Reconstructing individual responses to direct questions: a new method for reconstructing malingered responses.

Introduction: The false consensus effect consists of an overestimation of how common a subject opinion is among other people. This research demonstrates that individual endorsement of questions may be predicted by estimating peers' responses to the same question. Moreover, we aim to demonstrate how this prediction can be used to reconstruct the individual's response to a single item as well as the overall response to all of the items, making the technique suitable and effective for malingering detection. Method: We have validated the procedure of reconstructing individual responses from peers' estimation in two separate studies, one addressing anxiety-related questions and the other to the Dark Triad. The questionnaires, adapted to our scopes, were submitted to the groups of participants for a total of 187 subjects across both studies. Machine learning models were used to estimate the results. Results: According to the results, individual responses to a single question requiring a "yes" or "no" response are predicted with 70-80% accuracy. The overall participant-predicted score on all questions (total test score) is predicted with a correlation of 0.7-0.77 with actual results. Discussion: The application of the false consensus effect format is a promising procedure for reconstructing truthful responses in forensic settings when the respondent is highly likely to alter his true (genuine) response and true responses to the tests are missing.

"I'm Too Old for That": The Role of Ageism and Sexual Dysfunctional Beliefs in Sexual Health in a Sample of Heterosexual and LGB Older Adults: A Pilot Study.

The role of ageism (i.e., discrimination against individuals or groups on the basis of their age), in particular toward the sexuality of the elderly, remains, to date, an overlooked issue. A few studies have suggested that ageistic stereotypes can negatively affect older adults' sexual health. No data are available, in particular, about differences among heterosexual and LGB (lesbian, gay, and bisexual) populations. The present study aimed to investigate differences in perceived ageism and related dysfunctional beliefs in a sample of heterosexual (n = 104) and LGB (n = 103) older adults (aged 55 or older; mean age 66.5) and their impact on sexual health and satisfaction. LGB individuals reported higher rates of masturbation and sexual intercourse and an increased quality of sexual activity as compared to heterosexuals. In addition, no differences between the groups emerged as regards perceived ageism and dysfunctional beliefs toward ageing. In conclusion, LGB individuals perceived more ageism toward sexuality than their counterparts; however, heterosexuals were more likely to have dysfunctional beliefs toward sexuality in ageing. The study findings highlight the significance of examining sexual orientation to understand experiences of sexuality in ageing of the growing older population. Renewed socio-educational efforts based on these data are clearly needed.

Psychological Distress Related to the COVID-19 Pandemic: The Protective Role of Hope.

The COVID-19 outbreak and the worldwide lockdown measures had an impact on the global mental health and psychological well-being of the general population. Several studies attempted to investigate the protective and risk factors for psychological distress related to the pandemic. However, to date, little is known about the role of hope in this context. The aim of this study was to determine the relationship between hope and psychological distress related to the COVID-19 outbreak in the general population. The sample consisted of 504 Pakistani people who completed cross-sectionally the COVID-19 Peritraumatic Distress Index (CPDI) and the Adult Hope Scale (AHS). Bivariate Pearson correlation analysis was run to measure the relationship between hope and psychological distress; hierarchical regression analysis was run to investigate the association between demographics and hope with psychological distress. Higher levels of hope predicted lower levels of psychological distress. Being female, being older, lower level of education, urban residence, being married and living in nuclear family systems were associated with higher levels of psychological distress. The study highlights the protective role of hope on psychological distress related to COVID-19, contributing to knowledge on factors promoting positive mental health during emergency times and providing useful information for implementing effective public health policies and programmes.

Machine learning item selection for short scale construction: A proof-of-concept using the SIMS.

ObjectiveThis proof-of-concept paper provides evidence to support machine learning (ML) as a valid alternative to traditional psychometric techniques in the development of short forms of longer parent psychological tests. ML comprises a variety of feature selection techniques that can be efficiently applied to identify the set of items that best replicates the characteristics of the original test. MethodsIn the present study, we integrated a dataset of 329 participants from published and unpublished datasets used in previous research on the Structured Inventory of Malingered Symptomatology (SIMS) to develop a short version of the scale. The SIMS is a multi-axial self-report questionnaire and a highly efficient psychometric measure of symptom validity, which is frequently applied in forensic settings. Results State-of-the-art ML item selection techniques achieved a 72% reduction in length while capturing 92% of the variance of the original SIMS. The new SIMS short form now consists of 21 items. ConclusionsThe results suggest that the proposed ML-based item selection technique represents a promising alternative to standard psychometric correlation-based methods (i.e. item selection, item response theory), especially when selection techniques (e.g. wrapper) are employed that evaluate global, rather than local, item value.

