Automatically Guilty: Associations between Evidence and Guilt

AUTOMATICALLY GUILTY: ASSOCIATIONS BETWEEN EVIDENCE AND GUILT by WILLIAM EDWARD CROZIER Advisor: Deryn Strange Both real-life cases and laboratory research demonstrate that confession evidence is very convincing—even when it should not be. Could this be due to an automatic association between a confession and guilt? We1 tested this possibility using a Deese-RoedigerMcDermott (DRM) list, which measures automatic associations by presenting participants with a list of words that are thematically related but, importantly, lack the word describing the theme (“critical lure”). When the association between the list words and the theme is sufficiently strong, participants incorrectly report seeing the critical lure. We hypothesized that participants would show more false recall for seeing “guilty” on a “guilty”-themed DRM list when the list included evidence that is automatically associated with guilt, such as “confession” and “DNA. ” Although our previous research on this topic found no significant effects, we addressed limitations of that research in three studies using an Amazon MechanicalTurk sample. Our first study addressed a possible ceiling effect by decreasing the associative strength of our “guilty” list. Our second study increased external validity by 1 Because research is a collaborative endeavor, and this project would not have been possible without my advisor’s support and contribution, I have chosen to use the pronoun “we” throughout my dissertation. I think it is the most accurate characterization of this work. v AUTOMATICALLY GUILTY presenting our DRM List as a DRM Story—a narrative format that provides context for the list words. Our third study investigated the effects of priming evidence quality on the association to guilty. Overall, we found little support for our hypotheses. Across all three studies, we did not detect any effects of the evidence type (Study 1, 2, and 3) or prime type (Study 3). We did, however, find several interesting trends in the data. We discuss explanations for the lack of significant findings and address directions for future research. Specifically, adapting this paradigm for other research applications and to increase our understanding of the memorial effects of the “guilty” DRM list. vi AUTOMATICALLY GUILTY Acknowledgements First and foremost, I offer my eternal gratitude to my advisor, Dr. Deryn Strange. Without her constant patience, encouragement, support, and rapid email responding, I honestly believe this project would not have been possible. I have her to thank for the completion of my degree, as well as my insatiable desire to learn more about the world. Second, I am similarly grateful to my partner, Lindsey Cartner, for her unwavering support during frustration. I can never overstate how essential she has been to the development of this project, every paper I’ve written, and my growth as scientist. Third, my utmost appreciation to the members of my committee: Professors Saul Kassin, Charles Stone, Christian Meissner, and Stephen Dewhurst. Their genuine interest, critical questions, and enthusiastic suggestions in this project have improved this project and my skill as an experimental psychologist. I also owe a debt to Dr. Timothy Luke, who proved a welcome resource for venting frustration, celebrating victories, and understanding the fundamentals of research. Whether in practical discussions about statistics or less-stressful conversations about unrelated research projects, his friendship and mentorship have been invaluable. I would like to thank Ella Merriweather, Gabriella Rico, Caitlin McCauley, and Mashy Raul for their help in coding data and other miscellaneous contributions to the project. Finally, I am appreciative to the Forensic Psychology Research Institute and, specifically Dr. Widom, for financially supporting my dissertation. The Dissertation Improvement Grant, and revisions required to receive the grant, both improved this project, and made it possible. vii AUTOMATICALLY GUILTY TABLE OF CONTENTS CHAPTER 1: THE CONNECTION BETWEEN EVIDENCE AND GUILT . 1 CHAPTER 2: TESTING FOR AUTOMATIC ASSOCIATIONS . 5 The Implicit Associations Test . 5 The Deese-Roediger-McDermott Paradigm . 7 Activation-Monitoring Theory . 8 Using the DRM to Measure Automatic Semantic Associations . 10 CHAPTER 3: DEVELOPING AND TESTING A “GUILTY” DRM LIST . 14 CHAPTER 4: PROPOSED RESEARCH PLAN . 19 Data Analysis Approach . 21 CHAPTER 5: STUDY 1 – THE 7-WORD LIST . 23 CHAPTER 6: STUDY 1A . 24 Design . 24 Participants . 24 Materials . 25 Procedure . 25 Hypotheses . 26 Results . 27 Discussion . 29 CHAPTER 7: STUDY 1B . 31 Design . 31 Participants . 31 Materials . 32 Procedure . 32 Hypotheses . 33 Results . 33 Discussion . 37 CHAPTER 8: STUDY 2 – THE DRM STORY FORMAT . 40 CHAPTER 9: STUDY 2A . 43 Design . 43 Participants . 43 Materials . 43 Procedure . 45 Results . 45 Discussion . 46 viii AUTOMATICALLY GUILTY CHAPTER 10: STUDY 2B . 47 Design . 47 Participants . 47 Materials . 48 Procedure .