Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-06-17T13:01:49.788Z Has data issue: false hasContentIssue false

Part II - Sampling Mechanisms

Published online by Cambridge University Press:  01 June 2023

Edited by
Klaus Fiedler ,
Peter Juslin  and
Jerker Denrell
Klaus Fiedler
Affiliation:
Universität Heidelberg
Peter Juslin
Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell
Affiliation:
University of Warwick
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Sampling in Judgment and Decision Making , pp. 113 - 204
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Abdellaoui, M., L’Haridon, O., & Paraschiv, C. (2011). Experienced vs described uncertainty: Do we need two prospect theory specifications? Management Science, 57(10), 1879–1895.CrossRefGoogle Scholar
Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine. Econometrica: Journal of the Econometric Society, 21(4), 503–546. www.jstor.org/stable/1907921Google Scholar
Barron, G., & Erev, I. (2003). Small feedback-based decisions and their limited correspondence to description-based decisions. Journal of Behavioral Decision Making, 16(3), 215–233.CrossRefGoogle Scholar
Barron, G., & Yechiam, E. (2009). The coexistence of overestimation and underweighting of rare events and the contingent recency effect. Judgment and Decision Making, 4(6), 447–460. www.sjdm.org/~baron/journal/9729b/jdm9729b.pdfCrossRefGoogle Scholar
Cohen, D., Plonsky, O., & Erev, I. (2020). On the impact of experience on probability weighting in decisions under risk. Decision, 7(2), 153–162.CrossRefGoogle Scholar
Denrell, J., & March, J. G. (2001). Adaptation as information restriction: The hot stove effect. Organization Science, 12(5), 523–538.CrossRefGoogle Scholar
Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience. Psychological Review, 124(4), 369–409. https://doi.org/10.1037/rev0000062Google Scholar
Erev, I., Ert, E., & Roth, A. E. (2010a). A choice prediction competition for market entry games: An introduction. Games, 1(2), 117–136. https://doi.org/10.3390/g1020117CrossRefGoogle Scholar
Erev, I., Ert, E., Roth, A. E., Haruvy, E., Herzog, S. M., Hau, R., Hertwig, R., Stewart, T., West, R., & Lebiere, C. (2010b). A choice prediction competition: Choices from experience and from description. Journal of Behavioral Decision Making, 23(1), 15–47. https://doi.org/10.1002/bdm.683CrossRefGoogle Scholar
Erev, I., Glozman, I., & Hertwig, R. (2008a). What impacts the impact of rare events. Journal of Risk and Uncertainty, 36(2), 153–177. https://doi.org/10.1007/s11166–008-9035-zGoogle Scholar
Erev, I., & Roth, A. E. (2014). Maximization, learning, and economic behavior. Proceedings of the National Academy of Sciences, 111(Supplement 3), 10818–10825.Google Scholar
Erev, I., Shimonovich, D., Schurr, A., & Hertwig, R. (2008b). Base rates: How to make the intuitive mind appreciate or neglect them. In Intuition in judgment and decision making (pp. 135–148). Hillsdale, NJ: Erlbaum.Google Scholar
Erev, I., Wallsten, T. S., & Budescu, D. V. (1994). Simultaneous over- and :underconfidence: The role of error in judgment processes. Psychological Review, 101(3), 519–527.Google Scholar
Erev, I. Yakobi, O., Ashby, N. J. S., & Chater, N. (2022). The impact of experience on decisions based on pre-choice samples, and the face-or-cue hypothesis. Theory and Decisions, 92(3), 583–598.Google 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.Google Scholar
Fiedler, K., & Juslin, P. (2006). Information sampling and adaptive cognition. http://books.google.com/books?hl=en&lr=&id=-vNh-Y7NV3gC&oi=fnd&pg=PR7&dq=Information+Sampling+and+adaptive+cognition&ots=4ZJhKclvj3&sig=6MtQKmy5qMFOZodurPF-TusOUgAGoogle Scholar
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1978). Fault trees: Sensitivity of estimated failure probabilities to problem representation. Journal of Experimental Psychology: Human Perception and Performance, 4(2), 330–344.Google Scholar
Fox, C. R., & Tversky, A. (1998). A belief-based account of decision under uncertainty. Management Science, 44(7), 879–895.Google Scholar
Gonzalez, C., Lerch, J. F., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27(4), 591–635. https://doi.org/10.1016/S0364-0213(03)00031-4Google Scholar