Fear of Infection and the Common Good: COVID-19 and the First Italian Lockdown.

In 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.

Cigarette Smoking and Dating App Use: Findings from a Survey in a Sample of Adults in Italy.

Existing studies in the literature indicate an association between the use of dating apps and substance-related behaviours (i.e., alcohol consumption, drug consumption). However, to date, no studies investigated the relation between dating app use and smoking. This study aims to explore this association. A total of 1278 respondents completed an online ad hoc questionnaire assessing demographics, smoking habits, dating app use, motivations for using dating apps. Multiple logistic regression analyses were run to investigate the relation between demographics and dating apps use on tobacco consumption. Being active user was significantly associated with being smoker, light daily and moderate-to-heavy smoker. Among users, using apps with the motive of searching for friends accounted for lower odds of smoking, light daily smoking and moderate-to-heavy smoking. However, heavy dating app users were less likely to smoke, to be light daily smokers and to be moderate-to-heavy smokers. The study indicates an association between using the apps and smoking, suggesting that motives for using the apps and intensity of use may moderate this association.

"What Are You Looking For?" Investigating the Association Between Dating App Use and Sexual Risk Behaviors.

INTRODUCTION: Literature on the association between dating app use and sexual risk behaviors is still scant and inconclusive. AIM: To investigate the association between dating app use and sexual risk behaviors, considering the role of motives for using them. METHODS: 1,278 Italian respondents completed an online questionnaire assessing demographics, motives and patterns of dating app use, sexual behaviors and sexually transmitted infections (STIs) diagnoses. One-way ANOVA and Chi-squared analyses were used to investigate differences among the three subsamples (active vs former vs non-users). Multiple linear and logistic regression analyses were run to investigate the role of demographics, motives and patterns of dating app use on sexual risk taking and sexual health. MAIN OUTCOME MEASURES: Number of protected and unprotected full sexual partners in the last year; frequency of hook-ups in the last year; STIs lifetime. RESULTS: Active users, even more than former app users, were more likely to report risky behaviors and STI diagnoses than non-users (χ2 = 26.37, P < .001). Installing the apps to find friends or romantic partners was associated with less protected (find friends B = -0.364, P = .015; find romantic partners B = -0.300, P = .006) and unprotected (find friends B = -0.346, P = .016; find romantic partners B = -0.360, P < .001) sexual intercourses. Installing the apps to find sexual partners predicted higher odds of unprotected sexual activity (B = 0.193, P = .048), hook-ups (B = 0.496, P < .001) and STIs diagnoses (OR = 2.835, P = .025). Accessing apps more frequently and more years of usage was associated with reporting risky sexual behaviors and STI diagnoses among active users (app access frequency OR = 1.461, P = .003; usage years OR = 1.089, P = .013). CONCLUSION: Installing the apps to search for sexual partners, using them at length since first installation and accessing them frequently are significant factors in influencing the association between dating app use and sexual risk behaviors. Flesia L, Fietta V, Foresta C, Monaro M. "What Are You Looking For?" Investigating the Association Between Dating App Use and Sexual Risk Behaviors. Sex Med 2021;9:100405.

The detection of malingering in whiplash-related injuries: a targeted literature review of the available strategies.

OBJECTIVE: The present review is intended to provide an up-to-date overview of the strategies available to detect malingered symptoms following whiplash. Whiplash-associated disorders (WADs) represent the most common traffic injuries, having a major impact on economic and healthcare systems worldwide. Heterogeneous symptoms that may arise following whiplash injuries are difficult to objectify and are normally determined based on self-reported complaints. These elements, together with the litigation context, make fraudulent claims particularly likely. Crucially, at present, there is no clear evidence of the instruments available to detect malingered WADs. METHODS: We conducted a targeted literature review of the methodologies adopted to detect malingered WADs. Relevant studies were identified via Medline (PubMed) and Scopus databases published up to September 2020. RESULTS: Twenty-two methodologies are included in the review, grouped into biomechanical techniques, clinical tools applied to forensic settings, and cognitive-based lie detection techniques. Strengths and weaknesses of each methodology are presented, and future directions are discussed. CONCLUSIONS: Despite the variety of techniques that have been developed to identify malingering in forensic contexts, the present work highlights the current lack of rigorous methodologies for the assessment of WADs that take into account both the heterogeneous nature of the syndrome and the possibility of malingering. We conclude that it is pivotal to promote awareness about the presence of malingering in whiplash cases and highlight the need for novel, high-quality research in this field, with the potential to contribute to the development of standardised procedures for the evaluation of WADs and the detection of malingering.

