Sleep and interrogation: does losing sleep impact criminal history disclosure?

STUDY OBJECTIVES

Despite centuries of using sleep deprivation to interrogate, there is virtually no scientific evidence on how sleep shapes behavior within interrogation settings. To evaluate the impact of sleeplessness on participants' behavior during investigative interviews, an experimental study examined the impact of sleep restriction on disclosure of past illegal behavior.

METHODS

Healthy participants from a university community (N = 143) either maintained or curbed their sleep (up to 4 h a night) across 2 days with sleep monitored via actigraphy. They were then asked to disclose past illegal acts and interviewed about them. Next, they were reinterviewed following an example of a detailed memory account (model statement). Disclosures were blindly coded for quantity and quality by two independent raters.

RESULTS

Sleep-restricted individuals reported similar offenses, but less information during their disclosure with slightly less precision. Model statement increased disclosure but did not reduce the inhibiting impact of sleep loss. Mediation analysis confirmed the causal role of sleep as responsible for experimental differences in amount of information, and participants' reports suggested impaired motivation to recall information played a role.

CONCLUSIONS

The findings suggest that even moderate sleep loss can inhibit criminal disclosure during interviews, point to motivational factors as responsible, and suggest investigators should be cautious when interrogating sleepy participants.

Verbal Deception and the Model Statement as a Lie Detection Tool

We have been reliably informed by practitioners that police officers and intelligence officers across the world have started to use the Model Statement lie detection technique. In this article we introduce this technique. We describe why it works, report the empirical evidence that it works, and outline how to use it. Research examining the Model Statement only started recently and more research is required. We give suggestions for future research with the technique. The Model Statement technique is one of many recently developed verbal lie detection methods. We start this article with a short overview of the-in our view- most promising recent developments in verbal lie detection before turning our attention to the Model Statement technique.

Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling

Recently, verbal credibility assessment has been extended to the detection of deceptive intentions, the use of a model statement, and predictive modeling. The current investigation combines these 3 elements to detect deceptive intentions on a large scale. Participants read a model statement and wrote a truthful or deceptive statement about their planned weekend activities (Experiment 1). With the use of linguistic features for machine learning, more than 80% of the participants were classified correctly. Exploratory analyses suggested that liars included more person and location references than truth‐tellers. Experiment 2 examined whether these findings replicated on independent‐sample data. The classification accuracies remained well above chance level but dropped to 63%. Experiment 2 corroborated the finding that liars' statements are richer in location and person references than truth‐tellers' statements. Together, these findings suggest that liars may over‐prepare their statements. Predictive modeling shows promise as an automated veracity assessment approach but needs validation on independent data.