The Truth-Default, Diagnostic Utility, and the Value of Contextual Knowledge in Deceptive Interactions

This dissertation describes and reports an experimental study examining the predictions of truth-default theory (TDT). TDT is an expansive, modular theory of deception detection which explains the processes by which people abandon the truth-default state and navigate judgments of message veracity in conversations. The first goal of this study was to test TDT’s seventh proposition, which explains how communicators abandon the truth-default state through trigger events (i.e., actions which prompt communicators to be uncertain about the accuracy of the message) such as decreased sender believability and message coherence. The second goal of this study was to test the predictions of TDT’s ninth hypothesis, which explains how messages are judged as deceptive through the same triggers that prompt the abandonment of the truth-default state. Finally, this study tests TDT’s twelfth proposition, which claims that questioning which produces diagnostic utility will ultimately result in higher judgment accuracy than relying on perceptions of sender believability or message coherence when judging the veracity of messages. The proposed study sampled 361 participants from Amazon’s Mechanical Turk (MTurk), who watched a sequence of two videos and completed an anonymous online survey. None of the study hypotheses concerning TDT’s seventh, ninth, and twelfth propositions were supported, but the findings from this dissertation did contribute to the deception literature with regard to the prevalence of the truth-default and truth bias. Theoretical and practical implications of the results are discussed, as well as a review of the limitations influencing the interpretation of this dissertation’s findings.