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On making the right choice: A meta-analysis and large-scale replication attempt of the unconscious thought advantage
- Mark R. Nieuwenstein, Tjardie Wierenga, Richard D. Morey, Jelte M. Wicherts, Tesse N. Blom, Eric-Jan Wagenmakers, Hedderik van Rijn
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- Journal:
- Judgment and Decision Making / Volume 10 / Issue 1 / January 2015
- Published online by Cambridge University Press:
- 01 January 2023, pp. 1-17
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- Article
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Are difficult decisions best made after a momentary diversion of thought? Previous research addressing this important question has yielded dozens of experiments in which participants were asked to choose the best of several options (e.g., cars or apartments) either after conscious deliberation, or after a momentary diversion of thought induced by an unrelated task. The results of these studies were mixed. Some found that participants who had first performed the unrelated task were more likely to choose the best option, whereas others found no evidence for this so-called unconscious thought advantage (UTA). The current study examined two accounts of this inconsistency in previous findings. According to the reliability account, the UTA does not exist and previous reports of this effect concern nothing but spurious effects obtained with an unreliable paradigm. In contrast, the moderator account proposes that the UTA is a real effect that occurs only when certain conditions are met in the choice task. To test these accounts, we conducted a meta-analysis and a large-scale replication study (N = 399) that met the conditions deemed optimal for replicating the UTA. Consistent with the reliability account, the large-scale replication study yielded no evidence for the UTA, and the meta-analysis showed that previous reports of the UTA were confined to underpowered studies that used relatively small sample sizes. Furthermore, the results of the large-scale study also dispelled the recent suggestion that the UTA might be gender-specific. Accordingly, we conclude that there exists no reliable support for the claim that a momentary diversion of thought leads to better decision making than a period of deliberation.
9 - Bayesian hierarchical models of cognition
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- By Jeffrey N. Rouder, Department of Psychological Sciences, University of Missouri (USA), Richard D. Morey, University of Groningen (The Netherlands), Michael S. Pratte, Department of Psychology, Vanderbilt University (USA)
- Edited by William H. Batchelder, University of California, Irvine, Hans Colonius, Carl V. Ossietzky Universität Oldenburg, Germany, Ehtibar N. Dzhafarov, Purdue University, Indiana, Jay Myung, Ohio State University
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- Book:
- New Handbook of Mathematical Psychology
- Published online:
- 01 December 2016
- Print publication:
- 15 December 2016, pp 504-551
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Summary
Introduction: the need for hierarchical models
Those of us who study human cognition have no easy task. We try to understand how people functionally represent and process information in performing cognitive activities such as vision, perception, memory, language, and decision making. Fortunately, experimental psychology has a rich theoretical tradition, and there is no shortage of insightful theoretical proposals. Also, it has a rich experimental tradition, with a multitude of experimental techniques for isolating purported processes. What it lacks, however, is a rich statistical tradition to link theory to data. At the heart of the field is the difficult task of trying to use data from experiments to inform theory, that is, to understand accurately the relationships within the data and how they provide evidence for or against various theoretical positions.
The difficulty in linking data to theory can be seen in a classic example from Estes (1956). Estes considered two different theories of learning: one in which learning was gradual, and another where learning happened all at once. These two accounts are shown in Figure 9.1A. Because these accounts are so different, adjudicating between them should be trivial: one simply examines the data for either a step function or a gradual change. Yet, in many cases, this task is surprisingly difficult. To see this difficulty, consider the data of Reder and Ritter (1992), who studied the speed up in response times from repeated practice of a mathematics tasks. The data are shown in Figure 9.1B, and the gray lines show the data from individuals. These individual data are highly variable, making it impossible to spot trends. A first-order approach is to simply take the means across individuals at different levels of practice, and these means (points) decrease gradually, seemingly providing support for the gradual theory of learning. Estes, however, noted that this pattern does not necessarily imply that learning is gradual. Instead, learning might be all-at-once, but the time at which different individuals transition may be different. Figure 9.1C shows an example; for demonstration purposes, hypothetical data are shown without noise. If data are generated from the all-at-once model and there is variation in this transition time, then the mean will reflect the proportion of individuals in the unlearned state at a given level of practice.
Contributors
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- By Gregory H. Adkisson, Ozan Akça, Nawar Al-Rawas, John T. Anderson, Richard M. Bednarski, Francesca Bernabè, David G. Bjoraker, Lluis Blanch, Stephan H. Böhm, Edwin A. Bowe, Philip G. Boysen, Justin C. Cahill, Ira M. Cheifetz, David C. Cone, Nancy Craig, Daniel P. Davis, John B. Downs, Ronald Dueck, Jay L. Falk, Roger Fletcher, Michael A. Frakes, Andrea Gabrielli, Thomas J. Gallagher, Geoff Gilmartin, J. S. Gravenstein, Antonino Gullo, Donna Hamel, John W. Huang, Amy V. Isenberg, Michael B. Jaffe, Michael C. K. Khoo, Robert R. Kirby, E. F. Klein, A. Joseph Layon, Umberto Lucangelo, Emilio Maldonado, Paul E. Marik, Alicia E. Meuret, Timothy E. Morey, William Muir, Joseph A. Orr, Mehmet S. Ozcan, Lucía Isabel Passoni, David A. Paulus, Yong G. Peng, Carl W. Peters, George A. Ralls, Adriana G. Scandurra, Peter W. Scherer, Gerd Schmalisch, Adam Seiver, Salvatore Silvestri, Bob Smalhout, Fernando Suarez-Sipmann, Daniel E. Supkis, John Thompson, Patrick Troy, Jonathon D. Truwit, Gerardo Tusman, Joseph Varon, Ajeet G. Vinayak, Kevin R. Ward, Marvin A. Wayne, Charles Weissman, Dafna Willner, Kai Zhao, Christian C. Zuver
- Edited by J. S. Gravenstein, University of Florida, Michael B. Jaffe, Nikolaus Gravenstein, University of Florida, David A. Paulus, University of Florida
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- Book:
- Capnography
- Published online:
- 05 August 2011
- Print publication:
- 17 March 2011, pp ix-xii
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