A model to differentiate WAD patients and people with abnormal pain behaviour based on biomechanical and self-reported tests.

The prevalence of malingering among individuals presenting whiplash-related symptoms is significant and leads to a huge economic loss due to fraudulent injury claims. Various strategies have been proposed to detect malingering and symptoms exaggeration. However, most of them have been not consistently validated and tested to determine their accuracy in detecting feigned whiplash. This study merges two different approaches to detect whiplash malingering (the mechanical approach and the qualitative analysis of the symptomatology) to obtain a malingering detection model based on a wider range of indices, both biomechanical and self-reported. A sample of 46 malingerers and 59 genuine clinical patients was tested using a kinematic test and a self-report questionnaire asking about the presence of rare and impossible symptoms. The collected measures were used to train and validate a linear discriminant analysis (LDA) classification model. Results showed that malingerers were discriminated from genuine clinical patients based on a greater proportion of rare symptoms vs. possible self-reported symptoms and slower but more repeatable neck motions in the biomechanical test. The fivefold cross-validation of the LDA model yielded an area under the curve (AUC) of 0.84, with a sensitivity of 77.8% and a specificity of 84.7%.

Idiopathic and acquired pedophilia as two distinct disorders: an insight from neuroimaging.

Pedophilia is a disorder of public concern because of its association with child sexual offense and recidivism. Previous neuroimaging studies of potential brain abnormalities underlying pedophilic behavior, either in idiopathic or acquired (i.e., emerging following brain damages) pedophilia, led to inconsistent results. This study sought to explore the neural underpinnings of pedophilic behavior and to determine the extent to which brain alterations may be related to distinct psychopathological features in pedophilia. To this aim, we run a coordinate based meta-analysis on previously published papers reporting whole brain analysis and a lesion network analysis, using brain lesions as seeds in a resting state connectivity analysis. The behavioral profiling approach was applied to link identified regions with the corresponding psychological processes. While no consistent neuroanatomical alterations were identified in idiopathic pedophilia, the current results support that all the lesions causing acquired pedophilia are localized within a shared resting state network that included posterior midlines structures, right inferior temporal gyrus and bilateral orbitofrontal cortex. These regions are associated with action inhibition and social cognition, abilities that are consistently and severely impaired in acquired pedophiles. This study suggests that idiopathic and acquired pedophilia may be two distinct disorders, in line with their distinctive clinical features, including age of onset, reversibility and modus operandi. Understanding the neurobiological underpinnings of pedophilic behavior may contribute to a more comprehensive characterization of these individuals on a clinical ground, a pivotal step forward for the development of more efficient therapeutic rehabilitation strategies.

Detecting faking-good response style in personality questionnaires with four choice alternatives.

Deliberate attempts to portray oneself in an unrealistic manner are commonly encountered in the administration of personality questionnaires. The main aim of the present study was to explore whether mouse tracking temporal indicators and machine learning models could improve the detection of subjects implementing a faking-good response style when answering personality inventories with four choice alternatives, with and without time pressure. A total of 120 volunteers were randomly assigned to one of four experimental groups and asked to respond to the Virtuous Responding (VR) validity scale of the PPI-R and the Positive Impression Management (PIM) validity scale of the PAI via a computer mouse. A mixed design was implemented, and predictive models were calculated. The results showed that, on the PIM scale, faking-good participants were significantly slower in responding than honest respondents. Relative to VR items, PIM items are shorter in length and feature no negations. Accordingly, the PIM scale was found to be more sensitive in distinguishing between honest and faking-good respondents, demonstrating high classification accuracy (80-83%).

The detection of faked identity using unexpected questions and choice reaction times.

The identification of faked identities, especially within the Internet environment, still remains a challenging issue both for companies and researchers. Recently, however, latency-based lie detection techniques have been developed to evaluate whether the respondent is the real owner of a certain identity. Among the paradigms applied to this purpose, the technique of asking unexpected questions has proved to be useful to differentiate liars from truth-tellers. The aim of the present study was to assess whether a choice reaction times (RT) paradigm, combined with the unexpected question technique, could efficiently detect identity liars. Results demonstrate that the most informative feature in distinguishing liars from truth-tellers is the Inverse Efficiency Score (IES, an index that combines speed and accuracy) to unexpected questions. Moreover, to focus on the predictive power of the technique, machine-learning models were trained and tested, obtaining an out-of-sample classification accuracy of 90%. Overall, these findings indicate that it is possible to detect liars declaring faked identities by asking unexpected questions and measuring RTs and errors, with an accuracy comparable to that of well-established latency-based techniques, such as mouse and keystroke dynamics recording.

