1,720,994 research outputs found
Deception Detection With Behavioral Methods: The Autobiographical Implicit Association Test, Concealed Information Test-Reaction Time, Mouse Dynamics, and Keystroke Dynamics
No full textIn this chapter, we present a review of the behavioral lie detection tools currently available in the literature. Behavioral lie detection methods are based on the assumptions that being deceptive is cognitively more complex than telling the truth and that this greater complexity is reflected in an alteration of the subject's behavior during a task. Thus, these techniques are mainly based on analyzing the accuracy and response latency when the subject responds to questions related to the object of the investigation. They can be classified into two main categories, depending on whether they use the true memory among the response alternatives. We depict the main techniques for each category, focusing on the benefits and drawbacks of each tool. We give particular attention to new lie detection technologies that exploit human-computer interactions for behavioral analysis, and their applications. Moreover, we present new paradigms and novel approaches for increasing liars' cognitive loads. Finally, we discuss methodological observations regarding the application of machine learning in lie detection research
Which methods for the assessment of Large Language Models (LLM)? Comments on the usage of a neuropsychological approach and the development of psychometrics of LLMs
No full textMouse tracking: a tool to detect fake good and fake bad behaviours
No full textThe rate of suspected simulation in the clinical population is estimated to be around 20%. This data underlines important consequences both in terms of health care cost and in terms of absences from work, as well as it introduces a strong issue in the legal context for the assessment of the psychological damage and the evaluation of mental insanity. Similarly, it is estimated a dissimulation rate of 30% in job applicants and parents who are involved in child custody litigations. Presently, few instruments offer clinical support in the identification of psychiatric disorders simulation and dissimulation. This contribute will introduce a new method for the automatic detection of fake bad and fake good behaviours that exceed the limitations of the currently available instruments. The method proposed is based on kinematic analysis of mouse movements while the patient is engaged in a double choice computerized questionnaire that investigates the presence of certain psychological symptoms. Recent studies proved that the kinematics of hand movements may provide a reliable track of the mental processes underlying a task and can be effective in detecting the processes of a lie production. Based on this scientific evidence, we analysed the response trajectories of groups of subjects instructed to simulate or dissimulate a psychological disorder (depression, anxiety disorder, personality disorder) while they are answering to questions about their symptoms. The analysis of the kinematic parameters showed a statistically significant difference between fakers and control groups, both in the shape of the trajectory along time and in the response time
Fear of infection and the common good: Covid-19 and the first italian lockdown
Full text linkIn the first quarter of 2020, Italy became one of the earliest hotspots of COVID-19 infection, and the government imposed a lockdown. During the lockdown, an online survey of 2053 adults was conducted that asked about health behaviors and about the psychological and overall impact of COVID-19. The present study is a secondary analysis of that data. We hypothesized that self-control, higher socio-economic status, existing health conditions, and fear of infection were all inversely related to actions (or intentions) that violated the lockdown (i.e., infractions). Using partial least squares structural equation modeling (PLS-SEM), we found that only the fear of infection significantly dissuaded people from violating lockdown rules. Since it is not practical or ethical to sow a fear of infection, our study indicates that enacting rules and enforcing them firmly and fairly are important tools for containing the infection. This may become more important as vaccines become more widely available and people lose their fear of infection
Detection of malingering in psychic damage ascertainment
No full textMalingering is the intentional feigning or exaggeration of physical or psychological symptoms. Since the beginning of 1900 malingering detection has been one of the main challenges in medico-legal practice and in particular in psychiatric and cognitive assessment, as behavioral symptoms are very easy to produce, so that the need for specific tools and strategies for malingering detection is crucial. Although several tools and strategies are available, conclusions are often derived from mere subjective impressions and in many cases they lead to misclassifications. Here we present a non-exhaustive review of strategies for the detection of malingering, starting from the logic underlying a qualitative analysis of symptoms, to validated tools specifically designed to detect attempts at simulating or exaggerating psychopathological, psychiatric or cognitive diseases. Finally, we describe two recent approaches to the malingering detection problem. These approaches are grounded on the analysis of the reaction-times and on the dynamic analysis of kinematic features of mouse trajectories while an examinee is answering to double-choice questions
Do attitudes towards technology mediate engagement with digital mental health interventions?
No full textBackground: Digital Mental Health Interventions (DMHIs), including mHealth apps and serious games, promise to improve mental health outcomes, particularly among vulnerable populations, who have logistic, time and economic difficulties to reach health services or hospitals. However, high dropout rates remain a critical barrier to DMHIs’ success. Previous scientific studies suggest that users’ characteristics and predispositions significantly impact their engagement with DMHIs. Investigating implicit and explicit attitudes towards these technologies could reveal crucial insights into factors influencing user retention and motivation, opening the way for more effective and sustainable mental health solutions.
Methods: The present research will focus on mediators of engagement with DMHIs. This work adopts a novel approach, comparing users’ explicit and implicit attitudes towards mental health interventions before and after their use. Implicit attitudes, measured through techniques such as Implicit Association Tests (IAT), will be correlated with explicit self-reports (e.g. the TWente Engagement with eHealth Technologies Scale, TWEETS) and objective engagement data collected pre-, during and post-intervention usage. Objective metrics, including app usage frequency and task completion rates, will be paired with subjective feedback to understand user engagement and attitude towards technological tools for mental health comprehensively.
Future Findings: The study will identify key motivational and attitudinal factors influencing engagement with DMHIs. Preliminary analyses of the literature suggest that implicit attitudes could serve as significant predictors of user retention, critically complementing explicit measures of motivation and satisfaction with DMHIs for mental health. These findings will offer valuable insights into tailoring DMHIs to better align with users’ needs and expectations, ultimately reducing dropout rates and enhancing the effectiveness of digital mental health technologies.
Discussion: Understanding the mediators of engagement with DMHIs is crucial for their successful implementation. This study aims to test these assumptions, thereby aiding future research in addressing outcomes for populations with unique vulnerabilities and stressors. This research aims to address the current knowledge gap surrounding implicit attitudes' role in predicting user engagement with DMHIs. By examining how biases and preconceptions affect interaction, this approach may reveal how initial attitudes influence long-term commitment to these tools. The outcomes are expected to advance the field of digital mental health by providing actionable insights into improving user retention and optimizing interventions design for general and specific vulnerable populations, they will also underscore the importance of integrating psychological principles into the development of health technologies, contributing to their broader adoption and impact
Fit-to-drive after stroke and traumatic brain injury: A combination of performances in cognitive tests and driving simulator
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