Hau, R., Pleskac, T. J., Kiefer, J., & Hertwig, R. (2008). The description–experience gap in risky choice: The role of sample size and experienced probabilities. Journal of Behavioral Decision Making, 21(5), 493–518.CrossRefGoogle Scholar
Hertwig, R., Barron, G., Weber, E., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15(8), 534–539. https://doi.org/10.1111/j.0956-7976.2004.00715.xCrossRefGoogle ScholarPubMed
Hertwig, R., & Erev, I. (2009). The description–experience gap in risky choice. Trends in Cognitive Sciences, 13(12), 517–523. https://doi.org/10.1016/j.tics.2009.09.004CrossRefGoogle ScholarPubMed
Hertwig, R., & Pleskac, T. J. (2010). Decisions from experience: Why small samples? Cognition, 115(2), 225–237. https://doi.org/10.1016/j.cognition.2009.12.009Google Scholar
Jessup, R. K., Bishara, A. J., & Busemeyer, J. R. (2008). Feedback produces divergence from prospect theory in descriptive choice. Psychological Science, 19(10), 1015–1022. https://doi.org/10.1111/j.1467-9280.2008.02193.xGoogle Scholar
Juslin, P., & Olsson, H. (1997). Thurstonian and Brunswikian origins of uncertainty in judgment: A sampling model of confidence in sensory discrimination. Psychological Review, 104(2), 344–366.Google Scholar
Juslin, P., Winman, A., & Hansson, P. (2007). The naïve intuitive statistician: A naïve sampling model of intuitive confidence intervals. Psychological Review, 114(3), 678–703.CrossRefGoogle Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.CrossRefGoogle Scholar
Kareev, Y. (2000). Seven (indeed, plus or minus two) and the detection of correlations. Psychological Review, 107(2), 397–402. https://doi.org/10.1037/TO33-295X.107.2.397Google Scholar
Lejarraga, T., & Gonzalez, C. (2011). Effects of feedback and complexity on repeated decisions from description. Organizational Behavior and Human Decision Processes, 116(2), 286–295. https://doi.org/10.1016/j.obhdp.2011.05.001Google Scholar
Marchiori, D., Di Guida, S., & Erev, I. (2015). Noisy retrieval models of over-and undersensitivity to rare events. Decision, 2(2), 82–106.CrossRefGoogle Scholar
Phillips, L. D., & Edwards, W. (1966). Conservatism in a simple probability inference task. Journal of Experimental Psychology, 72(3), 346–354.CrossRefGoogle Scholar
Plonsky, O., Apel, R., Ert, E., et al. (2019). Predicting human decisions with behavioral theories and machine learning. ArXiv Preprint ArXiv:1904.06866.Google Scholar
Plonsky, O., & Teodorescu, K. (2020). The influence of biased exposure to foregone outcomes. Journal of Behavioral Decision Making, 33(3), 393–407.Google Scholar
Plonsky, O., Teodorescu, K., & Erev, I. (2015). Reliance on small samples, the wavy recency effect, and similarity-based learning. Psychological Review, 122(4), 621–647.CrossRefGoogle ScholarPubMed
Rapoport, A., Wallsten, T. S., Erev, I., & Cohen, B. L. (1990). Revision of opinion with verbally and numerically expressed uncertainties. Acta Psychologica, 74(1), 61–79.CrossRefGoogle Scholar
Savage, L. J. (1954). The foundations of statistics. New York: John Wiley.Google Scholar
Schurr, A. (2006). Peak or freq? The effect of unpleasant extreme experiences. Haifa, Israel: Technion-Israel Institute of Technology.Google Scholar
Skinner, B. (1953). Science and human behavior. Free Press.Google Scholar
Teodorescu, K., Amir, M., & Erev, I. (2013). The experience–description gap and the role of the inter decision interval. In Srinivasan, N. & Pamni, V. S. C. (Eds.), Progress in brain research (1st ed., Vol. 202, pp. 99–115). Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-444-62604-2.00006-XGoogle Scholar
Teodorescu, K., & Erev, I. (2014). Learned helplessness and learned prevalence: Exploring the causal relations among perceived controllability, reward prevalence, and exploration. Psychological Science, 25(10), 1861–1869.Google Scholar
Wulff, D. U., Mergenthaler-Canseco, M., & Hertwig, R. (2018). A meta-analytic review of two modes of learning and the description–experience gap. Psychological Bulletin, 144(2), 140–176.Google Scholar
Yechiam, E., Barron, G., & Erev, I. (2005). The role of personal experience in contributing to different patterns of response to rare terrorist attacks. Journal of Conflict Resolution, 49(3), 430–439. https://doi.org/10.1177/0022002704270847CrossRefGoogle Scholar

References