Interrogative suggestibility in the elderly.

Interrogative suggestibility (IS) describes the extent to which an individual behavioral response is affected by messages communicated during formal questioning within a closed social interaction. The present study aimed at improving knowledge about IS in the elderly (aged 65 years and older), in particular about its association with both emotive/affective and cognitive variables. The sample (N = 172) was divided into three groups on the basis of age: late adult (aged 55-64, N = 59), young elderly (aged 65-74, N = 63), and elderly (aged 75 and older, N = 50). Cognitive (i.e., Kaufman Brief Intelligence Test-2, Rey Auditory Verbal Learning Test), emotive/affective (i.e., Rosenberg Self-Esteem Scale, Marlowe-Crowne Social Desirability Scale, Penn State Worry Questionnaire) and suggestibility measures (i.e., Gudjonsson Suggestibility Scale-2) were administered. In order to identify differences and associations between groups in IS, cognitive and emotive/affective variables, ANOVAs tests and Pearson's correlations were run. Furthermore, moderation analyses and hierarchical regression were set to determine whether age, cognitive and emotive/affective variables predicted IS components (i.e., Yield and Shift). Finally, machine learning models were developed to highlight the best strategy for classifying elderly subjects with high suggestibility. The results corroborated the significant link between IS and age, showing that elderly participants had the worst performance on all suggestibility indexes. Age was also the most important predictor of both Yield and Shift. Results also confirmed the important role of non-verbal intelligence and memory impairment in explaining IS dimensions, showing that these associations were stronger in young elderly and elderly groups. Implications about interrogative procedures with older adults were discussed.

A 2-Month Follow-Up Study of Psychological Distress among Italian People during the COVID-19 Lockdown.

The spread of coronavirus disease 2019 (COVID-19) has called for unprecedented measures, including a national lockdown in Italy. The present study aimed at identifying psychological changes (e.g., changes in depression, stress, and anxiety levels) among the Italian public during the lockdown period, in addition to factors associated with these changes. An online follow-up survey was administered to 439 participants (original sample = 2766), between 28 April and 3 May 2020. A paired sample t-test tested for differences in stress, anxiety, and depression over the period. Multivariate regression models examined associations between sociodemographic variables, personality traits, coping strategies, depression, and stress. Results showed an increase in stress and depression over the lockdown, but not anxiety. Negative affect and detachment were associated with higher levels of depression and stress. Higher levels of depression at the start of the lockdown, as well as fewer coping strategies and childlessness, were associated with increased depression at follow-up, whereas higher levels of stress at the start of the lockdown and younger age were associated with higher stress at follow-up. These results may help us to identify persons at greater risk of suffering from psychological distress as a result lockdown conditions, and inform psychological interventions targeting post-traumatic symptoms.

How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms.

In the wake of the sudden spread of COVID-19, a large amount of the Italian population practiced incongruous behaviors with the protective health measures. The present study aimed at examining psychological and psychosocial variables that could predict behavioral compliance. An online survey was administered from 18-22 March 2020 to 2766 participants. Paired sample t-tests were run to compare efficacy perception with behavioral compliance. Mediation and moderated mediation models were constructed to explore the association between perceived efficacy and compliance, mediated by self-efficacy and moderated by risk perception and civic attitudes. Machine learning algorithms were trained to predict which individuals would be more likely to comply with protective measures. Results indicated significantly lower scores in behavioral compliance than efficacy perception. Risk perception and civic attitudes as moderators rendered the mediating effect of self-efficacy insignificant. Perceived efficacy on the adoption of recommended behaviors varied in accordance with risk perception and civic engagement. The 14 collected variables, entered as predictors in machine learning models, produced an ROC area in the range of 0.82-0.91 classifying individuals as high versus low compliance. Overall, these findings could be helpful in guiding age-tailored information/advertising campaigns in countries affected by COVID-19 and directing further research on behavioral compliance.

Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models.

The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on people's daily lives, with strong implications for stress levels due to the threat of contagion and restrictions to freedom. Given the link between high stress levels and adverse physical and mental consequences, the COVID-19 pandemic is certainly a global public health issue. In the present study, we assessed the effect of the pandemic on stress levels in N = 2053 Italian adults, and characterized more vulnerable individuals on the basis of sociodemographic features and stable psychological traits. A set of 18 psycho-social variables, generalized regressions, and predictive machine learning approaches were leveraged. We identified higher levels of perceived stress in the study sample relative to Italian normative values. Higher levels of distress were found in women, participants with lower income, and participants living with others. Higher rates of emotional stability and self-control, as well as a positive coping style and internal locus of control, emerged as protective factors. Predictive learning models identified participants with high perceived stress, with a sensitivity greater than 76%. The results suggest a characterization of people who are more vulnerable to experiencing high levels of stress during the COVID-19 pandemic. This characterization may contribute to early and targeted intervention strategies.

