Implicit measures of beliefs
Authors: Cummins Jamie 
Published: December 2020

The assessment of the thoughts and evaluations of human beings is a central feature of modern psychological science. Further to this, many researchers are specifically interested in the automatic thoughts and evaluations of individuals. To assess automatic thoughts and evaluations, researchers typically use a range of measurement procedures whose outcomes are broadly referred to as implicit measures (De Houwer, 2006). Historically, researchers have believed that only associations between stimuli operate at the automatic level. As a consequence, the procedures of implicit measures have tended to measure only associations between stimuli (i.e., without specifying the relations between stimuli). However, growing evidence now suggests that beliefs about how stimuli are related (i.e., implicit beliefs) can also be measured at the automatic level (for a recent review, see De Houwer et al., 2020). This has led to an issue for the measurement of automatic thoughts and evaluations: since most procedures of implicit measures have been developed from an associative perspective, these procedures are unable to accommodate relational information into their stimuli. As a result of the limitations of implicit measures developed from an associative perspective, researchers have begun developing relational implicit measures (i.e., implicit measures of beliefs) which can accommodate relational information. To date, four relational implicit measures have been developed: the Implicit Relational Assessment Procedure (IRAP; Power et al., 2009), the Relational Responding Task (RRT; De Houwer et al., 2015), the Propositional Evaluation Paradigm (PEP; Müller &amp; Rothermund, 2019), and the Autobiographical Implicit Association Test (aIAT; Sartori et al., 2008). Although the above four measures represent initial progress towards developing valid relational implicit measures, there is much work yet to be done: both in terms of developing new relational implicit measures, and in terms of validating existing measures (relative to whether they are valid measures of their construct of interest, whether they capture relational information, and whether they reflect automatic responding). These were precisely the goals of the current project: to develop and validate new and existing relational implicit measures. Chapter 1 begins with a general introduction to the field of implicit measures, and the three ways in which researchers commonly define the “implicit” term. With the historical context of these varying definitions, the Chapter explores why the need for relational implicit measures has only recently emerged, and describes the relational implicit measures which have been developed to date. Chapters 2, 3, and 4 detail the development of three new relational implicit measures: the Truth Misattribution Procedure (TMP), the Propositional Concealed Information Test (pCIT), and the Mousetracking Propositional Evaluation Paradigm (MT-PEP), respectively. Chapters 5 and 6 consist of comparative investigations of multiple relational implicit measures. Specifically, Chapter 5 compares the RRT, aIAT, and pCIT (as well as the standard IAT) in the context of drinking self-identity and the prediction of problematic drinking outcomes, whereas Chapter 6 compares the RRT, aIAT, and MT-PEP (as well as the standard IAT) in the context of sensitivity to experimentally-manipulated relational information. Finally, Chapter 7 provides a summary and synthesis of the results of the empirical chapters. In addition, the Chapter offers reflections on the use of implicit measures more broadly, critiquing the ultimate goals of using such measures, as well as reflecting on why the behaviour of scientists in the field of implicit measures has tended to be relatively unreceptive to recommendations for improving the measures.