.1111/j.1551-6709.2011.01212.xGoogle Scholar
Oaksford, M., & Chater, N. (2020). New paradigms in the psychology of reasoning. Annual Review of Psychology, 71, 305–330. https://doi.org/10.1146/annurev-psych-010419-051132CrossRefGoogle ScholarPubMed
Osherson, D. N., Smith, E. E., Wilkie, O., Lopez, A., & Shafir, E. (1990). Category-based induction. Psychological Review, 97(2), 185–200. https://doi.org/10.1037/0033-295X.97.2.185CrossRefGoogle Scholar
Pearl, J. (2000). Causality: Models, reasoning and inferences. San Francisco, CA: Morgan Kaufman.Google Scholar
Peterson, C. R., & Beach, L. R. (1967). Man as an intuitive statistician. Psychological Bulletin, 68(1), 29–46. https://doi.org/10.1037/h0024722Google Scholar
Ransom, K., Perfors, A., Hayes, B. K., & Connor Desai, S. (2022). What do our sampling assumptions affect how we encode data or how we reason from it? Journal of Experimental Psychology: Learning, Memory and Cognition. Advance online publication. https://doi.org/10.1037/xlm0001149Google Scholar
Ransom, K., Perfors, A., & Navarro, D. (2016). Leaping to conclusions: Why premise relevance affects argument strength. Cognitive Science, 40(7), 1775–1796. https://doi.org/10.1111/cogs.12308Google Scholar
Ransom, K., Voorspoels, W., Perfors, A., & Navarro, D. (2017). A cognitive analysis of deception without lying. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. J. (Eds.), Proceedings of the 39th annual conference of the Cognitive Science Society (pp. 992–997). Austin, TX: Cognitive Science Society.Google Scholar
Rhodes, M., Bonawitz, E., Shafto, P., Chen, A., & Caglar, L. (2015). Controlling the message: Preschoolers’ use of information to teach and deceive others. Frontiers in Psychology, 6, Article 867. https://doi.org/10.3389/fpsyg.2015.00867CrossRefGoogle ScholarPubMed
Rips, L. J. (1975). Inductive judgments about natural categories. Journal of Verbal Learning & Verbal Behavior, 14(6), 665–681. https://doi.org/10.1016/S0022-5371(75)80055-7CrossRefGoogle Scholar
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42. https://doi.org/10.1177/2515245917745629Google Scholar
Ross, L., Lepper, M. R., & Hubbard, M. (1975). Perseverance in self-perception and social perception: Biased attribution processes in the debriefing paradigm. Journal of Personality and Social Psychology, 32, 880–892.Google Scholar
Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117(4), 1144–1167. https://doi.org/10.1037/a0020511Google Scholar
Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others: The consequences of psychological reasoning for human learning. Perspectives on Psychological Science, 7(4), 341–351. https://doi.org/10.1177/1745691612448481CrossRefGoogle ScholarPubMed
Sloman, S. (2005). Causal models: How people think about the world and its alternatives. Oxford: Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195183115.001.0001Google Scholar
Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity and Bayesian inference. Behavioral and Brain Sciences, 24(4), 629–640. https://doi.org/10.1017/S0140525X01000061Google Scholar
Voorspoels, W., Navarro, D. J., Perfors, A., Ransom, K., & Storms, G. (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology, 81, 1–25. https://doi.org/10.1016/j.cogpsych.2015.07.001CrossRefGoogle ScholarPubMed
Vul, E., Goodman, N., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38(4), 599–637. https://doi.org/10.1111/cogs.12101Google Scholar
Zhu, J. Q., Sanborn, A. N., & Chater, N. (2020). The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments. Psychological Review, 127(5), 719–748. http://doi.org/10.1037/rev0000190CrossRefGoogle ScholarPubMed