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Dual-System Theories of Decision Making: Analytic Approaches and Empirical Tests

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Dual-System Theories of Decision Making: Analytic Approaches and Empirical Tests Dual-System Theories of Decision Making: Analytic Approaches and Empirical Tests DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Aleksandr Sinayev Graduate Program in Psychology The Ohio State University 2016 Dissertation Committee: Ellen Peters, Adviser Michael DeKay Trisha Van Zandt Copyrighted by Aleksandr Sinayev 2016 Abstract Dual-system models are popular in the study of decision making. They have faced criticisms, especially for being vague and lacking specific predictions. In order to address these criticisms, three categories of dual-system models are reviewed: parallel- competitive (in which intuitive, system 1, and deliberative, system 2, processing happen at the same time and both influence the response), default-interventionist (in which system 1 executes first and then system 2 may or may not override system 1), and interactive (in which both systems process information at the same time, but they are allowed to influence each other in complex back-and-forth interactions). Tests are conducted of the former two categories. Default-interventionist dual-system models predict that individual differences in reflectiveness should be associated with less biased decision making. The Cognitive Reflection Test (CRT) is thought to measure monitoring of system 1 intuitions such that, if cognitive reflection is high enough, intuitive errors will be detected and the problem will be solved. However, CRT items also require numeric ability to be answered correctly and it is unclear how much numeric ability vs. cognitive reflection contribute to better decision making. In two studies, CRT responses were used to calculate Cognitive Reflection and numeric ability; a numeracy scale was also administered. Numeric ability, measured with the CRT or the numeracy scale, accounted for the CRT's ability to predict ii more normative decisions (a subscale of decision-making competence, incentivized measures of impatient and risk-averse choices, and self-reported financial outcomes); Cognitive Reflection contributed no independent predictive power. Results were similar whether the two abilities were modeled (Study 1) or calculated using proportions (Studies 1 and 2). These findings demonstrated that correlations of decision performance with the CRT are insufficient evidence to implicate overriding intuitions, and, therefore, to support default-interventionist theories in the decision-making biases and outcomes we examined. Numeric ability appeared to be the key mechanism instead. After failing to find evidence for a default-interventionist model of decision making, a mathematical parallel-competitive model, first introduced by Mukherjee (2010), was reviewed and tested. This model was expanded in order to improve its fit to choice data. An experiment was conducted, in which participants made risky choices. Half of the participants made choices under heavy cognitive load, and the other half under no cognitive load. The model’s key parameter indicating the use of system 1 vs. system 2 changed in the predicted way, but the model failed to account for important features in the data. The model’s assumed system 1 process was changed to be sensitive to probability, rather than outcome, after which the model was able to account for qualitative patterns of data more effectively and fit the data better, suggesting this is a better representation of system 1. In the final chapter, I conclude that some dual-system models are specific enough to be mathematically identifiable and to make a number of very specific predictions. I then iii discuss the possibility of generalizing the analytic approaches I employed to other decision problems and the limitations of these approaches. iv Acknowledgments I would like to thank my adviser, Ellen Peters, for encouraging me throughout my studies and for always being in my corner. I have always left meetings feeling like no challenge is insurmountable and have always felt she is on my side. I would like to thank my whole committee for being patient with me as deadlines were moved and for providing endless crucial suggestions. I would like to thank my parents and brother for providing an emotional safety net during trying times, providing direction during times of confusion and for always believing I am worthy. v Vita May 2007 .......................................................Brooklyn Technical High School May 2011 .......................................................B.A. Psychology, Binghamton University May 2011 .......................................................B.A. Mathematics, Binghamton University May 2013 .......................................................M.A. Psychology, The Ohio State University Sept 2011 – Aug 2012 ...................................Fellow, Department of Psychology, The Ohio State University Sept 2012 - to present ...................................Graduate Teaching Associate, Department of Psychology, The Ohio State University Sept 2013 - to Aug 2014 ...............................Graduate Research Assistant, Department of Psychology, The Ohio State University Publications Sinayev, A., & Peters, E. (2015). Cognitive reflection vs. calculation in decision making. Frontiers in psychology, 6(532). doi: 10.3389/fpsyg.2015.00532. vi Sinayev, A., Peters, E., Tusler, M., & Fraenkel, L. (2015). Presenting Numeric Information with Percentages and Descriptive Risk Labels A Randomized Trial. Medical Decision Making, 35(8), 937-947. Fields of Study Major Field: Psychology vii Table of Contents Abstract ............................................................................................................................... ii Acknowledgments............................................................................................................... v Vita ..................................................................................................................................... vi List of Tables .................................................................................................................... xii List of Figures .................................................................................................................. xiii Chapter 1: Introduction ....................................................................................................... 1 Significance of dual-system models ................................................................................ 2 Review of dual-system models in reasoning and decision making ................................. 3 Types of models........................................................................................................... 4 Importance of working memory in dual-system models ........................................... 25 Alternatives to and criticisms of dual-system models ................................................... 26 Review of single continuum models ......................................................................... 27 Review of multiple systems models in relation to dual-system models .................... 33 Other criticisms of dual-system models .................................................................... 39 Summary ....................................................................................................................... 40 Transition to Chapter 2 .................................................................................................. 41 viii Chapter 2: Cognitive Reflection vs Numeric ability in predicting decision biases and outcomes (Sinayev & Peters, 2015) .................................................................................. 43 The Cognitive Reflection Hypothesis ........................................................................ 43 Dual-System Explanation .......................................................................................... 45 The Numeracy Hypothesis ........................................................................................ 47 Modeling Cognitive Reflection and Numeric Ability ............................................... 48 Do Cognitive Reflection and Numeracy Both Predict Good Decision Making? ...... 50 Study 1........................................................................................................................... 52 Method ....................................................................................................................... 53 Results ....................................................................................................................... 58 Discussion .................................................................................................................. 66 Study 2........................................................................................................................... 68 Method ....................................................................................................................... 70 Results ....................................................................................................................... 76 Discussion .................................................................................................................. 83 General Discussion ........................................................................................................ 85 Transition to Chapter 3 .................................................................................................
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