Giulia Melis

Deception is a central phenomenon in the study of human behaviour, and in forensic and investigative contexts it assumes significance due to its potential consequences. Although the unexpected questions technique has shown promising results, its application to the narration of a false autobiographical event remains underexplored. This study aims to fill this gap using the unexpected questions technique in an investigative interview setting. A total of 60 participants were randomly divided into two groups: the honest group was asked to describe their most recent day trip, while the deceptive group was asked to memorise a narrative describing a day trip. Both groups underwent an audio-recorded interview containing control, expected and unexpected questions. The results showed that both groups exhibited longer response latencies and higher error and inaccuracy rates in response to unexpected questions, but these indices were significantly higher in the deceptive group ( p = .022 for response latency, uncorrected for multiple comparisons; p < .001 for error and inaccuracy rates). The findings suggest that longer response times and higher error and inaccuracy rates during unexpected questions may reflect not only the difficulty of the questions, but also additional cognitive demands faced by deceptive participants. Furthermore, these indicators were used to classify a subject as deceptive or honest using machine-learning algorithms. Within the sample, the model showed excellent cross-validated discriminatory performance (AUC = 0.967), suggesting that these indices may have diagnostic potential worthy of further investigation.

Purpose: Behavioural and ocular markers may provide reliable indicators of lying, although their combined use remains underexplored. This study examined whether eye‐tracking could contribute to the detection of lying using an Induced fake identity (IFI) paradigm, considering the association between pupillary dynamics, eye‐blinks and behavioural indices of lying. Machine learning was also used to assess the predictive value of these measures. Methods: Fifty‐seven healthy adults underwent an IFI task, randomised to lying or truth‐telling conditions. Participants responded to Control, Expected and Unexpected questions while assuming either a fake identity or their true identity, while their ocular (pupil dilation and eye‐blinks) and behavioural (reaction times and errors) responses were recorded. Results: Liars exhibited significantly longer reaction times than truth‐tellers, particularly in response to Unexpected questions, consistent with greater cognitive demands during deception. Similarly, baseline‐corrected pupil responses also differed between groups: whereas truth‐tellers showed reduced pupil dilation during Unexpected questions compared with Control and Expected questions, liars exhibited relatively stable pupil responses across question types. No group differences emerged in eye‐blink rate, nor in errors. Machine‐learning models achieved up to 84% accuracy and identified reaction times to Unexpected questions as the strongest predictor of deception, whereas ocular measures provided limited additional predictive value. Conclusions: Lying is characterised by slower responses to identity‐related Unexpected questions and distinct patterns of pupillary modulation. Although reaction times were the strongest predictor of deception, pupillary responses may provide complementary information about the psychophysiological processes associated with deceptive responding.

Metaverse introduces novel challenges in the domain of criminal behavior, particularly concerning sexual abuse. Its defining characteristics—heightened presence, embodiment, anonymity, and decentralization—amplify risks and differentiate it from traditional digital platforms. Unlike conventional internet environments, Metaverse simulates real-life experiences with immersive realism, intensifying cybercrimes' psychological impact and necessitating a paradigm shift in legal and regulatory approaches. While digital threats such as the dissemination of pornographic material have long been recognized, behaviors like sexual harassment and assault have historically been addressed primarily within physical contexts. However, in Metaverse, the boundaries between virtual and physical harm are increasingly blurred, as illustrated by the first widely reported case of virtual rape in January 2024, involving the avatar of a young girl assaulted by other users in an immersive gaming environment. This paper, through a literature review, critically examines current psychological and legal perspectives on sexual abuse within the Metaverse. By synthesizing recent empirical findings and documented case reports, the results of this work highlight how immersive environments may exacerbate the psychological toll of virtual sexual victimization, hypothesizing the emergence of trauma responses comparable to those associated with physical assaults. Despite the severity of such experience, significant gaps remain in empirical research concerning the psychological consequences of sexual abuse in immersive virtual environments. This underscores the need for further investigation to better understand victim experiences and inform the development of effective therapeutic and protective measures. Moreover, current legal frameworks are ill-equipped to address offenses occurring in the Metaverse, constrained by definitional ambiguities and jurisdictional limitations. This paper identifies critical regulatory gaps and emphasizes the need for legal standards explicitly designed for immersive virtual contexts. By integrating insights from psychology, law, and technology, it advocates interdisciplinary approaches to redefine strategies for safeguarding users and addressing the complex ethical, psychological, and legal challenges posed by Metaverse.

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals’ performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Previous studies with the eye-tracking technology have predominantly tracked eye parameters in response to a single simple stimulus, and have generated interesting - sometimes inconsistent - results in research on deceptive behavior. The present study analyzed visual patterns in response to a complex image, to investigate potential differences in eye fixation between guilty versus innocent, and honest versus dishonest participants.One hundred and sixty participants were assigned to one of four experimental groups, defined by the parameters of honesty (dishonesty) and guilt (innocence), and asked to complete a computer-based task, looking at neutral and target images (i.e., images of the mock crime scene). RealEye software was used to capture participants’ eye movements when viewing the images.The findings revealed significant differences in eye movements between the four experimental groups in the pictures in which the area where the crime took place was clearly visible. Dishonest and guilty participants recorded fewer and shorter fixations in the area of the image where the crime took place than those who entered the crime scene but did not commit the crime. No differences between groups emerged in the visual patterns in response to neutral images, confirming that the number and duration of fixations in response to the target images may be attributed to the experimental condition.