Date of Degree
DRM, memory distortion, semantic association, evidence
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? We tested this possibility using a Deese-Roediger-McDermott (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 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.
Crozier, William E. IV, "Automatically Guilty: Associations Between Evidence and Guilt" (2017). CUNY Academic Works.