Albrecht, S., & Carbon, C. C. (2014). The fluency amplification model: Fluent stimuli show more intense but not evidently more positive evaluations. Acta Psychologica, 148, 195–203.CrossRefGoogle Scholar
Baeyens, F., Eelen, P., Crombez, G., & Van den Bergh, O. (1992). Human evaluative conditioning: Acquisition trials, presentation schedule, evaluative style and contingency awareness. Behaviour Research and Therapy, 30(2), 133–142.CrossRefGoogle ScholarPubMed
Bar-Anan, Y., & Dahan, N. (2013). The effect of comparative context on evaluative conditioning. Social Cognition, 27, 367–375.Google ScholarPubMed
Bem, D. J. (1972). Self-perception theory. Advances in Experimental Social Psychology, 6(1), 1–62.CrossRefGoogle Scholar
Cooper, J. (1971). Personal responsibility and dissonance: The role of foreseen consequences. Journal of Personality and Social Psychology, 18(3), 354–363.CrossRefGoogle Scholar
Corneille, O., & Stahl, C. (2019). Associative attitude learning: A closer look at evidence and how it relates to attitude models. Personality and Social Psychology Review, 23(2), 161–189.CrossRefGoogle Scholar
Corneille, O., Yzerbyt, V. Y., Pleyers, G., & Mussweiler, T. (2009). Beyond awareness and resources: Evaluative conditioning may be sensitive to processing goals. Journal of Experimental Social Psychology, 45, 279–282.Google Scholar
Davey, G. C. L. (1994). Is evaluative conditioning a qualitatively distinct form of classical conditioning? Behaviour Research and Therapy, 32(3), 291–299.Google Scholar
De Houwer, J. (2009). The propositional approach to associative learning as an alternative for association formation models. Learning and Behavior, 37, 1–20.CrossRefGoogle ScholarPubMed
De Houwer, J. (2018). Propositional models of evaluative conditioning. Social Psychological Bulletin, 13(3), e28046.CrossRefGoogle Scholar
Delprato, D. J. (1980). Hereditary determinants of fears and phobias: A critical review. Behavior Therapy, 11, 79–103.Google Scholar
Denrell, J. (2005). Why most people disapprove of me: Experience sampling in impression formation. Psychological Review, 112, 951–978.Google Scholar
Elwin, E., Juslin, P., Olsson, H., & Enkvist, T. (2007). Constructivist coding: Learning from selective feedback. Psychological Science, 18, 105–110.Google Scholar
Fan, X., Bodenhausen, G. V., & Lee, A. Y. (2021). Acquiring favorable attitudes based on aversive affective cues: Examining the spontaneity and efficiency of propositional evaluative conditioning. Journal of Experimental Social Psychology, 95, Article 104139.Google Scholar
Fazio, R. H., Eiser, J. R., & Shook, N. J. (2004). Attitude formation through exploration: valence asymmetries. Journal of Personality and Social Psychology, 87, 293–311.CrossRefGoogle ScholarPubMed
Festinger, L. (1957). A theory of cognitive dissonance. Palo Alto, CA: Stanford University Press.Google Scholar
Fiedler, K. (2017). What constitutes strong psychological science? The (neglected) role of diagnosticity and a priori theorizing. Perspectives on Psychological Science, 12, 46–61.Google Scholar
Field, A. P., & Davey, G. C. L. (1999). Reevaluating evaluative conditioning: A nonassociative explanation of conditioning effects in the visual evaluative conditioning paradigm. Journal of Experimental Psychology: Animal Behavior Processes, 25(2), 211–224.Google Scholar
Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132, 692–731.Google Scholar
Gawronski, B., & Bodenhausen, G. V. (2018). Evaluative conditioning from the perspective of the associative-propositional evaluation model. Social Psychological Bulletin, 13, e28024.Google Scholar
Hofmann, W., De Houwer, J., Perugini, M., Baeyens, F., & Crombez, G. (2010). Evaluative conditioning in humans: A meta-analysis. Psychological Bulletin, 136(3), 390–421.CrossRefGoogle ScholarPubMed
Hughes, S., Ye, Y., & De Houwer, J. (2019). Evaluative conditioning effects are modulated by the nature of contextual pairings. Cognition & Emotion, 33(5), 871–884.CrossRefGoogle ScholarPubMed
Hütter, M., & Fiedler, K. (2016). Editorial: Conceptual, theoretical, and methodological challenges in evaluative conditioning research. Social Cognition, 34(5), 343–356.CrossRefGoogle Scholar
Hütter, M., & Genschow, O. (2020). What is learned in approach-avoidance tasks? On the scope and generalizability of approach-avoidance effects. Journal of Experimental Psychology: General, 149(8), 1460–1476.Google Scholar