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study.

The aim of the present study was to explore whether kinematic indicators could improve the detection of subjects demonstrating faking-good behaviour when responding to personality questionnaires. One hundred and twenty volunteers were randomly assigned to one of four experimental groups (honest unspeeded, faking-good unspeeded, honest speeded, and faking-good speeded). Participants were asked to respond to the MMPI-2 underreporting scales (L, K, S) and the PPI-R Virtuous Responding (VR) scale using a computer mouse. The collected data included T-point scores on the L, K, S, and VR scales; response times on these scales; and several temporal and spatial mouse parameters. These data were used to investigate the presence of significant differences between the two manipulated variables (honest vs. faking-good; speeded vs. unspeeded). The results demonstrated that T-scores were significantly higher in the faking-good condition relative to the honest condition; however, faking-good and honest respondents showed no statistically significant differences between the speeded and unspeeded conditions. Concerning temporal and spatial kinematic parameters, we observed mixed results for different scales and further investigations are required. The most consistent finding, albeit with small observed effects, regards the L scale, in which faking-good respondents took longer to respond to stimuli and outlined wider mouse trajectories to arrive at the given response.

Indicators to distinguish symptom accentuators from symptom producers in individuals with a diagnosed adjustment disorder: A pilot study on inconsistency subtypes using SIMS and MMPI-2-RF.

In the context of legal damage evaluations, evaluees may exaggerate or simulate symptoms in an attempt to obtain greater economic compensation. To date, practitioners and researchers have focused on detecting malingering behavior as an exclusively unitary construct. However, we argue that there are two types of inconsistent behavior that speak to possible malingering-accentuating (i.e., exaggerating symptoms that are actually experienced) and simulating (i.e., fabricating symptoms entirely)-each with its own unique attributes; thus, it is necessary to distinguish between them. The aim of the present study was to identify objective indicators to differentiate symptom accentuators from symptom producers and consistent participants. We analyzed the Structured Inventory of Malingered Symptomatology scales and the Minnesota Multiphasic Personality Inventory-2 Restructured Form validity scales of 132 individuals with a diagnosed adjustment disorder with mixed anxiety and depressed mood who had undergone assessment for psychiatric/psychological damage. The results indicated that the SIMS Total Score, Neurologic Impairment and Low Intelligence scales and the MMPI-2-RF Infrequent Responses (F-r) and Response Bias (RBS) scales successfully discriminated among symptom accentuators, symptom producers, and consistent participants. Machine learning analysis was used to identify the most efficient parameter for classifying these three groups, recognizing the SIMS Total Score as the best indicator.

Malingering Detection of Cognitive Impairment With the b Test Is Boosted Using Machine Learning.

Objective: Here we report an investigation on the accuracy of the b Test, a measure to identify malingering of cognitive symptoms, in detecting malingerers of mild cognitive impairment. Method: Three groups of participants, patients with Mild Neurocognitive Disorder (n = 21), healthy elders (controls, n = 21), and healthy elders instructed to simulate mild cognitive disorder (malingerers, n = 21) were administered two background neuropsychological tests (MMSE, FAB) as well as the b Test. Results: Malingerers performed significantly worse on all error scores as compared to patients and controls, and performed poorly than controls, but comparably to patients, on the time score. Patients performed significantly worse than controls on all scores, but both groups showed the same pattern of more omission than commission errors. By contrast, malingerers exhibited the opposite pattern with more commission errors than omission errors. Machine learning models achieve an overall accuracy higher than 90% in distinguishing patients from malingerers on the basis of b Test results alone. Conclusions: Our findings suggest that b Test error scores accurately distinguish patients with Mild Neurocognitive Disorder from malingerers and may complement other validated procedures such as the Medical Symptom Validity Test.

Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times.

Background and Purpose. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis. Materials and Methods. The sample was composed of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: honest speeded, faking-good speeded, honest unspeeded, and faking-good unspeeded. A software version of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) was administered. Results. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers' performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T-scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55-65%), whereas temporal features resulted to be more useful (accuracies 70-75%). By contrast, temporal features and T-scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%). Discussion. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way than does no time pressure condition.