Hütter, M., Niese, Z. A., & Ihmels, M. (2022). Bridging the gap between autonomous and predetermined paradigms: The role of sampling in evaluative learning. Journal of Experimental Psychology: General, 151(8), 1972–1998.Google Scholar
Hütter, M., & Sweldens, S. (2018). Dissociating controllable and uncontrollable effects of affective stimuli on attitudes and consumption. Journal of Consumer Research, 45, 320–349.Google Scholar
Jones, C. R., Fazio, R. H., & Olson, M. A. (2009). Implicit misattribution as a mechanism underlying evaluative conditioning. Journal of Personality and Social Psychology, 96, 933–948.Google Scholar
Juslin, P., & Olsson, H. (1997). Thurstonian and Brunswikian origins of uncertainty in judgment: A sampling model of confidence in sensory discrimination. Psychological Review, 104, 344–366.CrossRefGoogle ScholarPubMed
Kawakami, K., Phills, C. E., Steele, J. R., & Dovidio, J. F. (2007). (Close) distance makes the heart grow fonder: Improving implicit racial evaluations and interracial interactions through approach behaviors. Journal of Personality and Social Psychology, 92, 957–971.CrossRefGoogle Scholar
Landwehr, J. R., & Eckmann, L. (2020). The nature of processing fluency: Amplification versus hedonic marking. Journal of Experimental Social Psychology, 90, 103997.Google Scholar
Landwehr, J. R., Golla, B., & Reber, R. (2017). Processing fluency: An inevitable side effect of evaluative conditioning. Journal of Experimental Social Psychology, 70, 124–128.Google Scholar
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8. Gainesville: University of Florida.Google Scholar
Le Mens, G., & Denrell, J. (2011). Rational learning and information sampling: On the “naivety” assumption in sampling explanations of judgment biases. Psychological Review, 118(2), 379–392.CrossRefGoogle ScholarPubMed
Levey, A. B., & Martin, I. (1975). Classical conditioning of human “evaluative” responses. Behaviour Research and Therapy, 13(4), 221–226.CrossRefGoogle ScholarPubMed
Marchewka, A., Żurawski, Ł., Jednoróg, K., & Grabowska, A. (2014). The Nencki Affective Picture System (NAPS): Introduction to a novel, standardized, wide-range, high-quality, realistic picture database. Behavior Research Methods, 46(2), 596–610.Google Scholar
Martin, I., & Levey, A. (1994). The evaluative response: Primitive but necessary. Behaviour Research and Therapy, 32(3), 301–305.CrossRefGoogle ScholarPubMed
Martin, I., & Levey, A. B. (1978). Evaluative conditioning. Advances in Behaviour Research and Therapy, 1(2), 57–102.Google Scholar
Olson, M. A., & Fazio, R. H. (2001). Implicit attitude formation through classical conditioning. Psychological Science, 12, 413–417.Google Scholar
Pearce, J. M., & Hall, G. (1980). A model for Pavlovian learning: Variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychological Review, 87, 532–552.Google Scholar
Prager, J., Fiedler, K., & McCaughey, (2022). Thurstonian uncertainty in self-determined judgment and decision making. In Fiedler, Klaus, Juslin, Peter, & Denrell, Jerker (Eds.), Sampling in Judgment and Decision Making (pp. 311–333). Cambridge: Cambridge University Press.Google Scholar
Prager, J., Krueger, J. I., & Fiedler, K. (2018). Towards a deeper understanding of impression formation: New insights gained from a cognitive-ecological perspective. Journal of Personality and Social Psychology, 115, 379–397.CrossRefGoogle ScholarPubMed
Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H. & Prokasy, W. F. (Eds.), Classical conditioning II: Current research and theory (pp. 64–99). New York: Appleton-Century-Crofts.Google Scholar
Seligman, M. P. (1970). On the generality of the laws of learning. Psychological Review, 77, 406–418.CrossRefGoogle Scholar
Seligman, M. E. (1971). Phobias and preparedness. Behavior Therapy, 2(3), 307–320.Google Scholar
Skinner, B. F. (1938). The behavior of organisms: An experimental analysis. Oxford: Appleton-Century.Google Scholar
Sweldens, S., van Osselaer, S. M. J., & Janiszewski, C. (2010). Evaluative conditioning procedures and the resilience of conditioned brand attitudes. Journal of Consumer Research, 37, 473–489.Google Scholar
Unkelbach, C., & Fiedler, K. (2016). Contrastive CS–US relations reverse evaluative conditioning effects. Social Cognition, 34, 413–434.CrossRefGoogle Scholar
Van Dessel, P., Hughes, S. J., & De Houwer, J. (2018). Consequence-based approach-avoidance training: A new and improved method for changing behavior. Psychological Science, 29(12), 1899–1910.Google Scholar
Walther, E., & Grigoriadis, S. (2004). Why sad people like shoes better: The influence of mood on the evaluative conditioning of consumer attitudes. Psychology & Marketing, 21, 755–773.Google Scholar
Wiers, R. W., Eberl, C., Rinck, M., Becker, E. S., & Lindenmeyer, J. (2011). Retraining automatic action tendencies changes alcoholic patients’ approach bias for alcohol and improves treatment outcome. Psychological Science, 22, 490–497.Google Scholar
Winkielman, P., & Cacioppo, J. T. (2001). Mind at ease puts a smile on the face: Psychophysiological evidence that processing facilitation increases positive affect. Journal of Personality and Social Psychology, 81, 989–1000.Google Scholar

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/s41562–017-0064Google 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.CrossRefGoogle 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.112.4.951Google Scholar
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/CBO9780511809477.019CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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/10.1037/0096-3445.129.3.399Google Scholar
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.2109Google 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.12456Google Scholar
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.2016.08.005Google Scholar
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/10.1037/0022-3514.39.4.578CrossRefGoogle Scholar
Hand, D. J. (2020). Dark data. Princeton, NJ: Princeton University Press.Google 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.2019.05.003CrossRefGoogle ScholarPubMed
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
Hearst, E. (1991). Psychology and nothing. American Scientist, 79(5), 432–443.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.Google 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
Jessen, R. J. (1978). Statistical survey techniques. New York: Wiley.Google Scholar
Kahneman, D. (2011). Thinking, fast and slow. New York: Macmillan.Google 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.37.5.596CrossRefGoogle ScholarPubMed
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.2017.12.001Google Scholar
Lombrozo, T. (2006). The structure and function of explanations. Trends in Cognitive Sciences, 10(10), 464–470.Google Scholar
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/S0140525X10000968Google Scholar
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-010419-051132Google Scholar
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.97.2.185Google 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.2015.00867Google Scholar
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)80055-7Google 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.CrossRefGoogle ScholarPubMed
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/10.1177/1745691612448481Google Scholar
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.2015.07.001Google Scholar
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.12101CrossRefGoogle ScholarPubMed
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/rev0000190Google Scholar

References

Brehmer, B. (1994). The psychology of linear judgement models. Acta Psychologica, 87(2–3), 137–154.Google Scholar
Brunswik, E. (1956). Perception and the representative design of psychological experiments (2nd ed., rev. and enl.). Berkeley: University of California Press.Google Scholar
Collsiöö, A., Juslin, P., & Winman, A. (2023). Is numerical information always beneficial? Verbal and numerical cue-integration in additive and non-additive tasks. Under review.Google Scholar
Cooksey, R. W. (1996). Judgment analysis: Theory, methods, and applications. San Diego: Academic Press.Google Scholar
DeLosh, E. L., Busemeyer, J. R., & McDaniel, M. A. (1997). Extrapolation: The sine qua non for abstraction in function learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(4), 968–986.Google Scholar
Einhorn, H. J., & Hogarth, R. M. (1986). Judging probable cause. Psychological Bulletin, 99(1), 3–19.CrossRefGoogle Scholar
Evans, J. St. B. T. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59(1), 255–278.Google Scholar
Evans, J. St. B. T. (2018). Dual-process theories. In Ball, L. J. & Thompson, V. A. (Eds.), The Routledge international handbook series: The Routledge international handbook of thinking and reasoning (pp. 151–166). London: Routledge/Taylor & Francis.Google Scholar
Evans, J. St. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241.Google Scholar
Hintzman, D. L. (1986). “Schema abstraction” in a multiple-trace memory model. Psychological Review, 93(4), 411–428.Google Scholar
Hoffmann, J. A., von Helversen, B., & Rieskamp, J. (2014). Pillars of judgment: how memory abilities affect performance in rule-based and exemplar-based judgments. Journal of Experimental Psychology: General, 143(6), 2242–2261.Google Scholar
Juslin, P., Karlsson, L., & Olsson, H. (2008). Information integration in multiple cue judgment: A division of labor hypothesis. Cognition, 106(1), 259–298.Google Scholar
Juslin, P., Olsson, H., & Olsson, A. C. (2003). Exemplar effects in categorization and multiple-cue judgment. Journal of Experimental Psychology: General, 132(1), 133–156.Google Scholar
Juslin, P., & Persson, M. (2002). PROBabilities from EXemplars (PROBEX): A “lazy” algorithm for probabilistic inference from generic knowledge. Cognitive Science, 26, 563–607.Google Scholar
Karelaia, N., & Hogarth, R. M. (2008). Determinants of linear judgment: A meta-analysis of lens model studies. Psychological Bulletin, 134(3), 404–426.Google Scholar
Karlsson, L., Juslin, P., & Olsson, H. (2007). Adaptive changes between cue abstraction and exemplar memory in a multiple-cue judgmentf task with continuous cues. Psychonomic Bulletin & Review, 14(6), 1140–1146.Google Scholar
Kruglanski, A. W., & Gigerenzer, G. (2011). Intuitive and deliberate judgments are based on common principles. Psychological Review, 118(1), 97–109CrossRefGoogle ScholarPubMed
Little, J. L., & McDaniel, M. A. (2015). Individual differences in category learning: Memorization versus rule abstraction. Memory & Cognition, 43(2), 283–297.Google Scholar
Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95(4), 492–527.Google Scholar
Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207–238.Google Scholar
Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115(1), 39–57.Google Scholar
Nosofsky, R. M. (2015). An exemplar-model account of feature inference from uncertain categorizations. Journal of Experimental Psychology: Learning, Memory and Cognition, 41, 1929–1941.Google Scholar
Nosofsky, R. M., & Johansen, M. K. (2000). Exemplar-based accounts of “multiple-system” phenomena in perceptual categorization. Psychonomic Bulletin & Review 7(3), 375–402.Google Scholar
Pachur, T., & Olsson, H. (2012). Type of learning task impacts performance and strategy selection in decision making. Cognitive Psychology, 65(2), 207–240.Google Scholar
Platzer, C., & Bröder, A. (2013). When the rule is ruled out: Exemplars and rules in decisions from memory. Journal of Behavioral Decision Making, 26(5), 429–441.Google Scholar
Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.Google Scholar
Schank, R. C. (1982). Dynamic memory: A theory of reminding and learning in computers and people. New York: Cambridge University Press.Google Scholar
Shepard, R. N. (1994) Perceptual-cognitive universals as reflections of the world. Psychonomic Bulletin & Review, 1, 2–28.CrossRefGoogle ScholarPubMed
Sundh, J., Collsiöö, A., Millroth, P., & Juslin, P. (2021) Precise/not precise (PNP): A Brunswikian model that uses judgment error distributions to identify cognitive processes. Psychonomic Bulletin & Review, 28, 351–373.Google Scholar
Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90(4), 293.Google Scholar
Von Helversen, B., Mata, R., & Olsson, H. (2010). Do children profit from looking beyond looks? From similarity-based to cue abstraction processes in multiple-cue judgment. Developmental Psychology, 46(1), 220–229.Google Scholar
Von Helversen, B., & Rieskamp, J. (2009). Models of quantitative estimations: rule-based and exemplar-based processes compared. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 867–889.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×